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2025-04-10 08:38:38
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2025-04-19 04:15:39
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Tami3/HazardNet
|
Tami3
|
visual-question-answering
|
[
"transformers",
"safetensors",
"trl",
"sft",
"visual-question-answering",
"en",
"dataset:Tami3/HazardQA",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"doi:10.57967/hf/3895",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-12-18T22:04:37Z |
2024-12-22T12:18:38+00:00
| 0 | 0 |
---
base_model:
- Qwen/Qwen2-VL-2B-Instruct
datasets:
- Tami3/HazardQA
language:
- en
library_name: transformers
license: apache-2.0
model_name: HazardNet-unsloth-v0.4
pipeline_tag: visual-question-answering
tags:
- trl
- sft
licence: license
---
# Model Card for HazardNet-unsloth-v0.4
This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO
# Initialize the Visual Question Answering pipeline with HazardNet
hazard_vqa = pipeline(
"visual-question-answering",
model="Tami3/HazardNet"
)
# Function to load image from a local path or URL
def load_image(image_path=None, image_url=None):
if image_path:
return Image.open(image_path).convert("RGB")
elif image_url:
response = requests.get(image_url)
response.raise_for_status() # Ensure the request was successful
return Image.open(BytesIO(response.content)).convert("RGB")
else:
raise ValueError("Provide either image_path or image_url.")
# Example 1: Loading image from a local file
try:
image_path = "path_to_your_ego_car_image.jpg" # Replace with your local image path
image = load_image(image_path=image_path)
except Exception as e:
print(f"Error loading image from path: {e}")
# Optionally, handle the error or exit
# Example 2: Loading image from a URL
# try:
# image_url = "https://example.com/path_to_image.jpg" # Replace with your image URL
# image = load_image(image_url=image_url)
# except Exception as e:
# print(f"Error loading image from URL: {e}")
# # Optionally, handle the error or exit
# Define your question about potential hazards
question = "Is there a pedestrian crossing the road ahead?"
# Get the answer from the HazardNet pipeline
try:
result = hazard_vqa(question=question, image=image)
answer = result.get('answer', 'No answer provided.')
score = result.get('score', 0.0)
print("Question:", question)
print("Answer:", answer)
print("Confidence Score:", score)
except Exception as e:
print(f"Error during inference: {e}")
# Optionally, handle the error or exit
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.13.0
- Transformers: 4.47.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
| null |
Non_BioNLP
|
# Model Card for HazardNet-unsloth-v0.4
This model is a fine-tuned version of [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO
# Initialize the Visual Question Answering pipeline with HazardNet
hazard_vqa = pipeline(
"visual-question-answering",
model="Tami3/HazardNet"
)
# Function to load image from a local path or URL
def load_image(image_path=None, image_url=None):
if image_path:
return Image.open(image_path).convert("RGB")
elif image_url:
response = requests.get(image_url)
response.raise_for_status() # Ensure the request was successful
return Image.open(BytesIO(response.content)).convert("RGB")
else:
raise ValueError("Provide either image_path or image_url.")
# Example 1: Loading image from a local file
try:
image_path = "path_to_your_ego_car_image.jpg" # Replace with your local image path
image = load_image(image_path=image_path)
except Exception as e:
print(f"Error loading image from path: {e}")
# Optionally, handle the error or exit
# Example 2: Loading image from a URL
# try:
# image_url = "https://example.com/path_to_image.jpg" # Replace with your image URL
# image = load_image(image_url=image_url)
# except Exception as e:
# print(f"Error loading image from URL: {e}")
# # Optionally, handle the error or exit
# Define your question about potential hazards
question = "Is there a pedestrian crossing the road ahead?"
# Get the answer from the HazardNet pipeline
try:
result = hazard_vqa(question=question, image=image)
answer = result.get('answer', 'No answer provided.')
score = result.get('score', 0.0)
print("Question:", question)
print("Answer:", answer)
print("Confidence Score:", score)
except Exception as e:
print(f"Error during inference: {e}")
# Optionally, handle the error or exit
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.13.0
- Transformers: 4.47.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
{"base_model": ["Qwen/Qwen2-VL-2B-Instruct"], "datasets": ["Tami3/HazardQA"], "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "model_name": "HazardNet-unsloth-v0.4", "pipeline_tag": "visual-question-answering", "tags": ["trl", "sft"], "licence": "license"}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,911 |
jorgeduardo13/platzi-distilbert-model-similaritytexts-JorgeEnciso
|
jorgeduardo13
|
text-classification
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-04T02:41:08Z |
2023-09-04T02:59:55+00:00
| 10 | 0 |
---
base_model: distilbert-base-uncased
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- text-classification
- generated_from_trainer
model-index:
- name: platzi-distilbert-model-similaritytexts-JorgeEnciso
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: datasetX
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- type: accuracy
value: 0.7083333333333334
name: Accuracy
- type: f1
value: 0.8125984251968503
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilbert-model-similaritytexts-JorgeEnciso
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5871
- Accuracy: 0.7083
- F1: 0.8126
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6336 | 1.09 | 500 | 0.6239 | 0.6838 | 0.8122 |
| 0.6337 | 2.18 | 1000 | 0.6223 | 0.6912 | 0.8158 |
| 0.6076 | 3.27 | 1500 | 0.5871 | 0.7083 | 0.8126 |
| 0.5585 | 4.36 | 2000 | 0.6390 | 0.6887 | 0.7776 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi-distilbert-model-similaritytexts-JorgeEnciso
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5871
- Accuracy: 0.7083
- F1: 0.8126
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6336 | 1.09 | 500 | 0.6239 | 0.6838 | 0.8122 |
| 0.6337 | 2.18 | 1000 | 0.6223 | 0.6912 | 0.8158 |
| 0.6076 | 3.27 | 1500 | 0.5871 | 0.7083 | 0.8126 |
| 0.5585 | 4.36 | 2000 | 0.6390 | 0.6887 | 0.7776 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
{"base_model": "distilbert-base-uncased", "datasets": ["glue"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["text-classification", "generated_from_trainer"], "model-index": [{"name": "platzi-distilbert-model-similaritytexts-JorgeEnciso", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "datasetX", "type": "glue", "config": "mrpc", "split": "validation", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.7083333333333334, "name": "Accuracy"}, {"type": "f1", "value": 0.8125984251968503, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,912 |
Helsinki-NLP/opus-mt-sv-ceb
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"sv",
"ceb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T12:04:52+00:00
| 33 | 0 |
---
license: apache-2.0
tags:
- translation
---
### opus-mt-sv-ceb
* source languages: sv
* target languages: ceb
* OPUS readme: [sv-ceb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ceb/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.ceb | 39.2 | 0.609 |
| null |
Non_BioNLP
|
### opus-mt-sv-ceb
* source languages: sv
* target languages: ceb
* OPUS readme: [sv-ceb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ceb/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.ceb | 39.2 | 0.609 |
|
{"license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,913 |
KoNqUeRoR3891/HW2-reward
|
KoNqUeRoR3891
|
text-classification
|
[
"transformers",
"safetensors",
"gpt2",
"text-classification",
"trl",
"reward-trainer",
"generated_from_trainer",
"dataset:piqa",
"base_model:KoNqUeRoR3891/HW2-supervised",
"base_model:finetune:KoNqUeRoR3891/HW2-supervised",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-03T16:10:59Z |
2024-09-03T17:00:23+00:00
| 6 | 0 |
---
base_model: KoNqUeRoR3891/HW2-supervised
datasets:
- piqa
library_name: transformers
license: mit
metrics:
- accuracy
tags:
- trl
- reward-trainer
- generated_from_trainer
model-index:
- name: HW2-reward
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: piqa
type: piqa
config: plain_text
split: train
args: plain_text
metrics:
- type: accuracy
value: 0.640818858560794
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW2-reward
This model is a fine-tuned version of [KoNqUeRoR3891/HW2-supervised](https://huggingface.co/KoNqUeRoR3891/HW2-supervised) on the piqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7024
- Accuracy: 0.6408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6793 | 1.0 | 3626 | 0.6733 | 0.5782 |
| 0.6767 | 2.0 | 7252 | 0.6590 | 0.6210 |
| 0.5686 | 3.0 | 10878 | 0.7024 | 0.6408 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW2-reward
This model is a fine-tuned version of [KoNqUeRoR3891/HW2-supervised](https://huggingface.co/KoNqUeRoR3891/HW2-supervised) on the piqa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7024
- Accuracy: 0.6408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6793 | 1.0 | 3626 | 0.6733 | 0.5782 |
| 0.6767 | 2.0 | 7252 | 0.6590 | 0.6210 |
| 0.5686 | 3.0 | 10878 | 0.7024 | 0.6408 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
{"base_model": "KoNqUeRoR3891/HW2-supervised", "datasets": ["piqa"], "library_name": "transformers", "license": "mit", "metrics": ["accuracy"], "tags": ["trl", "reward-trainer", "generated_from_trainer"], "model-index": [{"name": "HW2-reward", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "piqa", "type": "piqa", "config": "plain_text", "split": "train", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.640818858560794, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,914 |
armandnlp/distilbert-base-uncased-finetuned-emotion
|
armandnlp
|
text-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-06-18T16:22:52Z |
2022-09-22T12:43:07+00:00
| 22 | 0 |
---
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: accuracy
value: 0.9275
name: Accuracy
- type: f1
value: 0.9273822408882375
name: F1
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- type: accuracy
value: 0.919
name: Accuracy
verified: true
- type: precision
value: 0.8882001804445858
name: Precision Macro
verified: true
- type: precision
value: 0.919
name: Precision Micro
verified: true
- type: precision
value: 0.9194695149914663
name: Precision Weighted
verified: true
- type: recall
value: 0.857858142469294
name: Recall Macro
verified: true
- type: recall
value: 0.919
name: Recall Micro
verified: true
- type: recall
value: 0.919
name: Recall Weighted
verified: true
- type: f1
value: 0.8684381937860847
name: F1 Macro
verified: true
- type: f1
value: 0.919
name: F1 Micro
verified: true
- type: f1
value: 0.9182406234430719
name: F1 Weighted
verified: true
- type: loss
value: 0.21632428467273712
name: loss
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2237
- Accuracy: 0.9275
- F1: 0.9274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8643 | 1.0 | 250 | 0.3324 | 0.9065 | 0.9025 |
| 0.2589 | 2.0 | 500 | 0.2237 | 0.9275 | 0.9274 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2237
- Accuracy: 0.9275
- F1: 0.9274
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8643 | 1.0 | 250 | 0.3324 | 0.9065 | 0.9025 |
| 0.2589 | 2.0 | 500 | 0.2237 | 0.9275 | 0.9274 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9275, "name": "Accuracy"}, {"type": "f1", "value": 0.9273822408882375, "name": "F1"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.919, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.8882001804445858, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.919, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.9194695149914663, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.857858142469294, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.919, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.919, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.8684381937860847, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.919, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.9182406234430719, "name": "F1 Weighted", "verified": true}, {"type": "loss", "value": 0.21632428467273712, "name": "loss", "verified": true}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,916 |
y629/distilbert-base-uncased-finetuned-emotion
|
y629
|
text-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-01-26T14:37:37Z |
2024-01-26T14:54:25+00:00
| 5 | 0 |
---
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.919
name: Accuracy
- type: f1
value: 0.9192117062934999
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2241
- Accuracy: 0.919
- F1: 0.9192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8491 | 1.0 | 250 | 0.3189 | 0.905 | 0.9024 |
| 0.2564 | 2.0 | 500 | 0.2241 | 0.919 | 0.9192 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2241
- Accuracy: 0.919
- F1: 0.9192
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8491 | 1.0 | 250 | 0.3189 | 0.905 | 0.9024 |
| 0.2564 | 2.0 | 500 | 0.2241 | 0.919 | 0.9192 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.13.3
|
{"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.919, "name": "Accuracy"}, {"type": "f1", "value": 0.9192117062934999, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,918 |
folflo/Bert2Bert_HunSum_1205
|
folflo
|
summarization
|
[
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-12-06T21:24:16Z |
2023-12-08T09:49:58+00:00
| 29 | 0 |
---
tags:
- summarization
- generated_from_trainer
model-index:
- name: Bert2Bert_HunSum_1205
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bert2Bert_HunSum_1205
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bert2Bert_HunSum_1205
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
{"tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "Bert2Bert_HunSum_1205", "results": []}]}
|
task
|
[
"SUMMARIZATION"
] | 41,919 |
DmitriyVasiliev/ruT5-base-simplification
|
DmitriyVasiliev
|
text2text-generation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"Simplification",
"Summarization",
"paraphrase",
"ru",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-10T07:20:08Z |
2023-06-10T11:18:34+00:00
| 68 | 0 |
---
language:
- ru
tags:
- Simplification
- Summarization
- paraphrase
---
Данная модель является дообучнной версией "ai-forever/ruT5-base" (ранее"sberbank-ai/ruT5-base") на задаче упрощения текста (text simplification).
Набор данных (https://drive.google.com/file/d/14lCIp0TJ78R8E9miVm5Ac88guEjCkgFR) был собран из материалов конференции "Dialog-21" (https://github.com/dialogue-evaluation/RuSimpleSentEval), корпуса "RuAdapt" (https://github.com/Digital-Pushkin-Lab/RuAdapt), а также уникальных данных, предоставленными институтом ИФиЯК СФУ.
Параметры обучения: данные из файла "dia_train", Learning Rate = 3e-5, batch size = 1, optimizer = AdamW.
Оценка SARI (среднее на файле "dia_test") = 33,14
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "DmitriyVasiliev/ruT5-base-simplification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_text = "Хотя мечте Петра не суждено было сбыться, именно зарубежный опыт лёг в основу дальнейшей застройки."
ml = 2048
import re
def out_gen(input_line):
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
input_ids = tokenizer(
[WHITESPACE_HANDLER(input_line)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=ml
)["input_ids"].to(device)
output_ids = model.generate(
input_ids=input_ids,
max_length=ml,
no_repeat_ngram_size=2,
num_beams=5,
)[0].to(device)
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
return summary
answer = out_gen(input_text)
```
| null |
Non_BioNLP
|
Данная модель является дообучнной версией "ai-forever/ruT5-base" (ранее"sberbank-ai/ruT5-base") на задаче упрощения текста (text simplification).
Набор данных (https://drive.google.com/file/d/14lCIp0TJ78R8E9miVm5Ac88guEjCkgFR) был собран из материалов конференции "Dialog-21" (https://github.com/dialogue-evaluation/RuSimpleSentEval), корпуса "RuAdapt" (https://github.com/Digital-Pushkin-Lab/RuAdapt), а также уникальных данных, предоставленными институтом ИФиЯК СФУ.
Параметры обучения: данные из файла "dia_train", Learning Rate = 3e-5, batch size = 1, optimizer = AdamW.
Оценка SARI (среднее на файле "dia_test") = 33,14
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "DmitriyVasiliev/ruT5-base-simplification"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_text = "Хотя мечте Петра не суждено было сбыться, именно зарубежный опыт лёг в основу дальнейшей застройки."
ml = 2048
import re
def out_gen(input_line):
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
input_ids = tokenizer(
[WHITESPACE_HANDLER(input_line)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=ml
)["input_ids"].to(device)
output_ids = model.generate(
input_ids=input_ids,
max_length=ml,
no_repeat_ngram_size=2,
num_beams=5,
)[0].to(device)
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
return summary
answer = out_gen(input_text)
```
|
{"language": ["ru"], "tags": ["Simplification", "Summarization", "paraphrase"]}
|
task
|
[
"SUMMARIZATION"
] | 41,920 |
TheBloke/Airoboros-L2-70b-2.2.1-GPTQ
|
TheBloke
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-2.2.1",
"base_model:jondurbin/airoboros-l2-70b-2.2.1",
"base_model:quantized:jondurbin/airoboros-l2-70b-2.2.1",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | 2023-09-21T21:55:31Z |
2023-09-27T12:53:56+00:00
| 71 | 4 |
---
base_model: jondurbin/airoboros-l2-70b-2.2.1
datasets:
- jondurbin/airoboros-2.2.1
license: llama2
model_name: Airoboros L2 70B 2.2.1
inference: false
model_creator: Jon Durbin
model_type: llama
prompt_template: "A chat.\nUSER: {prompt}\nASSISTANT: \n"
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Airoboros L2 70B 2.2.1 - GPTQ
- Model creator: [Jon Durbin](https://huggingface.co/jondurbin)
- Original model: [Airoboros L2 70B 2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Jon Durbin's Airoboros L2 70B 2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GGUF)
* [Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Chat
```
A chat.
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 31.84 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Airoboros-L2-70b-2.2.1-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Airoboros-L2-70b-2.2.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Airoboros-L2-70b-2.2.1-GPTQ:main`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Airoboros-L2-70b-2.2.1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Airoboros-L2-70b-2.2.1-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''A chat.
USER: {prompt}
ASSISTANT:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jon Durbin's Airoboros L2 70B 2.2.1
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
This is essentially a minor "fix" branch of [airoboros-l2-70b-2.2](https://hf.co/jondurbin/airoboros-l2-70b-2.2) with a updates, primarily:
- [re-generated writing responses](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#re-generated-writing-responses)
- [longer contextual blocks](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#longer-contextual-blocks)
- [removal of "rp" data](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#rp-category-removed)
- [(less aggressive) de-censoring](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#de-censoring)
- 4 epochs instead of 3
__*Q4_0 quantization WILL NOT WORK on this version, it produces garbage!*__
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format:
```
A chat.
USER: {prompt}
ASSISTANT:
```
The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc.
Here's another example:
```
A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously.
USER: {prompt}
ASSISTANT:
```
And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf).
```
A chat between old friends: Timmy and Tommy.
{description of characters}
{setting for the chat}
Timmy: *takes a big sip from his coffee* "Ah, sweet, delicious, magical coffee."
Tommy:
```
__*I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.*__
### Fine tuning info
https://wandb.ai/jondurbin/airoboros-l2-70b-2.2.1/runs/f91zmwuz?workspace=user-jondurbin
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.2 models are built on top of llama-2/codellama.
The llama-2 base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
| null |
Non_BioNLP
|
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Airoboros L2 70B 2.2.1 - GPTQ
- Model creator: [Jon Durbin](https://huggingface.co/jondurbin)
- Original model: [Airoboros L2 70B 2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Jon Durbin's Airoboros L2 70B 2.2.1](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GGUF)
* [Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Chat
```
A chat.
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 31.84 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Airoboros-L2-70b-2.2.1-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/Airoboros-L2-70b-2.2.1-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Airoboros-L2-70b-2.2.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Airoboros-L2-70b-2.2.1-GPTQ:main`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Airoboros-L2-70b-2.2.1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Airoboros-L2-70b-2.2.1-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''A chat.
USER: {prompt}
ASSISTANT:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jon Durbin's Airoboros L2 70B 2.2.1
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
This is essentially a minor "fix" branch of [airoboros-l2-70b-2.2](https://hf.co/jondurbin/airoboros-l2-70b-2.2) with a updates, primarily:
- [re-generated writing responses](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#re-generated-writing-responses)
- [longer contextual blocks](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#longer-contextual-blocks)
- [removal of "rp" data](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#rp-category-removed)
- [(less aggressive) de-censoring](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#de-censoring)
- 4 epochs instead of 3
__*Q4_0 quantization WILL NOT WORK on this version, it produces garbage!*__
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format:
```
A chat.
USER: {prompt}
ASSISTANT:
```
The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc.
Here's another example:
```
A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously.
USER: {prompt}
ASSISTANT:
```
And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf).
```
A chat between old friends: Timmy and Tommy.
{description of characters}
{setting for the chat}
Timmy: *takes a big sip from his coffee* "Ah, sweet, delicious, magical coffee."
Tommy:
```
__*I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.*__
### Fine tuning info
https://wandb.ai/jondurbin/airoboros-l2-70b-2.2.1/runs/f91zmwuz?workspace=user-jondurbin
### Helpful usage tips
*The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability.*
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 2.2 models are built on top of llama-2/codellama.
The llama-2 base model has a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
|
{"base_model": "jondurbin/airoboros-l2-70b-2.2.1", "datasets": ["jondurbin/airoboros-2.2.1"], "license": "llama2", "model_name": "Airoboros L2 70B 2.2.1", "inference": false, "model_creator": "Jon Durbin", "model_type": "llama", "prompt_template": "A chat.\nUSER: {prompt}\nASSISTANT: \n", "quantized_by": "TheBloke"}
|
task
|
[
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | 41,921 |
keras/t5_base_multi
|
keras
|
text-generation
|
[
"keras-hub",
"text-classification",
"keras",
"text-generation",
"arxiv:1910.10683",
"license:apache-2.0",
"region:us"
] | 2024-10-29T00:45:08Z |
2025-03-24T22:44:53+00:00
| 10 | 0 |
---
library_name: keras-hub
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-classification
- keras
---
### Model Overview
⚠️ T5 is currently only available via the `keras-hub-nightly` package. Use `pip install keras-hub-nightly` to try this model.
T5 encoder-decoder backbone model.
T5 is a LLM pretrained on a mix of unsupervised and supervised tasks,
where each task is converted to a sequence-to-sequence format.
T5 works well on a variety of tasks out-of-the-box by prepending
various prefixes to the input sequence, e.g., for translation:
`"translate English to German: ..."`, for summarization:
`"summarize: ..."`.
T5 was introduced in
[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)
The default constructor gives a fully customizable, randomly initialized T5
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the `from_preset` constructor.
Disclaimer: Pre-trained models are provided on an "as is" basis, without
warranties or conditions of any kind.
## Links
* [T5 Quickstart Notebook](coming soon)
* [T5 API Documentation](https://keras.io/keras_hub/api/models/t5/)
* [T5 Model Card](https://github.com/google-research/text-to-text-transfer-transformer/tree/main)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
## Installation
Keras and KerasHub can be installed with:
```
pip install -U -q keras-hub
pip install -U -q keras
```
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
## Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|----------------|------------|--------------------------------------------------|
| t5_small_multi | 0 | 8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).|
| t5_base_multi| 0 | 12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| t5_large_multi | 0 | 24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_small_multi | 0 | 8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_base_multi | 0 | 12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_large_multi | 0 | 24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| t5_1.1_small | 60.51M | |
| tt5_1.1_base | 247.58M | |
| t5_1.1_large | 750.25M | |
| t5_1.1_xl | 2.85B | |
| t5_1.1_xxl | 11.14B | |
__Arguments__
- __vocabulary_size__: int. The size of the token vocabulary.
- __num_layers__: int. The number of Transformer layers.
- __num_heads__: int. The number of attention heads for each Transformer.
The hidden size must be divisible by the number of attention heads.
- __hidden_dim__: int. The hidden size of the Transformer layers.
- __intermediate_dim__: int. The output dimension of the first Dense layer in
a two-layer feedforward network for each Transformer layer.
- __key_value_dim__: int. The dimension of each head of the key/value
projections in the multi-head attention layers. Defaults to
hidden_dim / num_heads.
- __dropout__: float. Dropout probability for the Transformer layers.
- __activation__: activation function (or activation string name). The
activation to be used in the inner dense blocks of the
Transformer layers. Defaults to `"relu"`.
- __use_gated_activation__: boolean. Whether to use activation gating in
the inner dense blocks of the Transformer layers.
The original T5 architecture didn't use gating, but more
recent versions do. Defaults to `True`.
- __layer_norm_epsilon__: float. Epsilon factor to be used in the
layer normalization layers in the Transformer layers.
- __tie_embedding_weights__: boolean. If `True`, the weights of the token
embedding and the weights projecting language model outputs from
`hidden_dim`
| null |
Non_BioNLP
|
### Model Overview
⚠️ T5 is currently only available via the `keras-hub-nightly` package. Use `pip install keras-hub-nightly` to try this model.
T5 encoder-decoder backbone model.
T5 is a LLM pretrained on a mix of unsupervised and supervised tasks,
where each task is converted to a sequence-to-sequence format.
T5 works well on a variety of tasks out-of-the-box by prepending
various prefixes to the input sequence, e.g., for translation:
`"translate English to German: ..."`, for summarization:
`"summarize: ..."`.
T5 was introduced in
[Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683)
The default constructor gives a fully customizable, randomly initialized T5
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the `from_preset` constructor.
Disclaimer: Pre-trained models are provided on an "as is" basis, without
warranties or conditions of any kind.
## Links
* [T5 Quickstart Notebook](coming soon)
* [T5 API Documentation](https://keras.io/keras_hub/api/models/t5/)
* [T5 Model Card](https://github.com/google-research/text-to-text-transfer-transformer/tree/main)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
## Installation
Keras and KerasHub can be installed with:
```
pip install -U -q keras-hub
pip install -U -q keras
```
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
## Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|----------------|------------|--------------------------------------------------|
| t5_small_multi | 0 | 8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4).|
| t5_base_multi| 0 | 12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| t5_large_multi | 0 | 24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_small_multi | 0 | 8-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_base_multi | 0 | 12-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| flan_large_multi | 0 | 24-layer T5 model. Trained on the Colossal Clean Crawled Corpus (C4). |
| t5_1.1_small | 60.51M | |
| tt5_1.1_base | 247.58M | |
| t5_1.1_large | 750.25M | |
| t5_1.1_xl | 2.85B | |
| t5_1.1_xxl | 11.14B | |
__Arguments__
- __vocabulary_size__: int. The size of the token vocabulary.
- __num_layers__: int. The number of Transformer layers.
- __num_heads__: int. The number of attention heads for each Transformer.
The hidden size must be divisible by the number of attention heads.
- __hidden_dim__: int. The hidden size of the Transformer layers.
- __intermediate_dim__: int. The output dimension of the first Dense layer in
a two-layer feedforward network for each Transformer layer.
- __key_value_dim__: int. The dimension of each head of the key/value
projections in the multi-head attention layers. Defaults to
hidden_dim / num_heads.
- __dropout__: float. Dropout probability for the Transformer layers.
- __activation__: activation function (or activation string name). The
activation to be used in the inner dense blocks of the
Transformer layers. Defaults to `"relu"`.
- __use_gated_activation__: boolean. Whether to use activation gating in
the inner dense blocks of the Transformer layers.
The original T5 architecture didn't use gating, but more
recent versions do. Defaults to `True`.
- __layer_norm_epsilon__: float. Epsilon factor to be used in the
layer normalization layers in the Transformer layers.
- __tie_embedding_weights__: boolean. If `True`, the weights of the token
embedding and the weights projecting language model outputs from
`hidden_dim`
|
{"library_name": "keras-hub", "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["text-classification", "keras"]}
|
task
|
[
"TRANSLATION",
"SUMMARIZATION"
] | 41,922 |
mrp/bert-finetuned-squad
|
mrp
|
question-answering
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2022-08-31T09:52:58+00:00
| 35 | 0 |
---
datasets:
- squad
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: loss
value: 1.073493242263794
name: Loss
verified: true
- type: exact_match
value: 80.0853
name: Exact Match
verified: true
- type: f1
value: 87.606
name: F1
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"datasets": ["squad"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-finetuned-squad", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "loss", "value": 1.073493242263794, "name": "Loss", "verified": true}, {"type": "exact_match", "value": 80.0853, "name": "Exact Match", "verified": true}, {"type": "f1", "value": 87.606, "name": "F1", "verified": true}]}]}]}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,923 |
bullmount/quanIta_t5
|
bullmount
|
text2text-generation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text_generation",
"question_answering",
"it",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-04-25T16:37:10Z |
2023-04-27T17:23:55+00:00
| 83 | 1 |
---
language:
- it
license: apache-2.0
tags:
- text2text_generation
- question_answering
widget:
- text: Quante torri ha Bologna? La torre degli Asinelli è una delle cosiddette
due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana,
all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore
e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile
Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri
e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può
dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume
che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di
fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo
una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel
1119.
- text: Chi costruì la torre degli Asinelli? La torre degli Asinelli è una delle
cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta
Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San
Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il
1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest
per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora
non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli.
Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere
di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo
una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel
1119.
- text: Chi è l'autore della Gioconda? La torre degli Asinelli è una delle cosiddette
due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana,
all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore
e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile
Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri
e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può
dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume
che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di
fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo
una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel
1119.
- text: Chi fece accostare Seneca agli insegnamenti di Pitagora? Seneca seguì molto
intensamente gli insegnamenti dei maestri, che esercitarono su di lui un profondo
influsso sia con la parola sia con l'esempio di una vita vissuta in coerenza con
gli ideali professati. Da Attalo imparò i principi dello stoicismo e l'abitudine
alle pratiche ascetiche. Da Sozione, oltre ad apprendere i principi delle dottrine
di Pitagora, fu avviato per qualche tempo verso la pratica vegetariana; venne
distolto però dal padre che non amava la filosofia e dal fatto che l'imperatore
Tiberio proibisse di seguire consuetudini di vita non romane.
model-index:
- name: quanIta_t5
results: []
---
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB).<br/>
This is an mt5-based Question Answering model for the Italian language. <br/>
Training is done on translated subset of SQuAD 2.0 dataset (of about 100k questions).<br/>
Thus, this model not only attempts to answer questions through reading comprehension, but also refrains when presented with a question that cannot be answered based on the paragraph provided.
You can test the model by entering question + context like the string shown below:
```
In quale anno si è verificato il terremoto nel Sichuan?
Il terremoto del Sichuan del 2008 o il terremoto del Gran Sichuan, misurato a 8.0 Ms e 7.9 Mw, e si è verificato alle 02:28:01 PM China Standard Time all' epicentro (06:28:01 UTC) il 12 maggio nella provincia del Sichuan, ha ucciso 69.197 persone e lasciato 18.222 dispersi.
```
The train achieves the following results:
- EM: 78.69
- F1: 84.69
- rouge1: precision=0.862, recall=0.849, fmeasure=0.845
- rouge2: precision=0.309, recall=0.300, fmeasure=0.298
- rougeL: precision=0.862, recall=0.849, fmeasure=0.845
- rougeLsum: precision=0.862, recall=0.849, fmeasure=0.845
| null |
Non_BioNLP
|
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on [Thoroughly Cleaned Italian mC4 Corpus](https://huggingface.co/datasets/gsarti/clean_mc4_it) (~41B words, ~275GB).<br/>
This is an mt5-based Question Answering model for the Italian language. <br/>
Training is done on translated subset of SQuAD 2.0 dataset (of about 100k questions).<br/>
Thus, this model not only attempts to answer questions through reading comprehension, but also refrains when presented with a question that cannot be answered based on the paragraph provided.
You can test the model by entering question + context like the string shown below:
```
In quale anno si è verificato il terremoto nel Sichuan?
Il terremoto del Sichuan del 2008 o il terremoto del Gran Sichuan, misurato a 8.0 Ms e 7.9 Mw, e si è verificato alle 02:28:01 PM China Standard Time all' epicentro (06:28:01 UTC) il 12 maggio nella provincia del Sichuan, ha ucciso 69.197 persone e lasciato 18.222 dispersi.
```
The train achieves the following results:
- EM: 78.69
- F1: 84.69
- rouge1: precision=0.862, recall=0.849, fmeasure=0.845
- rouge2: precision=0.309, recall=0.300, fmeasure=0.298
- rougeL: precision=0.862, recall=0.849, fmeasure=0.845
- rougeLsum: precision=0.862, recall=0.849, fmeasure=0.845
|
{"language": ["it"], "license": "apache-2.0", "tags": ["text2text_generation", "question_answering"], "widget": [{"text": "Quante torri ha Bologna? La torre degli Asinelli è una delle cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel 1119."}, {"text": "Chi costruì la torre degli Asinelli? La torre degli Asinelli è una delle cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel 1119."}, {"text": "Chi è l'autore della Gioconda? La torre degli Asinelli è una delle cosiddette due torri di Bologna, simbolo della città, situate in piazza di porta Ravegnana, all'incrocio tra le antiche strade San Donato (ora via Zamboni), San Vitale, Maggiore e Castiglione. Eretta, secondo la tradizione, fra il 1109 e il 1119 dal nobile Gherardo Asinelli, la torre è alta 97,20 metri, pende verso ovest per 2,23 metri e presenta all'interno una scalinata composta da 498 gradini. Ancora non si può dire con certezza quando e da chi fu costruita la torre degli Asinelli. Si presume che la torre debba il proprio nome a Gherardo Asinelli, il nobile cavaliere di fazione ghibellina al quale se ne attribuisce la costruzione, iniziata secondo una consolidata tradizione l'11 ottobre 1109 e terminata dieci anni dopo, nel 1119."}, {"text": "Chi fece accostare Seneca agli insegnamenti di Pitagora? Seneca seguì molto intensamente gli insegnamenti dei maestri, che esercitarono su di lui un profondo influsso sia con la parola sia con l'esempio di una vita vissuta in coerenza con gli ideali professati. Da Attalo imparò i principi dello stoicismo e l'abitudine alle pratiche ascetiche. Da Sozione, oltre ad apprendere i principi delle dottrine di Pitagora, fu avviato per qualche tempo verso la pratica vegetariana; venne distolto però dal padre che non amava la filosofia e dal fatto che l'imperatore Tiberio proibisse di seguire consuetudini di vita non romane."}], "model-index": [{"name": "quanIta_t5", "results": []}]}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,924 |
hopkins/eng-ind-delfy
|
hopkins
|
translation
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-03T16:50:00Z |
2023-07-03T17:03:12+00:00
| 8 | 0 |
---
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: eng-ind-delfy
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-ind-delfy
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8283
- Bleu: 20.3506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-ind-delfy
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8283
- Bleu: 20.3506
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
{"metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "eng-ind-delfy", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 41,925 |
google/pix2struct-ai2d-base
|
google
|
visual-question-answering
|
[
"transformers",
"pytorch",
"safetensors",
"pix2struct",
"image-text-to-text",
"visual-question-answering",
"en",
"fr",
"ro",
"de",
"multilingual",
"arxiv:2210.03347",
"license:apache-2.0",
"region:us"
] | 2023-03-14T10:02:51Z |
2023-12-24T21:14:49+00:00
| 1,966 | 42 |
---
language:
- en
- fr
- ro
- de
- multilingual
license: apache-2.0
pipeline_tag: visual-question-answering
inference: false
---
# Model card for Pix2Struct - Finetuned on AI2D (scientific diagram VQA)
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Using the model](#using-the-model)
2. [Contribution](#contribution)
3. [Citation](#citation)
# TL;DR
Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The full list of available models can be found on the Table 1 of the paper:
The abstract of the model states that:
> Visually-situated language is ubiquitous—sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and
forms. Perhaps due to this diversity, previous work has typically relied on domainspecific recipes with limited sharing of the underlying data, model architectures,
and objectives. We present Pix2Struct, a pretrained image-to-text model for
purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse
masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large
source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy,
we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions
are rendered directly on top of the input image. For the first time, we show that a
single pretrained model can achieve state-of-the-art results in six out of nine tasks
across four domains: documents, illustrations, user interfaces, and natural images.
# Using the model
This model has been fine-tuned on VQA, you need to provide a question in a specific format, ideally in the format of a Choices question answering
## Running the model
### In full precision, on CPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt")
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
### In full precision, on GPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
### In half precision, on GPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
import torch
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base", torch_dtype=torch.bfloat16).to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda", torch.bfloat16)
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
## Converting from T5x to huggingface
You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_checkpoint_to_pytorch.py) script as follows:
```bash
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa
```
if you are converting a large model, run:
```bash
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa
```
Once saved, you can push your converted model with the following snippet:
```python
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE)
processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE)
model.push_to_hub("USERNAME/MODEL_NAME")
processor.push_to_hub("USERNAME/MODEL_NAME")
```
# Contribution
This model was originally contributed by Kenton Lee, Mandar Joshi et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).
# Citation
If you want to cite this work, please consider citing the original paper:
```
@misc{https://doi.org/10.48550/arxiv.2210.03347,
doi = {10.48550/ARXIV.2210.03347},
url = {https://arxiv.org/abs/2210.03347},
author = {Lee, Kenton and Joshi, Mandar and Turc, Iulia and Hu, Hexiang and Liu, Fangyu and Eisenschlos, Julian and Khandelwal, Urvashi and Shaw, Peter and Chang, Ming-Wei and Toutanova, Kristina},
keywords = {Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
| null |
Non_BioNLP
|
# Model card for Pix2Struct - Finetuned on AI2D (scientific diagram VQA)
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Using the model](#using-the-model)
2. [Contribution](#contribution)
3. [Citation](#citation)
# TL;DR
Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The full list of available models can be found on the Table 1 of the paper:
The abstract of the model states that:
> Visually-situated language is ubiquitous—sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and
forms. Perhaps due to this diversity, previous work has typically relied on domainspecific recipes with limited sharing of the underlying data, model architectures,
and objectives. We present Pix2Struct, a pretrained image-to-text model for
purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse
masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large
source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy,
we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions
are rendered directly on top of the input image. For the first time, we show that a
single pretrained model can achieve state-of-the-art results in six out of nine tasks
across four domains: documents, illustrations, user interfaces, and natural images.
# Using the model
This model has been fine-tuned on VQA, you need to provide a question in a specific format, ideally in the format of a Choices question answering
## Running the model
### In full precision, on CPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt")
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
### In full precision, on GPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
### In half precision, on GPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
import torch
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base", torch_dtype=torch.bfloat16).to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda", torch.bfloat16)
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
## Converting from T5x to huggingface
You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_checkpoint_to_pytorch.py) script as follows:
```bash
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa
```
if you are converting a large model, run:
```bash
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa
```
Once saved, you can push your converted model with the following snippet:
```python
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE)
processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE)
model.push_to_hub("USERNAME/MODEL_NAME")
processor.push_to_hub("USERNAME/MODEL_NAME")
```
# Contribution
This model was originally contributed by Kenton Lee, Mandar Joshi et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).
# Citation
If you want to cite this work, please consider citing the original paper:
```
@misc{https://doi.org/10.48550/arxiv.2210.03347,
doi = {10.48550/ARXIV.2210.03347},
url = {https://arxiv.org/abs/2210.03347},
author = {Lee, Kenton and Joshi, Mandar and Turc, Iulia and Hu, Hexiang and Liu, Fangyu and Eisenschlos, Julian and Khandelwal, Urvashi and Shaw, Peter and Chang, Ming-Wei and Toutanova, Kristina},
keywords = {Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
{"language": ["en", "fr", "ro", "de", "multilingual"], "license": "apache-2.0", "pipeline_tag": "visual-question-answering", "inference": false}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,926 |
Vs2882/liar_binaryclassifier_distilbert_cased
|
Vs2882
|
text-classification
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:liar",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-09-22T14:38:03Z |
2024-10-10T05:57:07+00:00
| 15 | 0 |
---
base_model: bert-base-cased
datasets:
- liar
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: liar_binaryclassifier_distilbert_cased
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: liar
type: liar
config: default
split: test
args: default
metrics:
- type: accuracy
value: 0.6464208242950108
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# liar_binaryclassifier_distilbert_cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the liar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6488
- Model Preparation Time: 0.0034
- Accuracy: 0.6464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|
| 0.6836 | 1.0 | 461 | 0.6520 | 0.0034 | 0.6226 |
| 0.6423 | 2.0 | 922 | 0.6326 | 0.0034 | 0.6399 |
| 0.6091 | 3.0 | 1383 | 0.6362 | 0.0034 | 0.6443 |
| 0.5843 | 4.0 | 1844 | 0.6422 | 0.0034 | 0.6551 |
| 0.5624 | 5.0 | 2305 | 0.6488 | 0.0034 | 0.6464 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# liar_binaryclassifier_distilbert_cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the liar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6488
- Model Preparation Time: 0.0034
- Accuracy: 0.6464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|
| 0.6836 | 1.0 | 461 | 0.6520 | 0.0034 | 0.6226 |
| 0.6423 | 2.0 | 922 | 0.6326 | 0.0034 | 0.6399 |
| 0.6091 | 3.0 | 1383 | 0.6362 | 0.0034 | 0.6443 |
| 0.5843 | 4.0 | 1844 | 0.6422 | 0.0034 | 0.6551 |
| 0.5624 | 5.0 | 2305 | 0.6488 | 0.0034 | 0.6464 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
{"base_model": "bert-base-cased", "datasets": ["liar"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "liar_binaryclassifier_distilbert_cased", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "liar", "type": "liar", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6464208242950108, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,927 |
sobamchan/roberta-base-mean-50
|
sobamchan
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:557850",
"loss:MultipleNegativesRankingLoss",
"en",
"dataset:sentence-transformers/all-nli",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2025-02-16T16:52:36Z |
2025-02-16T16:53:37+00:00
| 36 | 0 |
---
base_model: FacebookAI/roberta-base
datasets:
- sentence-transformers/all-nli
language:
- en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: A man is jumping unto his filthy bed.
sentences:
- A young male is looking at a newspaper while 2 females walks past him.
- The bed is dirty.
- The man is on the moon.
- source_sentence: A carefully balanced male stands on one foot near a clean ocean
beach area.
sentences:
- A man is ouside near the beach.
- Three policemen patrol the streets on bikes
- A man is sitting on his couch.
- source_sentence: The man is wearing a blue shirt.
sentences:
- Near the trashcan the man stood and smoked
- A man in a blue shirt leans on a wall beside a road with a blue van and red car
with water in the background.
- A man in a black shirt is playing a guitar.
- source_sentence: The girls are outdoors.
sentences:
- Two girls riding on an amusement part ride.
- a guy laughs while doing laundry
- Three girls are standing together in a room, one is listening, one is writing
on a wall and the third is talking to them.
- source_sentence: A construction worker peeking out of a manhole while his coworker
sits on the sidewalk smiling.
sentences:
- A worker is looking out of a manhole.
- A man is giving a presentation.
- The workers are both inside the manhole.
---
# SentenceTransformer based on FacebookAI/roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
'A worker is looking out of a manhole.',
'The workers are both inside the manhole.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 1e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Validation Loss |
|:------:|:----:|:---------------:|
| 0.0011 | 5 | 5.1316 |
| 0.0023 | 10 | 5.1293 |
| 0.0034 | 15 | 5.1253 |
| 0.0046 | 20 | 5.1196 |
| 0.0057 | 25 | 5.1120 |
| 0.0069 | 30 | 5.1025 |
| 0.0080 | 35 | 5.0908 |
| 0.0092 | 40 | 5.0768 |
| 0.0103 | 45 | 5.0603 |
| 0.0115 | 50 | 5.0409 |
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.2.0+cu121
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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| null |
Non_BioNLP
|
# SentenceTransformer based on FacebookAI/roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
'A worker is looking out of a manhole.',
'The workers are both inside the manhole.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 1e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Validation Loss |
|:------:|:----:|:---------------:|
| 0.0011 | 5 | 5.1316 |
| 0.0023 | 10 | 5.1293 |
| 0.0034 | 15 | 5.1253 |
| 0.0046 | 20 | 5.1196 |
| 0.0057 | 25 | 5.1120 |
| 0.0069 | 30 | 5.1025 |
| 0.0080 | 35 | 5.0908 |
| 0.0092 | 40 | 5.0768 |
| 0.0103 | 45 | 5.0603 |
| 0.0115 | 50 | 5.0409 |
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.2.0+cu121
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
{"base_model": "FacebookAI/roberta-base", "datasets": ["sentence-transformers/all-nli"], "language": ["en"], "library_name": "sentence-transformers", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:557850", "loss:MultipleNegativesRankingLoss"], "widget": [{"source_sentence": "A man is jumping unto his filthy bed.", "sentences": ["A young male is looking at a newspaper while 2 females walks past him.", "The bed is dirty.", "The man is on the moon."]}, {"source_sentence": "A carefully balanced male stands on one foot near a clean ocean beach area.", "sentences": ["A man is ouside near the beach.", "Three policemen patrol the streets on bikes", "A man is sitting on his couch."]}, {"source_sentence": "The man is wearing a blue shirt.", "sentences": ["Near the trashcan the man stood and smoked", "A man in a blue shirt leans on a wall beside a road with a blue van and red car with water in the background.", "A man in a black shirt is playing a guitar."]}, {"source_sentence": "The girls are outdoors.", "sentences": ["Two girls riding on an amusement part ride.", "a guy laughs while doing laundry", "Three girls are standing together in a room, one is listening, one is writing on a wall and the third is talking to them."]}, {"source_sentence": "A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.", "sentences": ["A worker is looking out of a manhole.", "A man is giving a presentation.", "The workers are both inside the manhole."]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,928 |
yspkm/distilbert-base-uncased-finetuned-emotion
|
yspkm
|
text-classification
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-26T06:33:37Z |
2024-06-26T06:38:04+00:00
| 19 | 0 |
---
base_model: distilbert-base-uncased
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.9375
name: Accuracy
- type: f1
value: 0.937780289917648
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1443
- Accuracy: 0.9375
- F1: 0.9378
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 63 | 0.8657 | 0.714 | 0.6520 |
| No log | 2.0 | 126 | 0.3186 | 0.9085 | 0.9076 |
| No log | 3.0 | 189 | 0.2032 | 0.928 | 0.9281 |
| 0.5856 | 4.0 | 252 | 0.1733 | 0.93 | 0.9301 |
| 0.5856 | 5.0 | 315 | 0.1578 | 0.937 | 0.9368 |
| 0.5856 | 6.0 | 378 | 0.1543 | 0.9335 | 0.9341 |
| 0.5856 | 7.0 | 441 | 0.1506 | 0.9345 | 0.9343 |
| 0.1139 | 8.0 | 504 | 0.1475 | 0.939 | 0.9396 |
| 0.1139 | 9.0 | 567 | 0.1444 | 0.9375 | 0.9374 |
| 0.1139 | 10.0 | 630 | 0.1443 | 0.9375 | 0.9378 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1443
- Accuracy: 0.9375
- F1: 0.9378
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 63 | 0.8657 | 0.714 | 0.6520 |
| No log | 2.0 | 126 | 0.3186 | 0.9085 | 0.9076 |
| No log | 3.0 | 189 | 0.2032 | 0.928 | 0.9281 |
| 0.5856 | 4.0 | 252 | 0.1733 | 0.93 | 0.9301 |
| 0.5856 | 5.0 | 315 | 0.1578 | 0.937 | 0.9368 |
| 0.5856 | 6.0 | 378 | 0.1543 | 0.9335 | 0.9341 |
| 0.5856 | 7.0 | 441 | 0.1506 | 0.9345 | 0.9343 |
| 0.1139 | 8.0 | 504 | 0.1475 | 0.939 | 0.9396 |
| 0.1139 | 9.0 | 567 | 0.1444 | 0.9375 | 0.9374 |
| 0.1139 | 10.0 | 630 | 0.1443 | 0.9375 | 0.9378 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
{"base_model": "distilbert-base-uncased", "datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9375, "name": "Accuracy"}, {"type": "f1", "value": 0.937780289917648, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,929 |
Methamera/bloomz-mt-fork
|
Methamera
|
text-generation
|
[
"pytorch",
"tensorboard",
"safetensors",
"bloom",
"text-generation",
"conversational",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zu",
"dataset:bigscience/xP3mt",
"arxiv:2211.01786",
"license:bigscience-bloom-rail-1.0",
"model-index",
"region:us"
] | 2024-12-14T06:46:02Z |
2024-12-14T06:50:08+00:00
| 8 | 0 |
---
datasets:
- bigscience/xP3mt
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
license: bigscience-bloom-rail-1.0
pipeline_tag: text-generation
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
widget:
- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous
review as positive, neutral or negative?
example_title: zh-en sentiment
- text: 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？
example_title: zh-zh sentiment
- text: Suggest at least five related search terms to "Mạng neural nhân tạo".
example_title: vi-en query
- text: Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels».
example_title: fr-fr query
- text: Explain in a sentence in Telugu what is backpropagation in neural networks.
example_title: te-en qa
- text: Why is the sky blue?
example_title: en-en qa
- text: 'Write a fairy tale about a troll saving a princess from a dangerous dragon.
The fairy tale is a masterpiece that has achieved praise worldwide and its moral
is "Heroes Come in All Shapes and Sizes". Story (in Spanish):'
example_title: es-en fable
- text: 'Write a fable about wood elves living in a forest that is suddenly invaded
by ogres. The fable is a masterpiece that has achieved praise worldwide and its
moral is "Violence is the last refuge of the incompetent". Fable (in Hindi):'
example_title: hi-en fable
model-index:
- name: bloomz-mt
results:
- task:
type: Coreference resolution
dataset:
name: Winogrande XL (xl)
type: winogrande
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 59.98
- task:
type: Coreference resolution
dataset:
name: XWinograd (en)
type: Muennighoff/xwinograd
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 69.33
- task:
type: Coreference resolution
dataset:
name: XWinograd (fr)
type: Muennighoff/xwinograd
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 68.67
- task:
type: Coreference resolution
dataset:
name: XWinograd (jp)
type: Muennighoff/xwinograd
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 58.39
- task:
type: Coreference resolution
dataset:
name: XWinograd (pt)
type: Muennighoff/xwinograd
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 64.64
- task:
type: Coreference resolution
dataset:
name: XWinograd (ru)
type: Muennighoff/xwinograd
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 60.32
- task:
type: Coreference resolution
dataset:
name: XWinograd (zh)
type: Muennighoff/xwinograd
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 76.39
- task:
type: Natural language inference
dataset:
name: ANLI (r1)
type: anli
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 49.7
- task:
type: Natural language inference
dataset:
name: ANLI (r2)
type: anli
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 45.0
- task:
type: Natural language inference
dataset:
name: ANLI (r3)
type: anli
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 45.58
- task:
type: Natural language inference
dataset:
name: SuperGLUE (cb)
type: super_glue
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 87.5
- task:
type: Natural language inference
dataset:
name: SuperGLUE (rte)
type: super_glue
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 85.92
- task:
type: Natural language inference
dataset:
name: XNLI (ar)
type: xnli
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 58.03
- task:
type: Natural language inference
dataset:
name: XNLI (bg)
type: xnli
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 46.75
- task:
type: Natural language inference
dataset:
name: XNLI (de)
type: xnli
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.69
- task:
type: Natural language inference
dataset:
name: XNLI (el)
type: xnli
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 46.55
- task:
type: Natural language inference
dataset:
name: XNLI (en)
type: xnli
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 61.81
- task:
type: Natural language inference
dataset:
name: XNLI (es)
type: xnli
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 59.12
- task:
type: Natural language inference
dataset:
name: XNLI (fr)
type: xnli
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 59.12
- task:
type: Natural language inference
dataset:
name: XNLI (hi)
type: xnli
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 52.53
- task:
type: Natural language inference
dataset:
name: XNLI (ru)
type: xnli
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 52.85
- task:
type: Natural language inference
dataset:
name: XNLI (sw)
type: xnli
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 50.36
- task:
type: Natural language inference
dataset:
name: XNLI (th)
type: xnli
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 43.98
- task:
type: Natural language inference
dataset:
name: XNLI (tr)
type: xnli
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 43.78
- task:
type: Natural language inference
dataset:
name: XNLI (ur)
type: xnli
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 51.24
- task:
type: Natural language inference
dataset:
name: XNLI (vi)
type: xnli
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.82
- task:
type: Natural language inference
dataset:
name: XNLI (zh)
type: xnli
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.5
- task:
type: Program synthesis
dataset:
name: HumanEval
type: openai_humaneval
config: None
split: test
revision: e8dc562f5de170c54b5481011dd9f4fa04845771
metrics:
- type: Pass@1
value: 13.55
- type: Pass@10
value: 26.26
- type: Pass@100
value: 47.01
- task:
type: Sentence completion
dataset:
name: StoryCloze (2016)
type: story_cloze
config: '2016'
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 96.69
- task:
type: Sentence completion
dataset:
name: SuperGLUE (copa)
type: super_glue
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 91.0
- task:
type: Sentence completion
dataset:
name: XCOPA (et)
type: xcopa
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ht)
type: xcopa
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 55.0
- task:
type: Sentence completion
dataset:
name: XCOPA (id)
type: xcopa
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 87.0
- task:
type: Sentence completion
dataset:
name: XCOPA (it)
type: xcopa
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 74.0
- task:
type: Sentence completion
dataset:
name: XCOPA (qu)
type: xcopa
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 56.0
- task:
type: Sentence completion
dataset:
name: XCOPA (sw)
type: xcopa
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 67.0
- task:
type: Sentence completion
dataset:
name: XCOPA (ta)
type: xcopa
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 70.0
- task:
type: Sentence completion
dataset:
name: XCOPA (th)
type: xcopa
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53.0
- task:
type: Sentence completion
dataset:
name: XCOPA (tr)
type: xcopa
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
name: XCOPA (vi)
type: xcopa
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 91.0
- task:
type: Sentence completion
dataset:
name: XCOPA (zh)
type: xcopa
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 86.0
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ar)
type: Muennighoff/xstory_cloze
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 94.04
- task:
type: Sentence completion
dataset:
name: XStoryCloze (es)
type: Muennighoff/xstory_cloze
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 95.5
- task:
type: Sentence completion
dataset:
name: XStoryCloze (eu)
type: Muennighoff/xstory_cloze
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 86.83
- task:
type: Sentence completion
dataset:
name: XStoryCloze (hi)
type: Muennighoff/xstory_cloze
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 89.15
- task:
type: Sentence completion
dataset:
name: XStoryCloze (id)
type: Muennighoff/xstory_cloze
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 92.79
- task:
type: Sentence completion
dataset:
name: XStoryCloze (my)
type: Muennighoff/xstory_cloze
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 52.35
- task:
type: Sentence completion
dataset:
name: XStoryCloze (ru)
type: Muennighoff/xstory_cloze
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 79.09
- task:
type: Sentence completion
dataset:
name: XStoryCloze (sw)
type: Muennighoff/xstory_cloze
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 81.14
- task:
type: Sentence completion
dataset:
name: XStoryCloze (te)
type: Muennighoff/xstory_cloze
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 82.4
- task:
type: Sentence completion
dataset:
name: XStoryCloze (zh)
type: Muennighoff/xstory_cloze
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 92.85
---
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
- **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**
<div class="max-w-full overflow-auto">
<table>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
</tr>
<tr>
<td>Parameters</td>
<td>300M</td>
<td>580M</td>
<td>1.2B</td>
<td>3.7B</td>
<td>13B</td>
<td>560M</td>
<td>1.1B</td>
<td>1.7B</td>
<td>3B</td>
<td>7.1B</td>
<td>176B</td>
</tr>
<tr>
<td>Finetuned Model</td>
<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
</tr>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
</tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
</tr>
<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
<tr>
<td>Pretrained Model</td>
<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
</tr>
</table>
</div>
# Use
## Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:
- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
- Explain in a sentence in Telugu what is backpropagation in neural networks.
**Feel free to share your generations in the Community tab!**
## How to use
### CPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-mt"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-mt"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU in 8bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-mt"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
<!-- Necessary for whitespace -->
###
# Limitations
**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".
# Training
## Model
- **Architecture:** Same as [bloom](https://huggingface.co/bigscience/bloom), also refer to the `config.json` file
- **Finetuning steps:** 498
- **Finetuning tokens:** 2.09 billion
- **Finetuning layout:** 72x pipeline parallel, 1x tensor parallel, 4x data parallel
- **Precision:** bfloat16
## Hardware
- **CPUs:** AMD CPUs with 512GB memory per node
- **GPUs:** 288 A100 80GB GPUs with 8 GPUs per node (36 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links
- **Communication:** NCCL-communications network with a fully dedicated subnet
## Software
- **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5)
- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# Evaluation
We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
# Citation
```bibtex
@article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
journal={arXiv preprint arXiv:2211.01786},
year={2022}
}
```
| null |
Non_BioNLP
|
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
- **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**
<div class="max-w-full overflow-auto">
<table>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
</tr>
<tr>
<td>Parameters</td>
<td>300M</td>
<td>580M</td>
<td>1.2B</td>
<td>3.7B</td>
<td>13B</td>
<td>560M</td>
<td>1.1B</td>
<td>1.7B</td>
<td>3B</td>
<td>7.1B</td>
<td>176B</td>
</tr>
<tr>
<td>Finetuned Model</td>
<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
</tr>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
</tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
</tr>
<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
<tr>
<td>Pretrained Model</td>
<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
</tr>
</table>
</div>
# Use
## Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:
- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
- Explain in a sentence in Telugu what is backpropagation in neural networks.
**Feel free to share your generations in the Community tab!**
## How to use
### CPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-mt"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-mt"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU in 8bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-mt"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
<!-- Necessary for whitespace -->
###
# Limitations
**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".
# Training
## Model
- **Architecture:** Same as [bloom](https://huggingface.co/bigscience/bloom), also refer to the `config.json` file
- **Finetuning steps:** 498
- **Finetuning tokens:** 2.09 billion
- **Finetuning layout:** 72x pipeline parallel, 1x tensor parallel, 4x data parallel
- **Precision:** bfloat16
## Hardware
- **CPUs:** AMD CPUs with 512GB memory per node
- **GPUs:** 288 A100 80GB GPUs with 8 GPUs per node (36 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links
- **Communication:** NCCL-communications network with a fully dedicated subnet
## Software
- **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5)
- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# Evaluation
We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
# Citation
```bibtex
@article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
journal={arXiv preprint arXiv:2211.01786},
year={2022}
}
```
|
{"datasets": ["bigscience/xP3mt"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zu"], "license": "bigscience-bloom-rail-1.0", "pipeline_tag": "text-generation", "programming_language": ["C", "C++", "C#", "Go", "Java", "JavaScript", "Lua", "PHP", "Python", "Ruby", "Rust", "Scala", "TypeScript"], "widget": [{"text": "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?", "example_title": "zh-en sentiment"}, {"text": "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？", "example_title": "zh-zh sentiment"}, {"text": "Suggest at least five related search terms to \"Mạng neural nhân tạo\".", "example_title": "vi-en query"}, {"text": "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels».", "example_title": "fr-fr query"}, {"text": "Explain in a sentence in Telugu what is backpropagation in neural networks.", "example_title": "te-en qa"}, {"text": "Why is the sky blue?", "example_title": "en-en qa"}, {"text": "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):", "example_title": "es-en fable"}, {"text": "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):", "example_title": "hi-en fable"}], "model-index": [{"name": "bloomz-mt", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"name": "Winogrande XL (xl)", "type": "winogrande", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 59.98}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (en)", "type": "Muennighoff/xwinograd", "config": "en", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 69.33}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (fr)", "type": "Muennighoff/xwinograd", "config": "fr", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 68.67}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (jp)", "type": "Muennighoff/xwinograd", "config": "jp", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 58.39}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (pt)", "type": "Muennighoff/xwinograd", "config": "pt", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 64.64}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (ru)", "type": "Muennighoff/xwinograd", "config": "ru", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 60.32}]}, {"task": {"type": "Coreference resolution"}, "dataset": {"name": "XWinograd (zh)", "type": "Muennighoff/xwinograd", "config": "zh", "split": "test", "revision": "9dd5ea5505fad86b7bedad667955577815300cee"}, "metrics": [{"type": "Accuracy", "value": 76.39}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "ANLI (r1)", "type": "anli", "config": "r1", "split": "validation", "revision": "9dbd830a06fea8b1c49d6e5ef2004a08d9f45094"}, "metrics": [{"type": "Accuracy", "value": 49.7}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "ANLI (r2)", "type": "anli", "config": "r2", "split": "validation", "revision": "9dbd830a06fea8b1c49d6e5ef2004a08d9f45094"}, "metrics": [{"type": "Accuracy", "value": 45.0}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "ANLI (r3)", "type": "anli", "config": "r3", "split": "validation", "revision": "9dbd830a06fea8b1c49d6e5ef2004a08d9f45094"}, "metrics": [{"type": "Accuracy", "value": 45.58}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "SuperGLUE (cb)", "type": "super_glue", "config": "cb", "split": "validation", "revision": "9e12063561e7e6c79099feb6d5a493142584e9e2"}, "metrics": [{"type": "Accuracy", "value": 87.5}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "SuperGLUE (rte)", "type": "super_glue", "config": "rte", "split": "validation", "revision": "9e12063561e7e6c79099feb6d5a493142584e9e2"}, "metrics": [{"type": "Accuracy", "value": 85.92}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (ar)", "type": "xnli", "config": "ar", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 58.03}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (bg)", "type": "xnli", "config": "bg", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 46.75}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (de)", "type": "xnli", "config": "de", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 53.69}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (el)", "type": "xnli", "config": "el", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 46.55}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (en)", "type": "xnli", "config": "en", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 61.81}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (es)", "type": "xnli", "config": "es", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 59.12}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (fr)", "type": "xnli", "config": "fr", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 59.12}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (hi)", "type": "xnli", "config": "hi", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 52.53}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (ru)", "type": "xnli", "config": "ru", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 52.85}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (sw)", "type": "xnli", "config": "sw", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 50.36}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (th)", "type": "xnli", "config": "th", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 43.98}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (tr)", "type": "xnli", "config": "tr", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 43.78}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (ur)", "type": "xnli", "config": "ur", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 51.24}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (vi)", "type": "xnli", "config": "vi", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 55.82}]}, {"task": {"type": "Natural language inference"}, "dataset": {"name": "XNLI (zh)", "type": "xnli", "config": "zh", "split": "validation", "revision": "a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16"}, "metrics": [{"type": "Accuracy", "value": 55.5}]}, {"task": {"type": "Program synthesis"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval", "config": "None", "split": "test", "revision": "e8dc562f5de170c54b5481011dd9f4fa04845771"}, "metrics": [{"type": "Pass@1", "value": 13.55}, {"type": "Pass@10", "value": 26.26}, {"type": "Pass@100", "value": 47.01}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "StoryCloze (2016)", "type": "story_cloze", "config": "2016", "split": "validation", "revision": "e724c6f8cdf7c7a2fb229d862226e15b023ee4db"}, "metrics": [{"type": "Accuracy", "value": 96.69}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "SuperGLUE (copa)", "type": "super_glue", "config": "copa", "split": "validation", "revision": "9e12063561e7e6c79099feb6d5a493142584e9e2"}, "metrics": [{"type": "Accuracy", "value": 91.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (et)", "type": "xcopa", "config": "et", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 54.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (ht)", "type": "xcopa", "config": "ht", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 55.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (id)", "type": "xcopa", "config": "id", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 87.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (it)", "type": "xcopa", "config": "it", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 74.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (qu)", "type": "xcopa", "config": "qu", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 56.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (sw)", "type": "xcopa", "config": "sw", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 67.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (ta)", "type": "xcopa", "config": "ta", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 70.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (th)", "type": "xcopa", "config": "th", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 53.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (tr)", "type": "xcopa", "config": "tr", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 54.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (vi)", "type": "xcopa", "config": "vi", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 91.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XCOPA (zh)", "type": "xcopa", "config": "zh", "split": "validation", "revision": "37f73c60fb123111fa5af5f9b705d0b3747fd187"}, "metrics": [{"type": "Accuracy", "value": 86.0}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (ar)", "type": "Muennighoff/xstory_cloze", "config": "ar", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 94.04}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (es)", "type": "Muennighoff/xstory_cloze", "config": "es", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 95.5}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (eu)", "type": "Muennighoff/xstory_cloze", "config": "eu", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 86.83}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (hi)", "type": "Muennighoff/xstory_cloze", "config": "hi", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 89.15}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (id)", "type": "Muennighoff/xstory_cloze", "config": "id", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 92.79}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (my)", "type": "Muennighoff/xstory_cloze", "config": "my", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 52.35}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (ru)", "type": "Muennighoff/xstory_cloze", "config": "ru", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 79.09}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (sw)", "type": "Muennighoff/xstory_cloze", "config": "sw", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 81.14}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (te)", "type": "Muennighoff/xstory_cloze", "config": "te", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 82.4}]}, {"task": {"type": "Sentence completion"}, "dataset": {"name": "XStoryCloze (zh)", "type": "Muennighoff/xstory_cloze", "config": "zh", "split": "validation", "revision": "8bb76e594b68147f1a430e86829d07189622b90d"}, "metrics": [{"type": "Accuracy", "value": 92.85}]}]}]}
|
task
|
[
"COREFERENCE_RESOLUTION",
"TRANSLATION"
] | 41,930 |
bhadresh-savani/distilbert-base-uncased-finetuned-emotion
|
bhadresh-savani
|
text-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-07-11T05:51:35Z |
2022-07-14T06:59:49+00:00
| 185 | 1 |
---
datasets:
- emotion
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bertweet-base-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: accuracy
value: 0.9365
name: Accuracy
- type: f1
value: 0.9371
name: F1
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: default
split: test
metrics:
- type: accuracy
value: 0.923
name: Accuracy
verified: true
- type: precision
value: 0.8676576686813523
name: Precision Macro
verified: true
- type: precision
value: 0.923
name: Precision Micro
verified: true
- type: precision
value: 0.9268406401714973
name: Precision Weighted
verified: true
- type: recall
value: 0.8945488803260702
name: Recall Macro
verified: true
- type: recall
value: 0.923
name: Recall Micro
verified: true
- type: recall
value: 0.923
name: Recall Weighted
verified: true
- type: f1
value: 0.8798961895301041
name: F1 Macro
verified: true
- type: f1
value: 0.923
name: F1 Micro
verified: true
- type: f1
value: 0.9241278880972197
name: F1 Weighted
verified: true
- type: loss
value: 0.24626904726028442
name: loss
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1995
- Accuracy: 0.9365
- F1: 0.9371
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.475 | 1.0 | 503 | 0.2171 | 0.928 | 0.9292 |
| 0.1235 | 2.0 | 1006 | 0.1764 | 0.9365 | 0.9372 |
| 0.0802 | 3.0 | 1509 | 0.1788 | 0.938 | 0.9388 |
| 0.0531 | 4.0 | 2012 | 0.2005 | 0.938 | 0.9388 |
| 0.0367 | 5.0 | 2515 | 0.1995 | 0.9365 | 0.9371 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1995
- Accuracy: 0.9365
- F1: 0.9371
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.475 | 1.0 | 503 | 0.2171 | 0.928 | 0.9292 |
| 0.1235 | 2.0 | 1006 | 0.1764 | 0.9365 | 0.9372 |
| 0.0802 | 3.0 | 1509 | 0.1788 | 0.938 | 0.9388 |
| 0.0531 | 4.0 | 2012 | 0.2005 | 0.938 | 0.9388 |
| 0.0367 | 5.0 | 2515 | 0.1995 | 0.9365 | 0.9371 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"datasets": ["emotion"], "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bertweet-base-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9365, "name": "Accuracy"}, {"type": "f1", "value": 0.9371, "name": "F1"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.923, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.8676576686813523, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.923, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.9268406401714973, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.8945488803260702, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.923, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.923, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.8798961895301041, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.923, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.9241278880972197, "name": "F1 Weighted", "verified": true}, {"type": "loss", "value": 0.24626904726028442, "name": "loss", "verified": true}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,932 |
avfawkes/marian-finetuned-to-restore-ru-vowels
|
avfawkes
|
translation
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-uk-ru",
"base_model:finetune:Helsinki-NLP/opus-mt-uk-ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-11T12:44:24Z |
2024-06-14T11:50:06+00:00
| 4 | 0 |
---
base_model: Helsinki-NLP/opus-mt-uk-ru
license: apache-2.0
metrics:
- bleu
tags:
- translation
- generated_from_trainer
widget:
- text: Кгд прхл в дрвн, ббшк бл чнь рд мн вдть. Лбл првдть кннкл з грдм, в дл т
шм ст.
model-index:
- name: marian-finetuned-to-restore-ru-vowels
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-to-restore-ru-vowels
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-uk-ru](https://huggingface.co/Helsinki-NLP/opus-mt-uk-ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4423
- Bleu: 72.1787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| null |
TBD
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-to-restore-ru-vowels
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-uk-ru](https://huggingface.co/Helsinki-NLP/opus-mt-uk-ru) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4423
- Bleu: 72.1787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"base_model": "Helsinki-NLP/opus-mt-uk-ru", "license": "apache-2.0", "metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "widget": [{"text": "Кгд прхл в дрвн, ббшк бл чнь рд мн вдть. Лбл првдть кннкл з грдм, в дл т шм ст."}], "model-index": [{"name": "marian-finetuned-to-restore-ru-vowels", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 41,933 |
huggan/sim2real_cyclegan
|
huggan
|
image-to-image
|
[
"pytorch",
"conditional-image-generation",
"image-to-image",
"gan",
"cyclegan",
"arxiv:2104.13395",
"arxiv:1703.10593",
"license:mit",
"region:us"
] | 2022-04-12T11:33:57Z |
2022-12-16T10:18:00+00:00
| 0 | 7 |
---
license: mit
tags:
- conditional-image-generation
- image-to-image
- gan
- cyclegan
---
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A generator B to A, named G_BA conditioned on an image from B
- A domain classifier D_A, associated with G_AB
- A domain classifier D_B, associated with G_BA
At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A.
In the general setting, this technique provides style transfer functionalities between the selected image domains A and B.
This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA.
Under these framework, these aspects have been used to perform style transfer between synthetic data obtained from a simulated driving dataset, GTA5, and the real driving data from Cityscapes.
This is of paramount importance to develop autonomous driving perception deep learning models, as this allows to generate synthetic data with automatic annotations which resembles real world images, without requiring the intervention of a human annotator.
This is fundamental because a manual annotator has been shown to require 1.5 to 3.3 hours to create semantic and instance segmentation masks for a single images.
These have been provided in the original [cityscapes paper (Cordts et al 2016)](https://arxiv.org/abs/2104.13395) and the [adverse condition dataset (Sakaridis et al. 2021)](https://arxiv.org/abs/2104.13395) paper.
Hence the CycleGAN provides forward and backward translation between synthetic and real world data.
This has showed to allows high quality translation even in absence of paired sample-ground-truth data.
The idea behind such model is that as the synthetic data distribution gets closer to the real world one, deep models do not suffer from degraded performance due to the domain shift issue.
A broad literature is available on the minimization of the domain shift, under the research branch of domain adaptation and transfer learning, of which image translation models provide an alternative approach
## Intended uses & limitations
#### Installation
```bash
git clone https://github.com/huggingface/community-events.git
cd community-events
```
To install the repository as a python package, run:
```bash
pip install .
```
#### How to use
```python
import os
from PIL import Image
from torchvision import transforms as T
from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
from torchvision.utils import make_grid
from torch.utils.data import DataLoader
from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
import torch.nn as nn
import torch
import gradio as gr
import glob
def pred_pipeline(img, transforms):
orig_shape = img.shape
input = transforms(img)
input = input.unsqueeze(0)
output = model(input)
out_img = make_grid(output,#.detach().cpu(),
nrow=1, normalize=True)
out_transform = Compose([
T.Resize(orig_shape[:2]),
T.ToPILImage()
])
return out_transform(out_img)
n_channels = 3
image_size = 512
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
model = GeneratorResNet.from_pretrained('Chris1/sim2real', input_shape=(n_channels, image_size, image_size),
num_residual_blocks=9)
real_images = model(synthetic_images)
```
#### Limitations and bias
Due to the absence of paired data, some background parts of the synthetic images are seldom wrongly translated, e.g. sky is translated to vegetation.
Additional pretext tasks in parallel to the discriminative classifier of fake and real samples could improve the result.
One easy improvement is the use of an additional parallel branch that performs semantic segmentation on the synthetic data, in order to learn features which are common to sky and vegetation, thus disentangling their representations as separate classes.
## Training data
The CycleGAN model is trained on an unpaired dataset of samples from synthetic and real driving data, respectively from the GTA5 and Cityscapes datasets.
To this end, the synthetic-to-real dataset can be loaded by means of the function load_dataset in the huggingface library, as follows.
```python
from datasets import load_dataset
unpaired_dataset = load_dataset("huggan/sim2real_gta5_to_cityscapes")
```
This dataset contains two columns, imageA and imageB representing respectively the GTA5 and Cityscapes data.
Due to the fact that the two columns have to be of the same length, GTA5 is subsampled in order to reach the same number of samples provided by the Cityscapes train split (2975)
## Training procedure
#### Preprocessing
The following transformations are applied to each input sample of synthetic and real data.
The input size is fixed to RGB images of height, width = 512, 512.
This choice has been made in order to limit the impact of upsampling the translated images to higher resolutions.
```python
n_channels = 3
image_size = 512
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
```
#### Hardware
The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs.
#### Hyperparameters
The following configuration has been kept fixed for all translation models:
- learning rate 0.0002
- number of epochs 200
- learning rate decay activation at epoch 100
- number of residual blocks of the cyclegan 9
- image size 512x512
- number of channels=3
- cycle loss weight 10.0
- identity loss weight 5.0
- optimizer ADAM with beta1 0.5 and beta2 0.999
- batch size 8
- NO mixed precision training
## Eval results
#### Generated Images
In the provided images, row0 and row2 represent the synthetic and real images from the respective datasets.
Row1 is the translation of the immediate above images in row0(synthetic) by means of the G_AB translation model, to the real world style.
Row3 is the translation of the immediate above images in row2(real) by means of the G_BA translation model, to the synthetic world style.
Visualization over the training iterations for [synthetic (GTA5) to real (Cityscapes) translation](https://wandb.ai/chris1nexus/experiments_cyclegan_s2r_hp_opt--10/reports/CycleGAN-sim2real-training-results--VmlldzoxODUyNTk4?accessToken=tow3v4vp02aurzodedrdht15ig1cx69v5mited4dm8bgnup0z192wri0xtftaeqj)
### References
```bibtex
@misc{https://doi.org/10.48550/arxiv.1703.10593,
doi = {10.48550/ARXIV.1703.10593},
url = {https://arxiv.org/abs/1703.10593},
author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
| null |
Non_BioNLP
|
# CycleGAN for unpaired image-to-image translation.
## Model description
CycleGAN for unpaired image-to-image translation.
Given two image domains A and B, the following components are trained end2end to translate between such domains:
- A generator A to B, named G_AB conditioned on an image from A
- A generator B to A, named G_BA conditioned on an image from B
- A domain classifier D_A, associated with G_AB
- A domain classifier D_B, associated with G_BA
At inference time, G_AB or G_BA are relevant to translate images, respectively A to B or B to A.
In the general setting, this technique provides style transfer functionalities between the selected image domains A and B.
This allows to obtain a generated translation by G_AB, of an image from domain A that resembles the distribution of the images from domain B, and viceversa for the generator G_BA.
Under these framework, these aspects have been used to perform style transfer between synthetic data obtained from a simulated driving dataset, GTA5, and the real driving data from Cityscapes.
This is of paramount importance to develop autonomous driving perception deep learning models, as this allows to generate synthetic data with automatic annotations which resembles real world images, without requiring the intervention of a human annotator.
This is fundamental because a manual annotator has been shown to require 1.5 to 3.3 hours to create semantic and instance segmentation masks for a single images.
These have been provided in the original [cityscapes paper (Cordts et al 2016)](https://arxiv.org/abs/2104.13395) and the [adverse condition dataset (Sakaridis et al. 2021)](https://arxiv.org/abs/2104.13395) paper.
Hence the CycleGAN provides forward and backward translation between synthetic and real world data.
This has showed to allows high quality translation even in absence of paired sample-ground-truth data.
The idea behind such model is that as the synthetic data distribution gets closer to the real world one, deep models do not suffer from degraded performance due to the domain shift issue.
A broad literature is available on the minimization of the domain shift, under the research branch of domain adaptation and transfer learning, of which image translation models provide an alternative approach
## Intended uses & limitations
#### Installation
```bash
git clone https://github.com/huggingface/community-events.git
cd community-events
```
To install the repository as a python package, run:
```bash
pip install .
```
#### How to use
```python
import os
from PIL import Image
from torchvision import transforms as T
from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip
from torchvision.utils import make_grid
from torch.utils.data import DataLoader
from huggan.pytorch.cyclegan.modeling_cyclegan import GeneratorResNet
import torch.nn as nn
import torch
import gradio as gr
import glob
def pred_pipeline(img, transforms):
orig_shape = img.shape
input = transforms(img)
input = input.unsqueeze(0)
output = model(input)
out_img = make_grid(output,#.detach().cpu(),
nrow=1, normalize=True)
out_transform = Compose([
T.Resize(orig_shape[:2]),
T.ToPILImage()
])
return out_transform(out_img)
n_channels = 3
image_size = 512
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
model = GeneratorResNet.from_pretrained('Chris1/sim2real', input_shape=(n_channels, image_size, image_size),
num_residual_blocks=9)
real_images = model(synthetic_images)
```
#### Limitations and bias
Due to the absence of paired data, some background parts of the synthetic images are seldom wrongly translated, e.g. sky is translated to vegetation.
Additional pretext tasks in parallel to the discriminative classifier of fake and real samples could improve the result.
One easy improvement is the use of an additional parallel branch that performs semantic segmentation on the synthetic data, in order to learn features which are common to sky and vegetation, thus disentangling their representations as separate classes.
## Training data
The CycleGAN model is trained on an unpaired dataset of samples from synthetic and real driving data, respectively from the GTA5 and Cityscapes datasets.
To this end, the synthetic-to-real dataset can be loaded by means of the function load_dataset in the huggingface library, as follows.
```python
from datasets import load_dataset
unpaired_dataset = load_dataset("huggan/sim2real_gta5_to_cityscapes")
```
This dataset contains two columns, imageA and imageB representing respectively the GTA5 and Cityscapes data.
Due to the fact that the two columns have to be of the same length, GTA5 is subsampled in order to reach the same number of samples provided by the Cityscapes train split (2975)
## Training procedure
#### Preprocessing
The following transformations are applied to each input sample of synthetic and real data.
The input size is fixed to RGB images of height, width = 512, 512.
This choice has been made in order to limit the impact of upsampling the translated images to higher resolutions.
```python
n_channels = 3
image_size = 512
input_shape = (image_size, image_size)
transform = Compose([
T.ToPILImage(),
T.Resize(input_shape),
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
```
#### Hardware
The configuration has been tested on single GPU setup on a RTX5000 and A5000, as well as multi-gpu single-rank distributed setups composed of 2 of the mentioned GPUs.
#### Hyperparameters
The following configuration has been kept fixed for all translation models:
- learning rate 0.0002
- number of epochs 200
- learning rate decay activation at epoch 100
- number of residual blocks of the cyclegan 9
- image size 512x512
- number of channels=3
- cycle loss weight 10.0
- identity loss weight 5.0
- optimizer ADAM with beta1 0.5 and beta2 0.999
- batch size 8
- NO mixed precision training
## Eval results
#### Generated Images
In the provided images, row0 and row2 represent the synthetic and real images from the respective datasets.
Row1 is the translation of the immediate above images in row0(synthetic) by means of the G_AB translation model, to the real world style.
Row3 is the translation of the immediate above images in row2(real) by means of the G_BA translation model, to the synthetic world style.
Visualization over the training iterations for [synthetic (GTA5) to real (Cityscapes) translation](https://wandb.ai/chris1nexus/experiments_cyclegan_s2r_hp_opt--10/reports/CycleGAN-sim2real-training-results--VmlldzoxODUyNTk4?accessToken=tow3v4vp02aurzodedrdht15ig1cx69v5mited4dm8bgnup0z192wri0xtftaeqj)
### References
```bibtex
@misc{https://doi.org/10.48550/arxiv.1703.10593,
doi = {10.48550/ARXIV.1703.10593},
url = {https://arxiv.org/abs/1703.10593},
author = {Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
{"license": "mit", "tags": ["conditional-image-generation", "image-to-image", "gan", "cyclegan"]}
|
task
|
[
"TRANSLATION"
] | 41,934 |
TransferGraph/markt23917_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion
|
TransferGraph
|
text-classification
|
[
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:tweet_eval",
"base_model:markt23917/finetuning-sentiment-model-3000-samples",
"base_model:adapter:markt23917/finetuning-sentiment-model-3000-samples",
"license:apache-2.0",
"model-index",
"region:us"
] | 2024-02-29T12:49:04Z |
2024-02-29T12:49:06+00:00
| 0 | 0 |
---
base_model: markt23917/finetuning-sentiment-model-3000-samples
datasets:
- tweet_eval
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: markt23917_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- type: accuracy
value: 0.7299465240641712
name: accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# markt23917_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion
This model is a fine-tuned version of [markt23917/finetuning-sentiment-model-3000-samples](https://huggingface.co/markt23917/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.7299
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.2299 | None | 0 |
| 0.6979 | 0.8761 | 0 |
| 0.7487 | 0.6605 | 1 |
| 0.7353 | 0.6087 | 2 |
| 0.7299 | 0.5795 | 3 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# markt23917_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion
This model is a fine-tuned version of [markt23917/finetuning-sentiment-model-3000-samples](https://huggingface.co/markt23917/finetuning-sentiment-model-3000-samples) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.7299
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.2299 | None | 0 |
| 0.6979 | 0.8761 | 0 |
| 0.7487 | 0.6605 | 1 |
| 0.7353 | 0.6087 | 2 |
| 0.7299 | 0.5795 | 3 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
|
{"base_model": "markt23917/finetuning-sentiment-model-3000-samples", "datasets": ["tweet_eval"], "library_name": "peft", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "markt23917_finetuning-sentiment-model-3000-samples-finetuned-lora-tweet_eval_emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "config": "emotion", "split": "validation", "args": "emotion"}, "metrics": [{"type": "accuracy", "value": 0.7299465240641712, "name": "accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,935 |
flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A
|
flax-sentence-embeddings
|
sentence-similarity
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"arxiv:2102.07033",
"arxiv:2104.08727",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2021-07-25T21:33:06+00:00
| 38 | 3 |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# multi-QA_v1-mpnet-asymmetric-A
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used two separate pretrained [mpnet-base](https://huggingface.co/microsoft/mpnet-base) models and trained them using contrastive learning objective. Question and answer pairs from StackExchange and other datasets were used as training data to make the model robust to Question / Answer embedding similarity.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
This model set is intended to be used as a sentence encoder for a search engine. Given an input sentence, it ouptuts a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
Two models should be used on conjunction for Semantic Search purposes.
1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q) - Model to encode Questions
1. [multi-QA_v1-mpnet-asymmetric-A](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A) - Model to encode Answers
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q')
model_A = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A')
question = "Replace me by any question you'd like."
question_embbedding = model_Q.encode(text)
answer = "Replace me by any answer you'd like."
answer_embbedding = model_A.encode(text)
answer_likeliness = cosine_similarity(question_embedding, answer_embedding)
```
# Training procedure
## Pre-training
We use the pretrained [`Mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| null |
Non_BioNLP
|
# multi-QA_v1-mpnet-asymmetric-A
## Model Description
SentenceTransformers is a set of models and frameworks that enable training and generating sentence embeddings from given data. The generated sentence embeddings can be utilized for Clustering, Semantic Search and other tasks. We used two separate pretrained [mpnet-base](https://huggingface.co/microsoft/mpnet-base) models and trained them using contrastive learning objective. Question and answer pairs from StackExchange and other datasets were used as training data to make the model robust to Question / Answer embedding similarity.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well
as assistance from Google’s Flax, JAX, and Cloud team members about efficient deep learning frameworks.
## Intended uses
This model set is intended to be used as a sentence encoder for a search engine. Given an input sentence, it ouptuts a vector which captures
the sentence semantic information. The sentence vector may be used for semantic-search, clustering or sentence similarity tasks.
Two models should be used on conjunction for Semantic Search purposes.
1. [multi-QA_v1-mpnet-asymmetric-Q](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q) - Model to encode Questions
1. [multi-QA_v1-mpnet-asymmetric-A](https://huggingface.co/flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A) - Model to encode Answers
## How to use
Here is how to use this model to get the features of a given text using [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) library:
```python
from sentence_transformers import SentenceTransformer
model_Q = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-Q')
model_A = SentenceTransformer('flax-sentence-embeddings/multi-QA_v1-mpnet-asymmetric-A')
question = "Replace me by any question you'd like."
question_embbedding = model_Q.encode(text)
answer = "Replace me by any answer you'd like."
answer_embbedding = model_A.encode(text)
answer_likeliness = cosine_similarity(question_embedding, answer_embedding)
```
# Training procedure
## Pre-training
We use the pretrained [`Mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model
card for more detailed information about the pre-training procedure.
## Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
### Hyper parameters
We trained on model on a TPU v3-8. We train the model during 80k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository.
### Training data
We used the concatenation from multiple Stackexchange Question-Answer datasets to fine-tune our model. MSMARCO, NQ & other question-answer datasets were also used.
| Dataset | Paper | Number of training tuples |
|:--------------------------------------------------------:|:----------------------------------------:|:--------------------------:|
| [Stack Exchange QA - Title & Answer](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) | - | 4,750,619 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) | - | 364,001 |
| [TriviaqQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [Quora Question Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [PAQ](https://github.com/facebookresearch/PAQ) | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [MS MARCO](https://microsoft.github.io/msmarco/) | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) Question/Answer | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| SearchQA | - | 582,261 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
|
{"pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"]}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,936 |
comet24082002/ft_bge_newLaw_OnlineContrastiveLoss_V1_5epochs
|
comet24082002
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10524",
"loss:OnlineContrastiveLoss",
"arxiv:1908.10084",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-06-09T03:51:25Z |
2024-06-09T03:52:35+00:00
| 6 | 0 |
---
base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10524
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Thời gian cấp giấy đăng ký kết hôn là bao lâu?
sentences:
- '"Điều 4. Thủ tục, thẩm quyền giải quyết đề nghị cấp thẻ tạm trú
1. Người nước ngoài thuộc diện được cấp thẻ tạm trú theo quy định tại khoản 2
Điều 36 của Luật phải thông qua cơ quan, tổ chức, cá nhân đã mời, bảo lãnh trực
tiếp làm thủ tục tại Cục Quản lý xuất nhập cảnh hoặc Phòng Quản lý xuất nhập cảnh
Công an tỉnh, thành phố trực thuộc trung ương nơi cơ quan, tổ chức mời, bảo lãnh
đặt trụ sở hoặc nơi cá nhân mời, bảo lãnh cư trú.
2. Hồ sơ đề nghị cấp thẻ tạm trú theo quy định tại khoản 1 Điều 37 của Luật. Văn
bản đề nghị cấp thẻ tạm trú nêu tại điểm a khoản 1 Điều 37 của Luật theo mẫu do
Bộ Công an ban hành tại Thông tư số 04/2015/TT-BCA ngày 05/01/2015 quy định mẫu
giấy tờ liên quan đến việc nhập cảnh, xuất cảnh, cư trú của người nước ngoài tại
Việt Nam gồm:
a) Mẫu NA6 và NA8 sử dụng cho cơ quan, tổ chức;
b) Mẫu NA7 và NA8 sử dụng cho cá nhân.
3. Giải quyết cấp thẻ tạm trú
Trong thời hạn 05 ngày làm việc kể từ ngày nhận đủ hồ sơ, Cục Quản lý xuất nhập
cảnh hoặc Phòng Quản lý xuất nhập cảnh Công an tỉnh, thành phố trực thuộc trung
ương xem xét cấp thẻ tạm trú.
4. Thời hạn của thẻ tạm trú được cấp phù hợp với mục đích nhập cảnh và đề nghị
của cơ quan, tổ chức, cá nhân. Thẻ tạm trú có thời hạn từ 01 năm đến 05 năm nhưng
ngắn hơn thời hạn của hộ chiếu ít nhất 30 ngày."'
- 'Nội dung hợp đồng xây dựng
1. Hợp đồng xây dựng gồm các nội dung sau:
a) Căn cứ pháp lý áp dụng;
b) Ngôn ngữ áp dụng;
c) Nội dung và khối lượng công việc;
d) Chất lượng, yêu cầu kỹ thuật của công việc, nghiệm thu và bàn giao;
đ) Thời gian và tiến độ thực hiện hợp đồng;
e) Giá hợp đồng, tạm ứng, đồng tiền sử dụng trong thanh toán và thanh toán hợp
đồng xây dựng;
g) Bảo đảm thực hiện hợp đồng, bảo lãnh tạm ứng hợp đồng;
h) Điều chỉnh hợp đồng xây dựng;
i) Quyền và nghĩa vụ của các bên tham gia hợp đồng xây dựng;
k) Trách nhiệm do vi phạm hợp đồng, thưởng và phạt vi phạm hợp đồng;
l) Tạm ngừng và chấm dứt hợp đồng xây dựng;
m) Giải quyết tranh chấp hợp đồng xây dựng;
n) Rủi ro và bất khả kháng;
o) Quyết toán và thanh lý hợp đồng xây dựng;
p) Các nội dung khác.
2. Đối với hợp đồng tổng thầu xây dựng ngoài các nội dung quy định tại khoản 1
Điều này còn phải được bổ sung về nội dung và trách nhiệm quản lý của tổng thầu
xây dựng.
3. Chính phủ quy định chi tiết về hợp đồng xây dựng.'
- 'Nội dung vi phạm và hình thức xử lý kỷ luật đối với đoàn viên
1- Đoàn viên vi phạm một trong các trường hợp sau đây thì bị xử lý kỷ luật bằng
hình thức khiển trách:
a) Không chấp hành và không thực hiện Điều lệ Công đoàn Việt Nam, hoặc nghị quyết,
quyết định, quy định, kế hoạch, chương trình công tác... của công đoàn các cấp
gây hậu quả ít nghiêm trọng.
b) Không dự họp 50% trở lên số kỳ họp công đoàn trong một năm.
c) Không đóng đoàn phí liên tục 6 tháng mà không có lý do chính đáng.
2- Đoàn viên vi phạm một trong các trường hợp sau đây thì bị xử lý kỷ luật bằng
hình thức cảnh cáo:
a) Đã bị xử lý kỷ luật theo quy định tại tiết a khoản 1 Điều này mà tái phạm hoặc
vi phạm lần đầu gây hậu quả nghiêm trọng.
b) Không dự hợp 70% trở lên số kỳ họp công đoàn trong một năm.
c) Không đóng đoàn phí liên tục 9 tháng mà không có lý do chính đáng.
3- Đoàn viên vi phạm một trong các trường hợp sau đây thì bị xử lý kỷ luật bằng
hình thức khai trừ:
a) Đã bị xử lý kỷ luật theo quy định tại tiết a khoản 2 Điều này mà tái phạm hoặc
vi phạm lần đầu gây hậu quả rất nghiêm trọng.
b) Không dự họp 90% trở lên số kỳ họp công đoàn trong một năm.
c) Không đóng đoàn phí liên tục 12 tháng mà không có lý do chính đáng.'
- source_sentence: Hội đồng thảo luận, trao đổi và biểu quyết để tuyển chọn Thẩm phán
trung cấp Tòa án quân sự quân khu như thế nào?
sentences:
- 'Quy định về việc sử dụng phần sở hữu chung của nhà chung cư
1. Sử dụng thang máy và các thiết bị sử dụng chung theo đúng mục đích, công năng
thiết kế sử dụng.
2. Không được làm hư hỏng hoặc có hành vi vi phạm đến tài sản chung của nhà chung
cư.
3. Tuân thủ đầy đủ các quy định về việc dừng, đỗ xe tại nơi được dừng, đỗ xe theo
quy định.
4. Sử dụng nhà sinh hoạt cộng đồng vào đúng mục đích, công năng theo quy định
của pháp Luật về nhà ở.
5. Tuân thủ đầy đủ các quy định về an toàn phòng cháy, chữa cháy của nhà chung
cư.
6. Các quy định khác: do Hội nghị nhà chung cư quy định thêm cho phù hợp với từng
nhà chung cư (nếu có).'
- 'Tiêu chuẩn đối với kiểm toán viên hành nghề được chấp thuận
Ngoài các tiêu chuẩn quy định tại Luật kiểm toán độc lập, kiểm toán viên hành
nghề được chấp thuận phải có các tiêu chuẩn sau:
1. Có tên trong danh sách kiểm toán viên đủ Điều kiện hành nghề kiểm toán trong
kỳ chấp thuận được Bộ Tài chính công khai tại thời Điểm nộp hồ sơ đăng ký thực
hiện kiểm toán.
2. Có ít nhất 24 tháng thực tế hành nghề kiểm toán tại Việt Nam tính từ ngày được
cơ quan có thẩm quyền xác nhận đủ Điều kiện hành nghề kiểm toán đến ngày nộp hồ
sơ đăng ký thực hiện kiểm toán.'
- 'Việc báo cáo với Hội đồng về hồ sơ của người được đề nghị tuyển chọn và bổ nhiệm
Thẩm phán theo danh sách trích ngang đã lập được thực hiện đối với từng Tòa án
một và đối với từng người một.
Hội đồng thảo luận, trao đổi và biểu quyết đối với từng người một.'
- source_sentence: Toà án phải gửi quyết định áp dụng biện pháp xử lý hành chính đưa
vào trường giáo dưỡng cho người đề nghị trong thời gian bao lâu từ ngày ra quyết
định?
sentences:
- 'Tiêu chuẩn cá nhân biên soạn sách giáo khoa
1. Người biên soạn sách giáo khoa phải đáp ứng các tiêu chuẩn sau:
a) Có trình độ được đào tạo từ đại học trở lên theo chuyên ngành phù hợp với môn
học, hoạt động giáo dục có sách giáo khoa được biên soạn; am hiểu về khoa học
giáo dục; có ít nhất 03 (ba) năm trực tiếp giảng dạy hoặc nghiên cứu về chuyên
môn phù hợp với môn học, hoạt động giáo dục có sách giáo khoa được biên soạn;
b) Là công dân Việt Nam, có phẩm chất đạo đức, tư tưởng tốt.
2. Người tham gia biên soạn sách giáo khoa không tham gia thẩm định sách giáo
khoa.'
- 'Quy trình bổ nhiệm lại
1. Chậm nhất 02 tháng trước ngày hết nhiệm kỳ, Hòa giải viên có nguyện vọng tiếp
tục làm Hòa giải viên, nộp hồ sơ đề nghị bổ nhiệm lại tại Tòa án nơi Hòa giải
viên làm việc.
2. Căn cứ nhu cầu thực tế, trên cơ sở kết quả thực hiện nhiệm vụ của Hòa giải
viên, Tòa án nơi có Hòa giải viên làm việc có văn bản đề nghị Chánh án Tòa án
nhân dân cấp tỉnh xem xét bổ nhiệm lại theo quy định.
3. Quy trình bổ nhiệm lại Hòa giải viên được thực hiện như quy định tại khoản
3, khoản 4 và khoản 5 Điều 6 Thông tư này.
4. Quyết định bổ nhiệm lại Hòa giải viên phải được ban hành trước ít nhất 01 ngày
làm việc, tính đến ngày hết nhiệm kỳ làm Hòa giải viên. Trường hợp không đáp ứng
điều kiện để bổ nhiệm lại, Chánh án Tòa án nhân dân cấp tỉnh phải thông báo bằng
văn bản và nêu rõ lý do.'
- '"Điều 13. Điều kiện cấp Chứng chỉ hành nghề dược
1. Có văn bằng, chứng chỉ, giấy chứng nhận chuyên môn (sau đây gọi chung là văn
bằng chuyên môn) được cấp hoặc công nhận tại Việt Nam phù hợp với vị trí công
việc và cơ sở kinh doanh dược bao gồm:
a) Bằng tốt nghiệp đại học ngành dược (sau đây gọi là Bằng dược sỹ);
b) Bằng tốt nghiệp đại học ngành y đa khoa;
c) Bằng tốt nghiệp đại học ngành y học cổ truyền hoặc đại học ngành dược cổ truyền;
d) Bằng tốt nghiệp đại học ngành sinh học;
đ) Bằng tốt nghiệp đại học ngành hóa học;
e) Bằng tốt nghiệp cao đẳng ngành dược;
g) Bằng tốt nghiệp trung cấp ngành dược;
h) Bằng tốt nghiệp cao đẳng, trung cấp ngành y;
i) Bằng tốt nghiệp trung cấp y học cổ truyền hoặc dược cổ truyền;
k) Văn bằng, chứng chỉ sơ cấp dược;
l) Giấy chứng nhận về lương y, giấy chứng nhận về lương dược, giấy chứng nhận
bài thuốc gia truyền hoặc văn bằng, chứng chỉ, giấy chứng nhận khác về y dược
cổ truyền được cấp trước ngày Luật này có hiệu lực.
Việc áp dụng Điều kiện về văn bằng, chứng chỉ, giấy chứng nhận quy định tại Điểm
l Khoản này do Bộ trưởng Bộ Y tế quy định phù hợp với Điều kiện phát triển kinh
tế - xã hội và nhu cầu khám bệnh, chữa bệnh của Nhân dân ở từng địa phương trong
từng thời kỳ.
2. Có thời gian thực hành tại cơ sở kinh doanh dược, bộ phận dược của cơ sở khám
bệnh, chữa bệnh, trường đào tạo chuyên ngành dược, cơ sở nghiên cứu dược, cơ sở
kiểm nghiệm thuốc, nguyên liệu làm thuốc, cơ quan quản lý về dược hoặc văn phòng
đại diện của thương nhân nước ngoài hoạt động trong lĩnh vực dược tại Việt Nam
(sau đây gọi chung là cơ sở dược); cơ sở khám bệnh, chữa bệnh phù hợp với chuyên
môn của người hành nghề theo quy định sau đây:
a) Đối với người bị thu hồi Chứng chỉ hành nghề dược theo quy định tại Khoản 9
Điều 28 của Luật này thì không yêu cầu thời gian thực hành nhưng phải cập nhật
kiến thức chuyên môn về dược;
b) Đối với người có trình độ chuyên khoa sau đại học phù hợp với phạm vi hành
nghề thì được giảm thời gian thực hành theo quy định của Chính phủ;
c) Đối với người có văn bằng chuyên môn quy định tại Điểm l Khoản 1 Điều 13 của
Luật này thì thời gian thực hành theo quy định của Bộ trưởng Bộ Y tế.
3. Có giấy chứng nhận đủ sức khỏe để hành nghề dược do cơ sở y tế có thẩm quyền
cấp.
4. Không thuộc một trong các trường hợp sau đây:
a) Đang bị truy cứu trách nhiệm hình sự, đang chấp hành bản án, quyết định của
Tòa án; trong thời gian bị cấm hành nghề, cấm làm công việc liên quan đến hoạt
động dược theo bản án, quyết định của Tòa án;
b) Bị hạn chế năng lực hành vi dân sự.
5. Đối với người tự nguyện xin cấp Chứng chỉ hành nghề dược theo hình thức thi,
phải đáp ứng đủ Điều kiện theo quy định tại Điều này."'
- source_sentence: Doanh nghiệp nhỏ và vừa tham gia chuỗi giá trị sản xuất, chế biến
có được hỗ trợ chi phí đào tạo hay không?
sentences:
- 'Nội dung hỗ trợ doanh nghiệp nhỏ và vừa tham gia cụm liên kết ngành, chuỗi giá
trị
1. Hỗ trợ đào tạo
a) Hỗ trợ tối đa 50% chi phí tổ chức khóa đào tạo nâng cao trình độ công nghệ,
kỹ thuật sản xuất chuyên sâu tại doanh nghiệp nhưng không quá 50 triệu đồng/khoá/năm/doanh
nghiệp;
b) Hỗ trợ tối đa 50% chi phí đào tạo cho học viên của doanh nghiệp khi tham gia
các khóa đào tạo chuyên sâu theo nhu cầu phát triển của ngành, chuỗi giá trị nhưng
không quá 10 triệu đồng/học viên/năm và không quá 03 học viên/doanh nghiệp/năm.
2. Hỗ trợ nâng cao năng lực liên kết sản xuất và kinh doanh
a) Hỗ trợ 100% giá trị hợp đồng tư vấn đánh giá toàn diện năng lực của doanh nghiệp
nhỏ và vừa trong cụm liên kết ngành, chuỗi giá trị nhưng không quá 30 triệu đồng/hợp
đồng/năm/doanh nghiệp;
b) Hỗ trợ 100% giá trị hợp đồng tư vấn cải tiến, nâng cấp kỹ thuật chuyên sâu
cho doanh nghiệp nhỏ và vừa nhằm cải thiện năng lực sản xuất, đáp ứng yêu cầu
kết nối, trở thành nhà cung cấp của doanh nghiệp đầu chuỗi nhưng không quá 100
triệu đồng/hợp đồng/năm/doanh nghiệp.
...'
- 'Chế độ tập sự
...
4. Trong thời gian thực hiện chế độ tập sự, đơn vị sự nghiệp công lập quản lý,
sử dụng viên chức phải cử viên chức tham gia khóa đào tạo, bồi dưỡng để hoàn chỉnh
tiêu chuẩn, điều kiện của chức danh nghề nghiệp trước khi bổ nhiệm. Thời gian
tham gia khóa đào tạo, bồi dưỡng được tính vào thời gian thực hiện chế độ tập
sự.
5. Không thực hiện chế độ tập sự đối với các trường hợp đã có thời gian công tác
có đóng bảo hiểm xã hội bắt buộc theo đúng quy định của Luật Bảo hiểm xã hội,
được bố trí làm việc theo đúng ngành, nghề đào tạo hoặc theo đúng chuyên môn nghiệp
vụ trước đây đã đảm nhiệm mà thời gian công tác có đóng bảo hiểm xã hội (nếu đứt
quãng thì được cộng dồn) bằng hoặc lớn hơn thời gian tập sự tương ứng với thời
gian tập sự của chức danh nghề nghiệp được tuyển dụng quy định tại khoản 2 Điều
này. Đối với các trường hợp không thực hiện chế độ tập sự, người đứng đầu đơn
vị sự nghiệp công lập phải cử viên chức tham gia khóa bồi dưỡng để hoàn chỉnh
tiêu chuẩn, điều kiện của chức danh nghề nghiệp viên chức trước khi bổ nhiệm.
...'
- '"Điều 6. Nguyên tắc xếp lương và phụ cấp
1. Việc xếp lương, nâng bậc lương và áp dụng các chế độ phụ cấp thực hiện theo
quy định chung do Chính phủ ban hành.
2. Đối với các chức danh lãnh đạo (bầu cử, bổ nhiệm) chuyên trách Hội Cựu chiến
binh Việt Nam từ Trung ương đến cấp huyện, ngoài lương hưu, hàng tháng được hưởng
90% mức lương chuẩn và phụ cấp chức vụ (không phải đóng bảo hiểm xã hội và bảo
hiểm y tế).
Đối với cựu chiến binh thuộc chỉ tiêu biên chế được duyệt làm công tác chuyên
môn, nghiệp vụ từ Trung ương đến cấp huyện, ngoài lương hưu, hàng tháng được hưởng
cao nhất 90% mức lương chuẩn của cấp phó lãnh đạo trực tiếp cùng cấp (không phải
đóng bảo hiểm xã hội và bảo hiểm y tế)."'
- source_sentence: Tàu cá không thực hiện đánh dấu theo quy định có thể bị xử phạt
như thế nào?
sentences:
- 'Vi phạm quy định về đánh dấu tàu cá
1. Phạt tiền từ 3.000.000 đồng đến 5.000.000 đồng đối với hành vi không đánh dấu
nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có
chiều dài lớn nhất từ 12 mét đến dưới 15 mét để khai thác thủy sản.
2. Phạt tiền từ 5.000.000 đồng đến 7.000.000 đồng đối với hành vi không đánh dấu
nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có
chiều dài lớn nhất từ 15 mét đến dưới 24 mét để khai thác thủy sản.
3. Phạt tiền từ 7.000.000 đồng đến 10.000.000 đồng đối với hành vi không đánh
dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá
có chiều dài lớn nhất từ 24 mét trở lên để khai thác thủy sản.'
- 'Đại hội toàn thể hội viên và Đại hội bất thường
...
2. Nhiệm vụ chính của Đại hội:
a) Thảo luận, thông qua các báo cáo hoạt động của nhiệm kỳ trước, đề ra phương
hướng hoạt động nhiệm kỳ mới của Hiệp hội;
b) Thông qua những điểm bổ sung hoặc sửa đổi Điều lệ của Hiệp hội;
c) Thảo luận và quyết định một số vấn đề quan trọng của Hiệp hội vượt quá thẩm
quyền giải quyết của Ban Chấp hành Hiệp hội;
d) Bầu Ban Chấp hành Hiệp hội và Ban Kiểm tra Hiệp hội;
đ) Thảo luận, phê duyệt quyết toán tài chính và thông qua kế hoạch tài chính khóa
mới;
e) Thảo luận, thông qua các vấn đề khác theo quy định của Điều lệ Hiệp hội.
...'
- 'Đối tượng được thăm gặp phạm nhân
1. Đối tượng được thăm gặp phạm nhân gồm: Ông, bà nội; ông, bà ngoại; bố, mẹ đẻ;
bố, mẹ vợ (hoặc chồng); bố, mẹ nuôi hợp pháp; vợ hoặc chồng; con đẻ, con dâu,
con rể, con nuôi hợp pháp; anh, chị, em ruột; anh, chị, em dâu, rể; anh, chị em
vợ (hoặc chồng); cô, dì, chú, bác, cậu, cháu ruột, số lượng thân nhân mỗi lần
đến thăm gặp phạm nhân không quá 03 người.
2. Đại diện cơ quan, tổ chức hoặc cá nhân khác ngoài những người được quy định
tại Khoản 1 Điều này được thăm gặp phạm nhân nếu Thủ trưởng cơ sở giam giữ xét
thấy phù hợp với yêu cầu công tác phòng, chống tội phạm và công tác quản lý, giáo
dục cải tạo phạm nhân.'
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision babcf60cae0a1f438d7ade582983d4ba462303c2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("comet24082002/ft_bge_newLaw_OnlineContrastiveLoss_V1_5epochs")
# Run inference
sentences = [
'Tàu cá không thực hiện đánh dấu theo quy định có thể bị xử phạt như thế nào?',
'Vi phạm quy định về đánh dấu tàu cá\n1. Phạt tiền từ 3.000.000 đồng đến 5.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 12 mét đến dưới 15 mét để khai thác thủy sản.\n2. Phạt tiền từ 5.000.000 đồng đến 7.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 15 mét đến dưới 24 mét để khai thác thủy sản.\n3. Phạt tiền từ 7.000.000 đồng đến 10.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 24 mét trở lên để khai thác thủy sản.',
'Đại hội toàn thể hội viên và Đại hội bất thường\n...\n2. Nhiệm vụ chính của Đại hội:\na) Thảo luận, thông qua các báo cáo hoạt động của nhiệm kỳ trước, đề ra phương hướng hoạt động nhiệm kỳ mới của Hiệp hội;\nb) Thông qua những điểm bổ sung hoặc sửa đổi Điều lệ của Hiệp hội;\nc) Thảo luận và quyết định một số vấn đề quan trọng của Hiệp hội vượt quá thẩm quyền giải quyết của Ban Chấp hành Hiệp hội;\nd) Bầu Ban Chấp hành Hiệp hội và Ban Kiểm tra Hiệp hội;\nđ) Thảo luận, phê duyệt quyết toán tài chính và thông qua kế hoạch tài chính khóa mới;\ne) Thảo luận, thông qua các vấn đề khác theo quy định của Điều lệ Hiệp hội.\n...',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10,524 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 8 tokens</li><li>mean: 24.17 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 282.93 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Nhân viên kiểm định của tổ chức kiểm định được chỉ định có bắt buộc phải có bằng đại học mới được cấp thẻ kiểm định viên đo lường không?</code> | <code>Yêu cầu đối với nhân viên kiểm định để được chứng nhận, cấp thẻ kiểm định viên đo lường<br>Nhân viên kiểm định của tổ chức kiểm định được chỉ định đáp ứng các yêu cầu sau đây được chứng nhận, cấp thẻ kiểm định viên đo lường:<br>1. Tốt nghiệp trung cấp hoặc tương đương trở lên.<br>2. Hoàn thành khóa đào tạo về kiểm định do Tổng cục tổ chức thực hiện tương ứng với lĩnh vực được chỉ định.<br>3. Có ít nhất mười hai (12) tháng kinh nghiệm hoạt động kiểm định, hiệu chuẩn, thử nghiệm tương ứng với lĩnh vực được chỉ định.</code> | <code>1</code> |
| <code>Tái phạm hành vi công chứng, chứng thực văn bằng, chứng chỉ trái quy định thì Đảng viên sẽ bị xử lý thế nào?</code> | <code>Hết thời hạn chấp hành quyết định áp dụng các biện pháp xử lý hành chính<br>1. Khi người vi phạm đã chấp hành xong quyết định giáo dục tại xã, phường, thị trấn thì Chủ tịch Uỷ ban nhân dân cấp xã cấp giấy chứng nhận cho người đã chấp hành xong và gửi bản sao cho gia đình người đó.<br>2. Khi người vi phạm đã chấp hành xong quyết định đưa vào trường giáo dưỡng, đưa vào cơ sở giáo dục bắt buộc, đưa vào cơ sở cai nghiện bắt buộc thì Hiệu trưởng trường giáo dưỡng, Giám đốc cơ sở giáo dục bắt buộc, Giám đốc cơ sở cai nghiện bắt buộc cấp giấy chứng nhận cho người đã chấp hành xong và gửi bản sao cho gia đình người đó, Tòa án nhân dân cấp huyện nơi đã ra quyết định, cơ quan quản lý trường giáo dưỡng, cơ sở giáo dục bắt buộc, cơ sở cai nghiện bắt buộc, Uỷ ban nhân dân cấp xã nơi người đó cư trú.<br>3. Đối tượng không xác định được nơi cư trú là người chưa thành niên hoặc người ốm yếu không còn khả năng lao động thì sau khi hết hạn chấp hành biện pháp đưa vào trường giáo dưỡng, cơ sở giáo dục bắt buộc, cơ sở cai nghiện bắt buộc được đưa về cơ sở bảo trợ xã hội tại địa phương nơi trường giáo dưỡng, cơ sở giáo dục bắt buộc, cơ sở cai nghiện bắt buộc đóng trụ sở.</code> | <code>0</code> |
| <code>Quy định về các nguyên tắc quản lý hồ sơ vụ án trong hệ thống Tòa án nhân dân?</code> | <code>"Điều 4. Nguyên tắc quản lý <br>1. Việc quản lý hồ sơ vụ án trong hệ thống Tòa án nhân dân phải bảo đảm an toàn, nguyên vẹn và các nguyên tắc chung của hồ sơ nghiệp vụ; bảo đảm bí mật theo quy định của pháp luật và của Tòa án nhân dân tôi cao. <br>2. Việc quản lý hồ sơ vụ án được thực hiện tập trung, thông nhất, khoa học, đầy đủ, chặt chẽ, kịp thời, không để bị hư hỏng, thất lạc. 3. Việc quản lý hồ sơ vụ án được thực hiện liên tục từ khi đơn vị, cá nhân lập hô sơ, nhận bàn giao hô sơ cho đên khi chuyên giao hô sơ cho đơn vị, cá nhân khác giải quyêt hoặc nộp vào Lưu trữ cơ quan."</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 4
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0190 | 50 | 0.1151 |
| 0.0380 | 100 | 0.0891 |
| 0.0570 | 150 | 0.0665 |
| 0.0760 | 200 | 0.074 |
| 0.0950 | 250 | 0.0431 |
| 0.1140 | 300 | 0.0578 |
| 0.1330 | 350 | 0.0851 |
| 0.1520 | 400 | 0.0569 |
| 0.1710 | 450 | 0.0648 |
| 0.1900 | 500 | 0.0678 |
| 0.2090 | 550 | 0.0484 |
| 0.2281 | 600 | 0.039 |
| 0.2471 | 650 | 0.0437 |
| 0.2661 | 700 | 0.0666 |
| 0.2851 | 750 | 0.0658 |
| 0.3041 | 800 | 0.0549 |
| 0.3231 | 850 | 0.0546 |
| 0.3421 | 900 | 0.0796 |
| 0.3611 | 950 | 0.0638 |
| 0.3801 | 1000 | 0.074 |
| 0.3991 | 1050 | 0.0838 |
| 0.4181 | 1100 | 0.0719 |
| 0.4371 | 1150 | 0.0761 |
| 0.4561 | 1200 | 0.0595 |
| 0.4751 | 1250 | 0.0803 |
| 0.4941 | 1300 | 0.0734 |
| 0.5131 | 1350 | 0.0875 |
| 0.5321 | 1400 | 0.0755 |
| 0.5511 | 1450 | 0.0694 |
| 0.5701 | 1500 | 0.079 |
| 0.5891 | 1550 | 0.0871 |
| 0.6081 | 1600 | 0.071 |
| 0.6271 | 1650 | 0.0989 |
| 0.6461 | 1700 | 0.081 |
| 0.6651 | 1750 | 0.0705 |
| 0.6842 | 1800 | 0.0861 |
| 0.7032 | 1850 | 0.0859 |
| 0.7222 | 1900 | 0.0818 |
| 0.7412 | 1950 | 0.062 |
| 0.7602 | 2000 | 0.0796 |
| 0.7792 | 2050 | 0.0996 |
| 0.7982 | 2100 | 0.0772 |
| 0.8172 | 2150 | 0.0832 |
| 0.8362 | 2200 | 0.0818 |
| 0.8552 | 2250 | 0.0961 |
| 0.8742 | 2300 | 0.0784 |
| 0.8932 | 2350 | 0.0945 |
| 0.9122 | 2400 | 0.0608 |
| 0.9312 | 2450 | 0.075 |
| 0.9502 | 2500 | 0.0867 |
| 0.9692 | 2550 | 0.0902 |
| 0.9882 | 2600 | 0.0657 |
| 1.0072 | 2650 | 0.0616 |
| 1.0262 | 2700 | 0.093 |
| 1.0452 | 2750 | 0.0684 |
| 1.0642 | 2800 | 0.0632 |
| 1.0832 | 2850 | 0.0513 |
| 1.1022 | 2900 | 0.0471 |
| 1.1212 | 2950 | 0.0815 |
| 1.1403 | 3000 | 0.051 |
| 1.1593 | 3050 | 0.0741 |
| 1.1783 | 3100 | 0.0461 |
| 1.1973 | 3150 | 0.0585 |
| 1.2163 | 3200 | 0.0592 |
| 1.2353 | 3250 | 0.0403 |
| 1.2543 | 3300 | 0.072 |
| 1.2733 | 3350 | 0.0582 |
| 1.2923 | 3400 | 0.08 |
| 1.3113 | 3450 | 0.0708 |
| 1.3303 | 3500 | 0.0447 |
| 1.3493 | 3550 | 0.0607 |
| 1.3683 | 3600 | 0.0522 |
| 1.3873 | 3650 | 0.0731 |
| 1.4063 | 3700 | 0.0992 |
| 1.4253 | 3750 | 0.0582 |
| 1.4443 | 3800 | 0.068 |
| 1.4633 | 3850 | 0.0691 |
| 1.4823 | 3900 | 0.052 |
| 1.5013 | 3950 | 0.0632 |
| 1.5203 | 4000 | 0.0616 |
| 1.5393 | 4050 | 0.0619 |
| 1.5583 | 4100 | 0.0574 |
| 1.5773 | 4150 | 0.0635 |
| 1.5964 | 4200 | 0.0638 |
| 1.6154 | 4250 | 0.0564 |
| 1.6344 | 4300 | 0.0613 |
| 1.6534 | 4350 | 0.0636 |
| 1.6724 | 4400 | 0.064 |
| 1.6914 | 4450 | 0.0919 |
| 1.7104 | 4500 | 0.0576 |
| 1.7294 | 4550 | 0.0356 |
| 1.7484 | 4600 | 0.0719 |
| 1.7674 | 4650 | 0.0633 |
| 1.7864 | 4700 | 0.0504 |
| 1.8054 | 4750 | 0.0832 |
| 1.8244 | 4800 | 0.0571 |
| 1.8434 | 4850 | 0.0491 |
| 1.8624 | 4900 | 0.0388 |
| 1.8814 | 4950 | 0.0678 |
| 1.9004 | 5000 | 0.0518 |
| 1.9194 | 5050 | 0.0847 |
| 1.9384 | 5100 | 0.0556 |
| 1.9574 | 5150 | 0.0487 |
| 1.9764 | 5200 | 0.0661 |
| 1.9954 | 5250 | 0.0602 |
| 2.0144 | 5300 | 0.0426 |
| 2.0334 | 5350 | 0.0489 |
| 2.0525 | 5400 | 0.0425 |
| 2.0715 | 5450 | 0.0425 |
| 2.0905 | 5500 | 0.0431 |
| 2.1095 | 5550 | 0.0494 |
| 2.1285 | 5600 | 0.0425 |
| 2.1475 | 5650 | 0.0426 |
| 2.1665 | 5700 | 0.0255 |
| 2.1855 | 5750 | 0.0571 |
| 2.2045 | 5800 | 0.0448 |
| 2.2235 | 5850 | 0.0448 |
| 2.2425 | 5900 | 0.0393 |
| 2.2615 | 5950 | 0.0491 |
| 2.2805 | 6000 | 0.0554 |
| 2.2995 | 6050 | 0.043 |
| 2.3185 | 6100 | 0.0455 |
| 2.3375 | 6150 | 0.0479 |
| 2.3565 | 6200 | 0.0376 |
| 2.3755 | 6250 | 0.0446 |
| 2.3945 | 6300 | 0.04 |
| 2.4135 | 6350 | 0.0456 |
| 2.4325 | 6400 | 0.033 |
| 2.4515 | 6450 | 0.0392 |
| 2.4705 | 6500 | 0.0416 |
| 2.4895 | 6550 | 0.0349 |
| 2.5086 | 6600 | 0.0358 |
| 2.5276 | 6650 | 0.039 |
| 2.5466 | 6700 | 0.0366 |
| 2.5656 | 6750 | 0.0338 |
| 2.5846 | 6800 | 0.04 |
| 2.6036 | 6850 | 0.0462 |
| 2.6226 | 6900 | 0.0356 |
| 2.6416 | 6950 | 0.0377 |
| 2.6606 | 7000 | 0.0321 |
| 2.6796 | 7050 | 0.0429 |
| 2.6986 | 7100 | 0.0313 |
| 2.7176 | 7150 | 0.0549 |
| 2.7366 | 7200 | 0.0326 |
| 2.7556 | 7250 | 0.0571 |
| 2.7746 | 7300 | 0.0366 |
| 2.7936 | 7350 | 0.0439 |
| 2.8126 | 7400 | 0.054 |
| 2.8316 | 7450 | 0.0446 |
| 2.8506 | 7500 | 0.049 |
| 2.8696 | 7550 | 0.0407 |
| 2.8886 | 7600 | 0.0268 |
| 2.9076 | 7650 | 0.0394 |
| 2.9266 | 7700 | 0.034 |
| 2.9456 | 7750 | 0.0482 |
| 2.9647 | 7800 | 0.0395 |
| 2.9837 | 7850 | 0.0437 |
| 3.0027 | 7900 | 0.0545 |
| 3.0217 | 7950 | 0.0349 |
| 3.0407 | 8000 | 0.0385 |
| 3.0597 | 8050 | 0.0186 |
| 3.0787 | 8100 | 0.0198 |
| 3.0977 | 8150 | 0.0288 |
| 3.1167 | 8200 | 0.0369 |
| 3.1357 | 8250 | 0.0287 |
| 3.1547 | 8300 | 0.0353 |
| 3.1737 | 8350 | 0.0187 |
| 3.1927 | 8400 | 0.031 |
| 3.2117 | 8450 | 0.027 |
| 3.2307 | 8500 | 0.0228 |
| 3.2497 | 8550 | 0.0247 |
| 3.2687 | 8600 | 0.0351 |
| 3.2877 | 8650 | 0.0247 |
| 3.3067 | 8700 | 0.0245 |
| 3.3257 | 8750 | 0.0232 |
| 3.3447 | 8800 | 0.028 |
| 3.3637 | 8850 | 0.038 |
| 3.3827 | 8900 | 0.0278 |
| 3.4017 | 8950 | 0.0293 |
| 3.4208 | 9000 | 0.0299 |
| 3.4398 | 9050 | 0.0267 |
| 3.4588 | 9100 | 0.0413 |
| 3.4778 | 9150 | 0.032 |
| 3.4968 | 9200 | 0.0239 |
| 3.5158 | 9250 | 0.0313 |
| 3.5348 | 9300 | 0.0211 |
| 3.5538 | 9350 | 0.0272 |
| 3.5728 | 9400 | 0.0228 |
| 3.5918 | 9450 | 0.0245 |
| 3.6108 | 9500 | 0.0332 |
| 3.6298 | 9550 | 0.032 |
| 3.6488 | 9600 | 0.0292 |
| 3.6678 | 9650 | 0.0273 |
| 3.6868 | 9700 | 0.0332 |
| 3.7058 | 9750 | 0.0287 |
| 3.7248 | 9800 | 0.0326 |
| 3.7438 | 9850 | 0.0175 |
| 3.7628 | 9900 | 0.028 |
| 3.7818 | 9950 | 0.0291 |
| 3.8008 | 10000 | 0.0272 |
| 3.8198 | 10050 | 0.0198 |
| 3.8388 | 10100 | 0.0336 |
| 3.8578 | 10150 | 0.0243 |
| 3.8769 | 10200 | 0.0265 |
| 3.8959 | 10250 | 0.0429 |
| 3.9149 | 10300 | 0.0282 |
| 3.9339 | 10350 | 0.0266 |
| 3.9529 | 10400 | 0.0235 |
| 3.9719 | 10450 | 0.02 |
| 3.9909 | 10500 | 0.0371 |
| 4.0099 | 10550 | 0.0295 |
| 4.0289 | 10600 | 0.0144 |
| 4.0479 | 10650 | 0.027 |
| 4.0669 | 10700 | 0.0154 |
| 4.0859 | 10750 | 0.0237 |
| 4.1049 | 10800 | 0.0153 |
| 4.1239 | 10850 | 0.0124 |
| 4.1429 | 10900 | 0.0261 |
| 4.1619 | 10950 | 0.0246 |
| 4.1809 | 11000 | 0.0212 |
| 4.1999 | 11050 | 0.0217 |
| 4.2189 | 11100 | 0.0207 |
| 4.2379 | 11150 | 0.0159 |
| 4.2569 | 11200 | 0.0175 |
| 4.2759 | 11250 | 0.0155 |
| 4.2949 | 11300 | 0.0113 |
| 4.3139 | 11350 | 0.0339 |
| 4.3330 | 11400 | 0.0082 |
| 4.3520 | 11450 | 0.0166 |
| 4.3710 | 11500 | 0.0151 |
| 4.3900 | 11550 | 0.0185 |
| 4.4090 | 11600 | 0.02 |
| 4.4280 | 11650 | 0.0283 |
| 4.4470 | 11700 | 0.0198 |
| 4.4660 | 11750 | 0.0168 |
| 4.4850 | 11800 | 0.0161 |
| 4.5040 | 11850 | 0.0209 |
| 4.5230 | 11900 | 0.0124 |
| 4.5420 | 11950 | 0.0359 |
| 4.5610 | 12000 | 0.0197 |
| 4.5800 | 12050 | 0.0161 |
| 4.5990 | 12100 | 0.0139 |
| 4.6180 | 12150 | 0.0168 |
| 4.6370 | 12200 | 0.0158 |
| 4.6560 | 12250 | 0.0176 |
| 4.6750 | 12300 | 0.0189 |
| 4.6940 | 12350 | 0.0196 |
| 4.7130 | 12400 | 0.0236 |
| 4.7320 | 12450 | 0.0187 |
| 4.7510 | 12500 | 0.0166 |
| 4.7700 | 12550 | 0.0212 |
| 4.7891 | 12600 | 0.0176 |
| 4.8081 | 12650 | 0.0157 |
| 4.8271 | 12700 | 0.0136 |
| 4.8461 | 12750 | 0.0176 |
| 4.8651 | 12800 | 0.0198 |
| 4.8841 | 12850 | 0.0205 |
| 4.9031 | 12900 | 0.0199 |
| 4.9221 | 12950 | 0.0165 |
| 4.9411 | 13000 | 0.02 |
| 4.9601 | 13050 | 0.0157 |
| 4.9791 | 13100 | 0.0126 |
| 4.9981 | 13150 | 0.0154 |
</details>
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Accelerate: 0.29.3
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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| null |
Non_BioNLP
|
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision babcf60cae0a1f438d7ade582983d4ba462303c2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("comet24082002/ft_bge_newLaw_OnlineContrastiveLoss_V1_5epochs")
# Run inference
sentences = [
'Tàu cá không thực hiện đánh dấu theo quy định có thể bị xử phạt như thế nào?',
'Vi phạm quy định về đánh dấu tàu cá\n1. Phạt tiền từ 3.000.000 đồng đến 5.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 12 mét đến dưới 15 mét để khai thác thủy sản.\n2. Phạt tiền từ 5.000.000 đồng đến 7.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 15 mét đến dưới 24 mét để khai thác thủy sản.\n3. Phạt tiền từ 7.000.000 đồng đến 10.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 24 mét trở lên để khai thác thủy sản.',
'Đại hội toàn thể hội viên và Đại hội bất thường\n...\n2. Nhiệm vụ chính của Đại hội:\na) Thảo luận, thông qua các báo cáo hoạt động của nhiệm kỳ trước, đề ra phương hướng hoạt động nhiệm kỳ mới của Hiệp hội;\nb) Thông qua những điểm bổ sung hoặc sửa đổi Điều lệ của Hiệp hội;\nc) Thảo luận và quyết định một số vấn đề quan trọng của Hiệp hội vượt quá thẩm quyền giải quyết của Ban Chấp hành Hiệp hội;\nd) Bầu Ban Chấp hành Hiệp hội và Ban Kiểm tra Hiệp hội;\nđ) Thảo luận, phê duyệt quyết toán tài chính và thông qua kế hoạch tài chính khóa mới;\ne) Thảo luận, thông qua các vấn đề khác theo quy định của Điều lệ Hiệp hội.\n...',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10,524 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 8 tokens</li><li>mean: 24.17 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 282.93 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Nhân viên kiểm định của tổ chức kiểm định được chỉ định có bắt buộc phải có bằng đại học mới được cấp thẻ kiểm định viên đo lường không?</code> | <code>Yêu cầu đối với nhân viên kiểm định để được chứng nhận, cấp thẻ kiểm định viên đo lường<br>Nhân viên kiểm định của tổ chức kiểm định được chỉ định đáp ứng các yêu cầu sau đây được chứng nhận, cấp thẻ kiểm định viên đo lường:<br>1. Tốt nghiệp trung cấp hoặc tương đương trở lên.<br>2. Hoàn thành khóa đào tạo về kiểm định do Tổng cục tổ chức thực hiện tương ứng với lĩnh vực được chỉ định.<br>3. Có ít nhất mười hai (12) tháng kinh nghiệm hoạt động kiểm định, hiệu chuẩn, thử nghiệm tương ứng với lĩnh vực được chỉ định.</code> | <code>1</code> |
| <code>Tái phạm hành vi công chứng, chứng thực văn bằng, chứng chỉ trái quy định thì Đảng viên sẽ bị xử lý thế nào?</code> | <code>Hết thời hạn chấp hành quyết định áp dụng các biện pháp xử lý hành chính<br>1. Khi người vi phạm đã chấp hành xong quyết định giáo dục tại xã, phường, thị trấn thì Chủ tịch Uỷ ban nhân dân cấp xã cấp giấy chứng nhận cho người đã chấp hành xong và gửi bản sao cho gia đình người đó.<br>2. Khi người vi phạm đã chấp hành xong quyết định đưa vào trường giáo dưỡng, đưa vào cơ sở giáo dục bắt buộc, đưa vào cơ sở cai nghiện bắt buộc thì Hiệu trưởng trường giáo dưỡng, Giám đốc cơ sở giáo dục bắt buộc, Giám đốc cơ sở cai nghiện bắt buộc cấp giấy chứng nhận cho người đã chấp hành xong và gửi bản sao cho gia đình người đó, Tòa án nhân dân cấp huyện nơi đã ra quyết định, cơ quan quản lý trường giáo dưỡng, cơ sở giáo dục bắt buộc, cơ sở cai nghiện bắt buộc, Uỷ ban nhân dân cấp xã nơi người đó cư trú.<br>3. Đối tượng không xác định được nơi cư trú là người chưa thành niên hoặc người ốm yếu không còn khả năng lao động thì sau khi hết hạn chấp hành biện pháp đưa vào trường giáo dưỡng, cơ sở giáo dục bắt buộc, cơ sở cai nghiện bắt buộc được đưa về cơ sở bảo trợ xã hội tại địa phương nơi trường giáo dưỡng, cơ sở giáo dục bắt buộc, cơ sở cai nghiện bắt buộc đóng trụ sở.</code> | <code>0</code> |
| <code>Quy định về các nguyên tắc quản lý hồ sơ vụ án trong hệ thống Tòa án nhân dân?</code> | <code>"Điều 4. Nguyên tắc quản lý <br>1. Việc quản lý hồ sơ vụ án trong hệ thống Tòa án nhân dân phải bảo đảm an toàn, nguyên vẹn và các nguyên tắc chung của hồ sơ nghiệp vụ; bảo đảm bí mật theo quy định của pháp luật và của Tòa án nhân dân tôi cao. <br>2. Việc quản lý hồ sơ vụ án được thực hiện tập trung, thông nhất, khoa học, đầy đủ, chặt chẽ, kịp thời, không để bị hư hỏng, thất lạc. 3. Việc quản lý hồ sơ vụ án được thực hiện liên tục từ khi đơn vị, cá nhân lập hô sơ, nhận bàn giao hô sơ cho đên khi chuyên giao hô sơ cho đơn vị, cá nhân khác giải quyêt hoặc nộp vào Lưu trữ cơ quan."</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 4
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0190 | 50 | 0.1151 |
| 0.0380 | 100 | 0.0891 |
| 0.0570 | 150 | 0.0665 |
| 0.0760 | 200 | 0.074 |
| 0.0950 | 250 | 0.0431 |
| 0.1140 | 300 | 0.0578 |
| 0.1330 | 350 | 0.0851 |
| 0.1520 | 400 | 0.0569 |
| 0.1710 | 450 | 0.0648 |
| 0.1900 | 500 | 0.0678 |
| 0.2090 | 550 | 0.0484 |
| 0.2281 | 600 | 0.039 |
| 0.2471 | 650 | 0.0437 |
| 0.2661 | 700 | 0.0666 |
| 0.2851 | 750 | 0.0658 |
| 0.3041 | 800 | 0.0549 |
| 0.3231 | 850 | 0.0546 |
| 0.3421 | 900 | 0.0796 |
| 0.3611 | 950 | 0.0638 |
| 0.3801 | 1000 | 0.074 |
| 0.3991 | 1050 | 0.0838 |
| 0.4181 | 1100 | 0.0719 |
| 0.4371 | 1150 | 0.0761 |
| 0.4561 | 1200 | 0.0595 |
| 0.4751 | 1250 | 0.0803 |
| 0.4941 | 1300 | 0.0734 |
| 0.5131 | 1350 | 0.0875 |
| 0.5321 | 1400 | 0.0755 |
| 0.5511 | 1450 | 0.0694 |
| 0.5701 | 1500 | 0.079 |
| 0.5891 | 1550 | 0.0871 |
| 0.6081 | 1600 | 0.071 |
| 0.6271 | 1650 | 0.0989 |
| 0.6461 | 1700 | 0.081 |
| 0.6651 | 1750 | 0.0705 |
| 0.6842 | 1800 | 0.0861 |
| 0.7032 | 1850 | 0.0859 |
| 0.7222 | 1900 | 0.0818 |
| 0.7412 | 1950 | 0.062 |
| 0.7602 | 2000 | 0.0796 |
| 0.7792 | 2050 | 0.0996 |
| 0.7982 | 2100 | 0.0772 |
| 0.8172 | 2150 | 0.0832 |
| 0.8362 | 2200 | 0.0818 |
| 0.8552 | 2250 | 0.0961 |
| 0.8742 | 2300 | 0.0784 |
| 0.8932 | 2350 | 0.0945 |
| 0.9122 | 2400 | 0.0608 |
| 0.9312 | 2450 | 0.075 |
| 0.9502 | 2500 | 0.0867 |
| 0.9692 | 2550 | 0.0902 |
| 0.9882 | 2600 | 0.0657 |
| 1.0072 | 2650 | 0.0616 |
| 1.0262 | 2700 | 0.093 |
| 1.0452 | 2750 | 0.0684 |
| 1.0642 | 2800 | 0.0632 |
| 1.0832 | 2850 | 0.0513 |
| 1.1022 | 2900 | 0.0471 |
| 1.1212 | 2950 | 0.0815 |
| 1.1403 | 3000 | 0.051 |
| 1.1593 | 3050 | 0.0741 |
| 1.1783 | 3100 | 0.0461 |
| 1.1973 | 3150 | 0.0585 |
| 1.2163 | 3200 | 0.0592 |
| 1.2353 | 3250 | 0.0403 |
| 1.2543 | 3300 | 0.072 |
| 1.2733 | 3350 | 0.0582 |
| 1.2923 | 3400 | 0.08 |
| 1.3113 | 3450 | 0.0708 |
| 1.3303 | 3500 | 0.0447 |
| 1.3493 | 3550 | 0.0607 |
| 1.3683 | 3600 | 0.0522 |
| 1.3873 | 3650 | 0.0731 |
| 1.4063 | 3700 | 0.0992 |
| 1.4253 | 3750 | 0.0582 |
| 1.4443 | 3800 | 0.068 |
| 1.4633 | 3850 | 0.0691 |
| 1.4823 | 3900 | 0.052 |
| 1.5013 | 3950 | 0.0632 |
| 1.5203 | 4000 | 0.0616 |
| 1.5393 | 4050 | 0.0619 |
| 1.5583 | 4100 | 0.0574 |
| 1.5773 | 4150 | 0.0635 |
| 1.5964 | 4200 | 0.0638 |
| 1.6154 | 4250 | 0.0564 |
| 1.6344 | 4300 | 0.0613 |
| 1.6534 | 4350 | 0.0636 |
| 1.6724 | 4400 | 0.064 |
| 1.6914 | 4450 | 0.0919 |
| 1.7104 | 4500 | 0.0576 |
| 1.7294 | 4550 | 0.0356 |
| 1.7484 | 4600 | 0.0719 |
| 1.7674 | 4650 | 0.0633 |
| 1.7864 | 4700 | 0.0504 |
| 1.8054 | 4750 | 0.0832 |
| 1.8244 | 4800 | 0.0571 |
| 1.8434 | 4850 | 0.0491 |
| 1.8624 | 4900 | 0.0388 |
| 1.8814 | 4950 | 0.0678 |
| 1.9004 | 5000 | 0.0518 |
| 1.9194 | 5050 | 0.0847 |
| 1.9384 | 5100 | 0.0556 |
| 1.9574 | 5150 | 0.0487 |
| 1.9764 | 5200 | 0.0661 |
| 1.9954 | 5250 | 0.0602 |
| 2.0144 | 5300 | 0.0426 |
| 2.0334 | 5350 | 0.0489 |
| 2.0525 | 5400 | 0.0425 |
| 2.0715 | 5450 | 0.0425 |
| 2.0905 | 5500 | 0.0431 |
| 2.1095 | 5550 | 0.0494 |
| 2.1285 | 5600 | 0.0425 |
| 2.1475 | 5650 | 0.0426 |
| 2.1665 | 5700 | 0.0255 |
| 2.1855 | 5750 | 0.0571 |
| 2.2045 | 5800 | 0.0448 |
| 2.2235 | 5850 | 0.0448 |
| 2.2425 | 5900 | 0.0393 |
| 2.2615 | 5950 | 0.0491 |
| 2.2805 | 6000 | 0.0554 |
| 2.2995 | 6050 | 0.043 |
| 2.3185 | 6100 | 0.0455 |
| 2.3375 | 6150 | 0.0479 |
| 2.3565 | 6200 | 0.0376 |
| 2.3755 | 6250 | 0.0446 |
| 2.3945 | 6300 | 0.04 |
| 2.4135 | 6350 | 0.0456 |
| 2.4325 | 6400 | 0.033 |
| 2.4515 | 6450 | 0.0392 |
| 2.4705 | 6500 | 0.0416 |
| 2.4895 | 6550 | 0.0349 |
| 2.5086 | 6600 | 0.0358 |
| 2.5276 | 6650 | 0.039 |
| 2.5466 | 6700 | 0.0366 |
| 2.5656 | 6750 | 0.0338 |
| 2.5846 | 6800 | 0.04 |
| 2.6036 | 6850 | 0.0462 |
| 2.6226 | 6900 | 0.0356 |
| 2.6416 | 6950 | 0.0377 |
| 2.6606 | 7000 | 0.0321 |
| 2.6796 | 7050 | 0.0429 |
| 2.6986 | 7100 | 0.0313 |
| 2.7176 | 7150 | 0.0549 |
| 2.7366 | 7200 | 0.0326 |
| 2.7556 | 7250 | 0.0571 |
| 2.7746 | 7300 | 0.0366 |
| 2.7936 | 7350 | 0.0439 |
| 2.8126 | 7400 | 0.054 |
| 2.8316 | 7450 | 0.0446 |
| 2.8506 | 7500 | 0.049 |
| 2.8696 | 7550 | 0.0407 |
| 2.8886 | 7600 | 0.0268 |
| 2.9076 | 7650 | 0.0394 |
| 2.9266 | 7700 | 0.034 |
| 2.9456 | 7750 | 0.0482 |
| 2.9647 | 7800 | 0.0395 |
| 2.9837 | 7850 | 0.0437 |
| 3.0027 | 7900 | 0.0545 |
| 3.0217 | 7950 | 0.0349 |
| 3.0407 | 8000 | 0.0385 |
| 3.0597 | 8050 | 0.0186 |
| 3.0787 | 8100 | 0.0198 |
| 3.0977 | 8150 | 0.0288 |
| 3.1167 | 8200 | 0.0369 |
| 3.1357 | 8250 | 0.0287 |
| 3.1547 | 8300 | 0.0353 |
| 3.1737 | 8350 | 0.0187 |
| 3.1927 | 8400 | 0.031 |
| 3.2117 | 8450 | 0.027 |
| 3.2307 | 8500 | 0.0228 |
| 3.2497 | 8550 | 0.0247 |
| 3.2687 | 8600 | 0.0351 |
| 3.2877 | 8650 | 0.0247 |
| 3.3067 | 8700 | 0.0245 |
| 3.3257 | 8750 | 0.0232 |
| 3.3447 | 8800 | 0.028 |
| 3.3637 | 8850 | 0.038 |
| 3.3827 | 8900 | 0.0278 |
| 3.4017 | 8950 | 0.0293 |
| 3.4208 | 9000 | 0.0299 |
| 3.4398 | 9050 | 0.0267 |
| 3.4588 | 9100 | 0.0413 |
| 3.4778 | 9150 | 0.032 |
| 3.4968 | 9200 | 0.0239 |
| 3.5158 | 9250 | 0.0313 |
| 3.5348 | 9300 | 0.0211 |
| 3.5538 | 9350 | 0.0272 |
| 3.5728 | 9400 | 0.0228 |
| 3.5918 | 9450 | 0.0245 |
| 3.6108 | 9500 | 0.0332 |
| 3.6298 | 9550 | 0.032 |
| 3.6488 | 9600 | 0.0292 |
| 3.6678 | 9650 | 0.0273 |
| 3.6868 | 9700 | 0.0332 |
| 3.7058 | 9750 | 0.0287 |
| 3.7248 | 9800 | 0.0326 |
| 3.7438 | 9850 | 0.0175 |
| 3.7628 | 9900 | 0.028 |
| 3.7818 | 9950 | 0.0291 |
| 3.8008 | 10000 | 0.0272 |
| 3.8198 | 10050 | 0.0198 |
| 3.8388 | 10100 | 0.0336 |
| 3.8578 | 10150 | 0.0243 |
| 3.8769 | 10200 | 0.0265 |
| 3.8959 | 10250 | 0.0429 |
| 3.9149 | 10300 | 0.0282 |
| 3.9339 | 10350 | 0.0266 |
| 3.9529 | 10400 | 0.0235 |
| 3.9719 | 10450 | 0.02 |
| 3.9909 | 10500 | 0.0371 |
| 4.0099 | 10550 | 0.0295 |
| 4.0289 | 10600 | 0.0144 |
| 4.0479 | 10650 | 0.027 |
| 4.0669 | 10700 | 0.0154 |
| 4.0859 | 10750 | 0.0237 |
| 4.1049 | 10800 | 0.0153 |
| 4.1239 | 10850 | 0.0124 |
| 4.1429 | 10900 | 0.0261 |
| 4.1619 | 10950 | 0.0246 |
| 4.1809 | 11000 | 0.0212 |
| 4.1999 | 11050 | 0.0217 |
| 4.2189 | 11100 | 0.0207 |
| 4.2379 | 11150 | 0.0159 |
| 4.2569 | 11200 | 0.0175 |
| 4.2759 | 11250 | 0.0155 |
| 4.2949 | 11300 | 0.0113 |
| 4.3139 | 11350 | 0.0339 |
| 4.3330 | 11400 | 0.0082 |
| 4.3520 | 11450 | 0.0166 |
| 4.3710 | 11500 | 0.0151 |
| 4.3900 | 11550 | 0.0185 |
| 4.4090 | 11600 | 0.02 |
| 4.4280 | 11650 | 0.0283 |
| 4.4470 | 11700 | 0.0198 |
| 4.4660 | 11750 | 0.0168 |
| 4.4850 | 11800 | 0.0161 |
| 4.5040 | 11850 | 0.0209 |
| 4.5230 | 11900 | 0.0124 |
| 4.5420 | 11950 | 0.0359 |
| 4.5610 | 12000 | 0.0197 |
| 4.5800 | 12050 | 0.0161 |
| 4.5990 | 12100 | 0.0139 |
| 4.6180 | 12150 | 0.0168 |
| 4.6370 | 12200 | 0.0158 |
| 4.6560 | 12250 | 0.0176 |
| 4.6750 | 12300 | 0.0189 |
| 4.6940 | 12350 | 0.0196 |
| 4.7130 | 12400 | 0.0236 |
| 4.7320 | 12450 | 0.0187 |
| 4.7510 | 12500 | 0.0166 |
| 4.7700 | 12550 | 0.0212 |
| 4.7891 | 12600 | 0.0176 |
| 4.8081 | 12650 | 0.0157 |
| 4.8271 | 12700 | 0.0136 |
| 4.8461 | 12750 | 0.0176 |
| 4.8651 | 12800 | 0.0198 |
| 4.8841 | 12850 | 0.0205 |
| 4.9031 | 12900 | 0.0199 |
| 4.9221 | 12950 | 0.0165 |
| 4.9411 | 13000 | 0.02 |
| 4.9601 | 13050 | 0.0157 |
| 4.9791 | 13100 | 0.0126 |
| 4.9981 | 13150 | 0.0154 |
</details>
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.39.3
- PyTorch: 2.1.2
- Accelerate: 0.29.3
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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{"base_model": "BAAI/bge-m3", "datasets": [], "language": [], "library_name": "sentence-transformers", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10524", "loss:OnlineContrastiveLoss"], "widget": [{"source_sentence": "Thời gian cấp giấy đăng ký kết hôn là bao lâu?", "sentences": ["\"Điều 4. Thủ tục, thẩm quyền giải quyết đề nghị cấp thẻ tạm trú\n1. Người nước ngoài thuộc diện được cấp thẻ tạm trú theo quy định tại khoản 2 Điều 36 của Luật phải thông qua cơ quan, tổ chức, cá nhân đã mời, bảo lãnh trực tiếp làm thủ tục tại Cục Quản lý xuất nhập cảnh hoặc Phòng Quản lý xuất nhập cảnh Công an tỉnh, thành phố trực thuộc trung ương nơi cơ quan, tổ chức mời, bảo lãnh đặt trụ sở hoặc nơi cá nhân mời, bảo lãnh cư trú.\n2. Hồ sơ đề nghị cấp thẻ tạm trú theo quy định tại khoản 1 Điều 37 của Luật. Văn bản đề nghị cấp thẻ tạm trú nêu tại điểm a khoản 1 Điều 37 của Luật theo mẫu do Bộ Công an ban hành tại Thông tư số 04/2015/TT-BCA ngày 05/01/2015 quy định mẫu giấy tờ liên quan đến việc nhập cảnh, xuất cảnh, cư trú của người nước ngoài tại Việt Nam gồm:\na) Mẫu NA6 và NA8 sử dụng cho cơ quan, tổ chức;\nb) Mẫu NA7 và NA8 sử dụng cho cá nhân.\n3. Giải quyết cấp thẻ tạm trú\nTrong thời hạn 05 ngày làm việc kể từ ngày nhận đủ hồ sơ, Cục Quản lý xuất nhập cảnh hoặc Phòng Quản lý xuất nhập cảnh Công an tỉnh, thành phố trực thuộc trung ương xem xét cấp thẻ tạm trú.\n4. Thời hạn của thẻ tạm trú được cấp phù hợp với mục đích nhập cảnh và đề nghị của cơ quan, tổ chức, cá nhân. Thẻ tạm trú có thời hạn từ 01 năm đến 05 năm nhưng ngắn hơn thời hạn của hộ chiếu ít nhất 30 ngày.\"", "Nội dung hợp đồng xây dựng\n1. Hợp đồng xây dựng gồm các nội dung sau:\na) Căn cứ pháp lý áp dụng;\nb) Ngôn ngữ áp dụng;\nc) Nội dung và khối lượng công việc;\nd) Chất lượng, yêu cầu kỹ thuật của công việc, nghiệm thu và bàn giao;\nđ) Thời gian và tiến độ thực hiện hợp đồng;\ne) Giá hợp đồng, tạm ứng, đồng tiền sử dụng trong thanh toán và thanh toán hợp đồng xây dựng;\ng) Bảo đảm thực hiện hợp đồng, bảo lãnh tạm ứng hợp đồng;\nh) Điều chỉnh hợp đồng xây dựng;\ni) Quyền và nghĩa vụ của các bên tham gia hợp đồng xây dựng;\nk) Trách nhiệm do vi phạm hợp đồng, thưởng và phạt vi phạm hợp đồng;\nl) Tạm ngừng và chấm dứt hợp đồng xây dựng;\nm) Giải quyết tranh chấp hợp đồng xây dựng;\nn) Rủi ro và bất khả kháng;\no) Quyết toán và thanh lý hợp đồng xây dựng;\np) Các nội dung khác.\n2. Đối với hợp đồng tổng thầu xây dựng ngoài các nội dung quy định tại khoản 1 Điều này còn phải được bổ sung về nội dung và trách nhiệm quản lý của tổng thầu xây dựng.\n3. Chính phủ quy định chi tiết về hợp đồng xây dựng.", "Nội dung vi phạm và hình thức xử lý kỷ luật đối với đoàn viên\n1- Đoàn viên vi phạm một trong các trường hợp sau đây thì bị xử lý kỷ luật bằng hình thức khiển trách:\na) Không chấp hành và không thực hiện Điều lệ Công đoàn Việt Nam, hoặc nghị quyết, quyết định, quy định, kế hoạch, chương trình công tác... của công đoàn các cấp gây hậu quả ít nghiêm trọng.\nb) Không dự họp 50% trở lên số kỳ họp công đoàn trong một năm.\nc) Không đóng đoàn phí liên tục 6 tháng mà không có lý do chính đáng.\n2- Đoàn viên vi phạm một trong các trường hợp sau đây thì bị xử lý kỷ luật bằng hình thức cảnh cáo:\na) Đã bị xử lý kỷ luật theo quy định tại tiết a khoản 1 Điều này mà tái phạm hoặc vi phạm lần đầu gây hậu quả nghiêm trọng.\nb) Không dự hợp 70% trở lên số kỳ họp công đoàn trong một năm.\nc) Không đóng đoàn phí liên tục 9 tháng mà không có lý do chính đáng.\n3- Đoàn viên vi phạm một trong các trường hợp sau đây thì bị xử lý kỷ luật bằng hình thức khai trừ:\na) Đã bị xử lý kỷ luật theo quy định tại tiết a khoản 2 Điều này mà tái phạm hoặc vi phạm lần đầu gây hậu quả rất nghiêm trọng.\nb) Không dự họp 90% trở lên số kỳ họp công đoàn trong một năm.\nc) Không đóng đoàn phí liên tục 12 tháng mà không có lý do chính đáng."]}, {"source_sentence": "Hội đồng thảo luận, trao đổi và biểu quyết để tuyển chọn Thẩm phán trung cấp Tòa án quân sự quân khu như thế nào?", "sentences": ["Quy định về việc sử dụng phần sở hữu chung của nhà chung cư\n1. Sử dụng thang máy và các thiết bị sử dụng chung theo đúng mục đích, công năng thiết kế sử dụng.\n2. Không được làm hư hỏng hoặc có hành vi vi phạm đến tài sản chung của nhà chung cư.\n3. Tuân thủ đầy đủ các quy định về việc dừng, đỗ xe tại nơi được dừng, đỗ xe theo quy định.\n4. Sử dụng nhà sinh hoạt cộng đồng vào đúng mục đích, công năng theo quy định của pháp Luật về nhà ở.\n5. Tuân thủ đầy đủ các quy định về an toàn phòng cháy, chữa cháy của nhà chung cư.\n6. Các quy định khác: do Hội nghị nhà chung cư quy định thêm cho phù hợp với từng nhà chung cư (nếu có).", "Tiêu chuẩn đối với kiểm toán viên hành nghề được chấp thuận\nNgoài các tiêu chuẩn quy định tại Luật kiểm toán độc lập, kiểm toán viên hành nghề được chấp thuận phải có các tiêu chuẩn sau:\n1. Có tên trong danh sách kiểm toán viên đủ Điều kiện hành nghề kiểm toán trong kỳ chấp thuận được Bộ Tài chính công khai tại thời Điểm nộp hồ sơ đăng ký thực hiện kiểm toán.\n2. Có ít nhất 24 tháng thực tế hành nghề kiểm toán tại Việt Nam tính từ ngày được cơ quan có thẩm quyền xác nhận đủ Điều kiện hành nghề kiểm toán đến ngày nộp hồ sơ đăng ký thực hiện kiểm toán.", "Việc báo cáo với Hội đồng về hồ sơ của người được đề nghị tuyển chọn và bổ nhiệm Thẩm phán theo danh sách trích ngang đã lập được thực hiện đối với từng Tòa án một và đối với từng người một.\nHội đồng thảo luận, trao đổi và biểu quyết đối với từng người một."]}, {"source_sentence": "Toà án phải gửi quyết định áp dụng biện pháp xử lý hành chính đưa vào trường giáo dưỡng cho người đề nghị trong thời gian bao lâu từ ngày ra quyết định?", "sentences": ["Tiêu chuẩn cá nhân biên soạn sách giáo khoa\n1. Người biên soạn sách giáo khoa phải đáp ứng các tiêu chuẩn sau:\na) Có trình độ được đào tạo từ đại học trở lên theo chuyên ngành phù hợp với môn học, hoạt động giáo dục có sách giáo khoa được biên soạn; am hiểu về khoa học giáo dục; có ít nhất 03 (ba) năm trực tiếp giảng dạy hoặc nghiên cứu về chuyên môn phù hợp với môn học, hoạt động giáo dục có sách giáo khoa được biên soạn;\nb) Là công dân Việt Nam, có phẩm chất đạo đức, tư tưởng tốt.\n2. Người tham gia biên soạn sách giáo khoa không tham gia thẩm định sách giáo khoa.", "Quy trình bổ nhiệm lại\n1. Chậm nhất 02 tháng trước ngày hết nhiệm kỳ, Hòa giải viên có nguyện vọng tiếp tục làm Hòa giải viên, nộp hồ sơ đề nghị bổ nhiệm lại tại Tòa án nơi Hòa giải viên làm việc.\n2. Căn cứ nhu cầu thực tế, trên cơ sở kết quả thực hiện nhiệm vụ của Hòa giải viên, Tòa án nơi có Hòa giải viên làm việc có văn bản đề nghị Chánh án Tòa án nhân dân cấp tỉnh xem xét bổ nhiệm lại theo quy định.\n3. Quy trình bổ nhiệm lại Hòa giải viên được thực hiện như quy định tại khoản 3, khoản 4 và khoản 5 Điều 6 Thông tư này.\n4. Quyết định bổ nhiệm lại Hòa giải viên phải được ban hành trước ít nhất 01 ngày làm việc, tính đến ngày hết nhiệm kỳ làm Hòa giải viên. Trường hợp không đáp ứng điều kiện để bổ nhiệm lại, Chánh án Tòa án nhân dân cấp tỉnh phải thông báo bằng văn bản và nêu rõ lý do.", "\"Điều 13. Điều kiện cấp Chứng chỉ hành nghề dược\n1. Có văn bằng, chứng chỉ, giấy chứng nhận chuyên môn (sau đây gọi chung là văn bằng chuyên môn) được cấp hoặc công nhận tại Việt Nam phù hợp với vị trí công việc và cơ sở kinh doanh dược bao gồm:\na) Bằng tốt nghiệp đại học ngành dược (sau đây gọi là Bằng dược sỹ);\nb) Bằng tốt nghiệp đại học ngành y đa khoa;\nc) Bằng tốt nghiệp đại học ngành y học cổ truyền hoặc đại học ngành dược cổ truyền;\nd) Bằng tốt nghiệp đại học ngành sinh học;\nđ) Bằng tốt nghiệp đại học ngành hóa học;\ne) Bằng tốt nghiệp cao đẳng ngành dược;\ng) Bằng tốt nghiệp trung cấp ngành dược;\nh) Bằng tốt nghiệp cao đẳng, trung cấp ngành y;\ni) Bằng tốt nghiệp trung cấp y học cổ truyền hoặc dược cổ truyền;\nk) Văn bằng, chứng chỉ sơ cấp dược;\nl) Giấy chứng nhận về lương y, giấy chứng nhận về lương dược, giấy chứng nhận bài thuốc gia truyền hoặc văn bằng, chứng chỉ, giấy chứng nhận khác về y dược cổ truyền được cấp trước ngày Luật này có hiệu lực.\nViệc áp dụng Điều kiện về văn bằng, chứng chỉ, giấy chứng nhận quy định tại Điểm l Khoản này do Bộ trưởng Bộ Y tế quy định phù hợp với Điều kiện phát triển kinh tế - xã hội và nhu cầu khám bệnh, chữa bệnh của Nhân dân ở từng địa phương trong từng thời kỳ.\n2. Có thời gian thực hành tại cơ sở kinh doanh dược, bộ phận dược của cơ sở khám bệnh, chữa bệnh, trường đào tạo chuyên ngành dược, cơ sở nghiên cứu dược, cơ sở kiểm nghiệm thuốc, nguyên liệu làm thuốc, cơ quan quản lý về dược hoặc văn phòng đại diện của thương nhân nước ngoài hoạt động trong lĩnh vực dược tại Việt Nam (sau đây gọi chung là cơ sở dược); cơ sở khám bệnh, chữa bệnh phù hợp với chuyên môn của người hành nghề theo quy định sau đây:\na) Đối với người bị thu hồi Chứng chỉ hành nghề dược theo quy định tại Khoản 9 Điều 28 của Luật này thì không yêu cầu thời gian thực hành nhưng phải cập nhật kiến thức chuyên môn về dược;\nb) Đối với người có trình độ chuyên khoa sau đại học phù hợp với phạm vi hành nghề thì được giảm thời gian thực hành theo quy định của Chính phủ;\nc) Đối với người có văn bằng chuyên môn quy định tại Điểm l Khoản 1 Điều 13 của Luật này thì thời gian thực hành theo quy định của Bộ trưởng Bộ Y tế.\n3. Có giấy chứng nhận đủ sức khỏe để hành nghề dược do cơ sở y tế có thẩm quyền cấp.\n4. Không thuộc một trong các trường hợp sau đây:\na) Đang bị truy cứu trách nhiệm hình sự, đang chấp hành bản án, quyết định của Tòa án; trong thời gian bị cấm hành nghề, cấm làm công việc liên quan đến hoạt động dược theo bản án, quyết định của Tòa án;\nb) Bị hạn chế năng lực hành vi dân sự.\n5. Đối với người tự nguyện xin cấp Chứng chỉ hành nghề dược theo hình thức thi, phải đáp ứng đủ Điều kiện theo quy định tại Điều này.\""]}, {"source_sentence": "Doanh nghiệp nhỏ và vừa tham gia chuỗi giá trị sản xuất, chế biến có được hỗ trợ chi phí đào tạo hay không?", "sentences": ["Nội dung hỗ trợ doanh nghiệp nhỏ và vừa tham gia cụm liên kết ngành, chuỗi giá trị\n1. Hỗ trợ đào tạo\na) Hỗ trợ tối đa 50% chi phí tổ chức khóa đào tạo nâng cao trình độ công nghệ, kỹ thuật sản xuất chuyên sâu tại doanh nghiệp nhưng không quá 50 triệu đồng/khoá/năm/doanh nghiệp;\nb) Hỗ trợ tối đa 50% chi phí đào tạo cho học viên của doanh nghiệp khi tham gia các khóa đào tạo chuyên sâu theo nhu cầu phát triển của ngành, chuỗi giá trị nhưng không quá 10 triệu đồng/học viên/năm và không quá 03 học viên/doanh nghiệp/năm.\n2. Hỗ trợ nâng cao năng lực liên kết sản xuất và kinh doanh\na) Hỗ trợ 100% giá trị hợp đồng tư vấn đánh giá toàn diện năng lực của doanh nghiệp nhỏ và vừa trong cụm liên kết ngành, chuỗi giá trị nhưng không quá 30 triệu đồng/hợp đồng/năm/doanh nghiệp;\nb) Hỗ trợ 100% giá trị hợp đồng tư vấn cải tiến, nâng cấp kỹ thuật chuyên sâu cho doanh nghiệp nhỏ và vừa nhằm cải thiện năng lực sản xuất, đáp ứng yêu cầu kết nối, trở thành nhà cung cấp của doanh nghiệp đầu chuỗi nhưng không quá 100 triệu đồng/hợp đồng/năm/doanh nghiệp.\n...", "Chế độ tập sự\n...\n4. Trong thời gian thực hiện chế độ tập sự, đơn vị sự nghiệp công lập quản lý, sử dụng viên chức phải cử viên chức tham gia khóa đào tạo, bồi dưỡng để hoàn chỉnh tiêu chuẩn, điều kiện của chức danh nghề nghiệp trước khi bổ nhiệm. Thời gian tham gia khóa đào tạo, bồi dưỡng được tính vào thời gian thực hiện chế độ tập sự.\n5. Không thực hiện chế độ tập sự đối với các trường hợp đã có thời gian công tác có đóng bảo hiểm xã hội bắt buộc theo đúng quy định của Luật Bảo hiểm xã hội, được bố trí làm việc theo đúng ngành, nghề đào tạo hoặc theo đúng chuyên môn nghiệp vụ trước đây đã đảm nhiệm mà thời gian công tác có đóng bảo hiểm xã hội (nếu đứt quãng thì được cộng dồn) bằng hoặc lớn hơn thời gian tập sự tương ứng với thời gian tập sự của chức danh nghề nghiệp được tuyển dụng quy định tại khoản 2 Điều này. Đối với các trường hợp không thực hiện chế độ tập sự, người đứng đầu đơn vị sự nghiệp công lập phải cử viên chức tham gia khóa bồi dưỡng để hoàn chỉnh tiêu chuẩn, điều kiện của chức danh nghề nghiệp viên chức trước khi bổ nhiệm.\n...", "\"Điều 6. Nguyên tắc xếp lương và phụ cấp\n1. Việc xếp lương, nâng bậc lương và áp dụng các chế độ phụ cấp thực hiện theo quy định chung do Chính phủ ban hành.\n2. Đối với các chức danh lãnh đạo (bầu cử, bổ nhiệm) chuyên trách Hội Cựu chiến binh Việt Nam từ Trung ương đến cấp huyện, ngoài lương hưu, hàng tháng được hưởng 90% mức lương chuẩn và phụ cấp chức vụ (không phải đóng bảo hiểm xã hội và bảo hiểm y tế).\nĐối với cựu chiến binh thuộc chỉ tiêu biên chế được duyệt làm công tác chuyên môn, nghiệp vụ từ Trung ương đến cấp huyện, ngoài lương hưu, hàng tháng được hưởng cao nhất 90% mức lương chuẩn của cấp phó lãnh đạo trực tiếp cùng cấp (không phải đóng bảo hiểm xã hội và bảo hiểm y tế).\""]}, {"source_sentence": "Tàu cá không thực hiện đánh dấu theo quy định có thể bị xử phạt như thế nào?", "sentences": ["Vi phạm quy định về đánh dấu tàu cá\n1. Phạt tiền từ 3.000.000 đồng đến 5.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 12 mét đến dưới 15 mét để khai thác thủy sản.\n2. Phạt tiền từ 5.000.000 đồng đến 7.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 15 mét đến dưới 24 mét để khai thác thủy sản.\n3. Phạt tiền từ 7.000.000 đồng đến 10.000.000 đồng đối với hành vi không đánh dấu nhận biết tàu cá hoặc đánh dấu sai quy định trong trường hợp sử dụng tàu cá có chiều dài lớn nhất từ 24 mét trở lên để khai thác thủy sản.", "Đại hội toàn thể hội viên và Đại hội bất thường\n...\n2. Nhiệm vụ chính của Đại hội:\na) Thảo luận, thông qua các báo cáo hoạt động của nhiệm kỳ trước, đề ra phương hướng hoạt động nhiệm kỳ mới của Hiệp hội;\nb) Thông qua những điểm bổ sung hoặc sửa đổi Điều lệ của Hiệp hội;\nc) Thảo luận và quyết định một số vấn đề quan trọng của Hiệp hội vượt quá thẩm quyền giải quyết của Ban Chấp hành Hiệp hội;\nd) Bầu Ban Chấp hành Hiệp hội và Ban Kiểm tra Hiệp hội;\nđ) Thảo luận, phê duyệt quyết toán tài chính và thông qua kế hoạch tài chính khóa mới;\ne) Thảo luận, thông qua các vấn đề khác theo quy định của Điều lệ Hiệp hội.\n...", "Đối tượng được thăm gặp phạm nhân\n1. Đối tượng được thăm gặp phạm nhân gồm: Ông, bà nội; ông, bà ngoại; bố, mẹ đẻ; bố, mẹ vợ (hoặc chồng); bố, mẹ nuôi hợp pháp; vợ hoặc chồng; con đẻ, con dâu, con rể, con nuôi hợp pháp; anh, chị, em ruột; anh, chị, em dâu, rể; anh, chị em vợ (hoặc chồng); cô, dì, chú, bác, cậu, cháu ruột, số lượng thân nhân mỗi lần đến thăm gặp phạm nhân không quá 03 người.\n2. Đại diện cơ quan, tổ chức hoặc cá nhân khác ngoài những người được quy định tại Khoản 1 Điều này được thăm gặp phạm nhân nếu Thủ trưởng cơ sở giam giữ xét thấy phù hợp với yêu cầu công tác phòng, chống tội phạm và công tác quản lý, giáo dục cải tạo phạm nhân."]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,938 |
mulinski/mt5-small-finetuned-amazon-en-es
|
mulinski
|
summarization
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-18T17:39:25Z |
2023-07-18T19:21:05+00:00
| 28 | 0 |
---
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0340
- Rouge1: 17.354
- Rouge2: 8.4787
- Rougel: 17.1305
- Rougelsum: 17.0075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 7.0197 | 1.0 | 1209 | 3.3037 | 13.683 | 5.3875 | 13.0828 | 13.1122 |
| 3.9145 | 2.0 | 2418 | 3.1418 | 15.5264 | 7.4742 | 14.8131 | 14.7471 |
| 3.5987 | 3.0 | 3627 | 3.0970 | 17.4004 | 8.5468 | 16.8991 | 16.8763 |
| 3.4274 | 4.0 | 4836 | 3.0672 | 16.7503 | 7.9732 | 16.2399 | 16.1352 |
| 3.3241 | 5.0 | 6045 | 3.0648 | 16.6407 | 8.1366 | 16.4552 | 16.3217 |
| 3.2468 | 6.0 | 7254 | 3.0444 | 17.2806 | 8.6183 | 17.0437 | 16.8567 |
| 3.2116 | 7.0 | 8463 | 3.0370 | 17.6282 | 8.6565 | 17.2977 | 17.2007 |
| 3.1821 | 8.0 | 9672 | 3.0340 | 17.354 | 8.4787 | 17.1305 | 17.0075 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0340
- Rouge1: 17.354
- Rouge2: 8.4787
- Rougel: 17.1305
- Rougelsum: 17.0075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 7.0197 | 1.0 | 1209 | 3.3037 | 13.683 | 5.3875 | 13.0828 | 13.1122 |
| 3.9145 | 2.0 | 2418 | 3.1418 | 15.5264 | 7.4742 | 14.8131 | 14.7471 |
| 3.5987 | 3.0 | 3627 | 3.0970 | 17.4004 | 8.5468 | 16.8991 | 16.8763 |
| 3.4274 | 4.0 | 4836 | 3.0672 | 16.7503 | 7.9732 | 16.2399 | 16.1352 |
| 3.3241 | 5.0 | 6045 | 3.0648 | 16.6407 | 8.1366 | 16.4552 | 16.3217 |
| 3.2468 | 6.0 | 7254 | 3.0444 | 17.2806 | 8.6183 | 17.0437 | 16.8567 |
| 3.2116 | 7.0 | 8463 | 3.0370 | 17.6282 | 8.6565 | 17.2977 | 17.2007 |
| 3.1821 | 8.0 | 9672 | 3.0340 | 17.354 | 8.4787 | 17.1305 | 17.0075 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
{"license": "apache-2.0", "metrics": ["rouge"], "tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]}
|
task
|
[
"SUMMARIZATION"
] | 41,939 |
hopkins/mbart-finetuned-eng-kor-129322430666
|
hopkins
|
translation
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-02T17:43:16Z |
2023-07-02T17:58:38+00:00
| 8 | 0 |
---
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: mbart-finetuned-eng-kor-129322430666
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-finetuned-eng-kor-129322430666
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9884
- Bleu: 7.1402
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-finetuned-eng-kor-129322430666
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9884
- Bleu: 7.1402
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
{"metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "mbart-finetuned-eng-kor-129322430666", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 41,940 |
l3cube-pune/marathi-mixed-ner-iob
|
l3cube-pune
|
token-classification
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"mr",
"dataset:L3Cube-MahaSocialNER",
"dataset:L3Cube-MahaNER",
"arxiv:2401.00170",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-05T11:40:06Z |
2024-01-03T10:02:55+00:00
| 16 | 0 |
---
datasets:
- L3Cube-MahaSocialNER
- L3Cube-MahaNER
language: mr
license: cc-by-4.0
widget:
- text: सोलापूर जिल्ह्यातील माळशिरस तालुक्यात जाधववाडी येथे विजय गंगाप्रकल्पाचा शुभारंभ
केला हे जलसंधारणाचे एकूण 28 किमीचे 3 टप्प्यात काम होत असून पहिल्या 10 किमीचे काम
पूर्ण झाले आहे
---
## MahaMixedNER-BERT-IOB
MahaMixedNER-BERT-IOB is a MahaBERT(<a href="https://huggingface.co/l3cube-pune/marathi-bert-v2">l3cube-pune/marathi-bert-v2</a>) model fine-tuned on L3Cube-MahaNER + MahaSocialNER (with IOB tags) - a Marathi named entity recognition datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2401.00170)
Non IOB Model : <a href="https://huggingface.co/l3cube-pune/marathi-mixed-ner"> marathi-mixed-ner </a>
Citing:
```
@article{chaudhari2023l3cube,
title={L3Cube-MahaSocialNER: A Social Media based Marathi NER Dataset and BERT models},
author={Chaudhari, Harsh and Patil, Anuja and Lavekar, Dhanashree and Khairnar, Pranav and Joshi, Raviraj},
journal={arXiv preprint arXiv:2401.00170},
year={2023}
}
```
```
@InProceedings{litake-EtAl:2022:WILDRE6,
author = {Litake, Onkar and Sabane, Maithili Ravindra and Patil, Parth Sachin and Ranade, Aparna Abhijeet and Joshi, Raviraj},
title = {L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models},
booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {29--34}
}
```
Other models from the family:<br>
Marathi NER : <a href="https://huggingface.co/l3cube-pune/marathi-ner"> marathi-ner </a> <br>
Marathi NER IOB : <a href="https://huggingface.co/l3cube-pune/marathi-ner-iob"> marathi-ner-iob </a> <br>
Marathi Social NER : <a href="https://huggingface.co/l3cube-pune/marathi-social-ner"> marathi-social-ner </a> <br>
Marathi Social NER IOB : <a href="https://huggingface.co/l3cube-pune/marathi-social-ner-iob"> marathi-social-ner-iob </a> <br>
Marathi Mixed NER (MahNER + MahaSocialNER) : <a href="https://huggingface.co/l3cube-pune/marathi-mixed-ner"> marathi-mixed-ner </a> <br>
Marathi Mixed NER IOB (MahNER IOB + MahaSocialNER IOB) : <a href="https://huggingface.co/l3cube-pune/marathi-mixed-ner-iob"> marathi-mixed-ner-iob </a> <br>
| null |
Non_BioNLP
|
## MahaMixedNER-BERT-IOB
MahaMixedNER-BERT-IOB is a MahaBERT(<a href="https://huggingface.co/l3cube-pune/marathi-bert-v2">l3cube-pune/marathi-bert-v2</a>) model fine-tuned on L3Cube-MahaNER + MahaSocialNER (with IOB tags) - a Marathi named entity recognition datasets.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2401.00170)
Non IOB Model : <a href="https://huggingface.co/l3cube-pune/marathi-mixed-ner"> marathi-mixed-ner </a>
Citing:
```
@article{chaudhari2023l3cube,
title={L3Cube-MahaSocialNER: A Social Media based Marathi NER Dataset and BERT models},
author={Chaudhari, Harsh and Patil, Anuja and Lavekar, Dhanashree and Khairnar, Pranav and Joshi, Raviraj},
journal={arXiv preprint arXiv:2401.00170},
year={2023}
}
```
```
@InProceedings{litake-EtAl:2022:WILDRE6,
author = {Litake, Onkar and Sabane, Maithili Ravindra and Patil, Parth Sachin and Ranade, Aparna Abhijeet and Joshi, Raviraj},
title = {L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models},
booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {29--34}
}
```
Other models from the family:<br>
Marathi NER : <a href="https://huggingface.co/l3cube-pune/marathi-ner"> marathi-ner </a> <br>
Marathi NER IOB : <a href="https://huggingface.co/l3cube-pune/marathi-ner-iob"> marathi-ner-iob </a> <br>
Marathi Social NER : <a href="https://huggingface.co/l3cube-pune/marathi-social-ner"> marathi-social-ner </a> <br>
Marathi Social NER IOB : <a href="https://huggingface.co/l3cube-pune/marathi-social-ner-iob"> marathi-social-ner-iob </a> <br>
Marathi Mixed NER (MahNER + MahaSocialNER) : <a href="https://huggingface.co/l3cube-pune/marathi-mixed-ner"> marathi-mixed-ner </a> <br>
Marathi Mixed NER IOB (MahNER IOB + MahaSocialNER IOB) : <a href="https://huggingface.co/l3cube-pune/marathi-mixed-ner-iob"> marathi-mixed-ner-iob </a> <br>
|
{"datasets": ["L3Cube-MahaSocialNER", "L3Cube-MahaNER"], "language": "mr", "license": "cc-by-4.0", "widget": [{"text": "सोलापूर जिल्ह्यातील माळशिरस तालुक्यात जाधववाडी येथे विजय गंगाप्रकल्पाचा शुभारंभ केला हे जलसंधारणाचे एकूण 28 किमीचे 3 टप्प्यात काम होत असून पहिल्या 10 किमीचे काम पूर्ण झाले आहे"}]}
|
task
|
[
"NAMED_ENTITY_RECOGNITION"
] | 41,941 |
aks1s/13volta-3
|
aks1s
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"merges",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:finetune:NousResearch/Hermes-2-Pro-Llama-3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-15T21:57:11Z |
2024-07-15T22:00:28+00:00
| 8 | 0 |
---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
datasets:
- teknium/OpenHermes-2.5
language:
- en
license: apache-2.0
tags:
- Llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- merges
widget:
- example_title: Hermes 2 Pro Llama-3 Instruct Merge
messages:
- role: system
content: You are a sentient, superintelligent artificial general intelligence,
here to teach and assist me.
- role: user
content: Write a short story about Goku discovering kirby has teamed up with Majin
Buu to destroy the world.
model-index:
- name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge
results: []
---
# - Hermes-2 Θ Llama-3 8B
## Model Description
Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard at [Arcee](https://www.arcee.ai/), the team behind MergeKit.
Hermes-2 Θ is a merged and then further RLHF'ed version our excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model to form a new model, Hermes-2 Θ, combining the best of both worlds of each model.
## Example Outputs
### Create New Mythos:
### Chat with a Meta-Cognitive Entity
### Ask for a structured JSON output:
# Prompt Format
Hermes 2 Θ uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling. While the system prompt looks complicated, we have created a GitHub repo containing code to easily build these based on real python functions.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
## Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Benchmarks
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5529|± |0.0145|
| | |acc_norm|0.5870|± |0.0144|
|arc_easy | 0|acc |0.8371|± |0.0076|
| | |acc_norm|0.8144|± |0.0080|
|boolq | 1|acc |0.8599|± |0.0061|
|hellaswag | 0|acc |0.6133|± |0.0049|
| | |acc_norm|0.7989|± |0.0040|
|openbookqa | 0|acc |0.3940|± |0.0219|
| | |acc_norm|0.4680|± |0.0223|
|piqa | 0|acc |0.8063|± |0.0092|
| | |acc_norm|0.8156|± |0.0090|
|winogrande | 0|acc |0.7372|± |0.0124|
```
Average: 72.59
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2441|± |0.0270|
| | |acc_norm|0.2441|± |0.0270|
|agieval_logiqa_en | 0|acc |0.3687|± |0.0189|
| | |acc_norm|0.3840|± |0.0191|
|agieval_lsat_ar | 0|acc |0.2304|± |0.0278|
| | |acc_norm|0.2174|± |0.0273|
|agieval_lsat_lr | 0|acc |0.5471|± |0.0221|
| | |acc_norm|0.5373|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6617|± |0.0289|
| | |acc_norm|0.6357|± |0.0294|
|agieval_sat_en | 0|acc |0.7670|± |0.0295|
| | |acc_norm|0.7379|± |0.0307|
|agieval_sat_en_without_passage| 0|acc |0.4417|± |0.0347|
| | |acc_norm|0.4223|± |0.0345|
|agieval_sat_math | 0|acc |0.4000|± |0.0331|
| | |acc_norm|0.3455|± |0.0321|
```
Average: 44.05
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6000|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6585|± |0.0247|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3178|± |0.0290|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2340|± |0.0224|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2980|± |0.0205|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2057|± |0.0153|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5367|± |0.0288|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4040|± |0.0220|
|bigbench_navigate | 0|multiple_choice_grade|0.4970|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7075|± |0.0102|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4821|± |0.0236|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2295|± |0.0133|
|bigbench_snarks | 0|multiple_choice_grade|0.6906|± |0.0345|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5375|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.6270|± |0.0153|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2216|± |0.0118|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1594|± |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5367|± |0.0288|
```
Average: 44.13
**IFEval**: 72.64
**MT_Bench**: Turn 1 - 8.3875, Turn 2 - 8.00625, Average - 8.196875
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Theta-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"NousResearch/Hermes-2-Theta-Llama-3-8B",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
## Inference Code for Function Calling:
All code for utilizing, parsing, and building function calling templates is available on our github:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
# Chat Interfaces
When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
## Quantized Versions:
GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF
# How to cite:
```bibtext
@misc{Hermes-2-Theta-Llama-3-8B,
url={[https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B][NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B))},
title={Hermes-2-Theta-Llama-3-8B},
author={"Teknium", Charles Goddard, "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
}
```
| null |
Non_BioNLP
|
# - Hermes-2 Θ Llama-3 8B
## Model Description
Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard at [Arcee](https://www.arcee.ai/), the team behind MergeKit.
Hermes-2 Θ is a merged and then further RLHF'ed version our excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model to form a new model, Hermes-2 Θ, combining the best of both worlds of each model.
## Example Outputs
### Create New Mythos:
### Chat with a Meta-Cognitive Entity
### Ask for a structured JSON output:
# Prompt Format
Hermes 2 Θ uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling. While the system prompt looks complicated, we have created a GitHub repo containing code to easily build these based on real python functions.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
## Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Benchmarks
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5529|± |0.0145|
| | |acc_norm|0.5870|± |0.0144|
|arc_easy | 0|acc |0.8371|± |0.0076|
| | |acc_norm|0.8144|± |0.0080|
|boolq | 1|acc |0.8599|± |0.0061|
|hellaswag | 0|acc |0.6133|± |0.0049|
| | |acc_norm|0.7989|± |0.0040|
|openbookqa | 0|acc |0.3940|± |0.0219|
| | |acc_norm|0.4680|± |0.0223|
|piqa | 0|acc |0.8063|± |0.0092|
| | |acc_norm|0.8156|± |0.0090|
|winogrande | 0|acc |0.7372|± |0.0124|
```
Average: 72.59
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2441|± |0.0270|
| | |acc_norm|0.2441|± |0.0270|
|agieval_logiqa_en | 0|acc |0.3687|± |0.0189|
| | |acc_norm|0.3840|± |0.0191|
|agieval_lsat_ar | 0|acc |0.2304|± |0.0278|
| | |acc_norm|0.2174|± |0.0273|
|agieval_lsat_lr | 0|acc |0.5471|± |0.0221|
| | |acc_norm|0.5373|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6617|± |0.0289|
| | |acc_norm|0.6357|± |0.0294|
|agieval_sat_en | 0|acc |0.7670|± |0.0295|
| | |acc_norm|0.7379|± |0.0307|
|agieval_sat_en_without_passage| 0|acc |0.4417|± |0.0347|
| | |acc_norm|0.4223|± |0.0345|
|agieval_sat_math | 0|acc |0.4000|± |0.0331|
| | |acc_norm|0.3455|± |0.0321|
```
Average: 44.05
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6000|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6585|± |0.0247|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3178|± |0.0290|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2340|± |0.0224|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2980|± |0.0205|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2057|± |0.0153|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5367|± |0.0288|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4040|± |0.0220|
|bigbench_navigate | 0|multiple_choice_grade|0.4970|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7075|± |0.0102|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4821|± |0.0236|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2295|± |0.0133|
|bigbench_snarks | 0|multiple_choice_grade|0.6906|± |0.0345|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5375|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.6270|± |0.0153|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2216|± |0.0118|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1594|± |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5367|± |0.0288|
```
Average: 44.13
**IFEval**: 72.64
**MT_Bench**: Turn 1 - 8.3875, Turn 2 - 8.00625, Average - 8.196875
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Theta-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"NousResearch/Hermes-2-Theta-Llama-3-8B",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
## Inference Code for Function Calling:
All code for utilizing, parsing, and building function calling templates is available on our github:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
# Chat Interfaces
When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
## Quantized Versions:
GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF
# How to cite:
```bibtext
@misc{Hermes-2-Theta-Llama-3-8B,
url={[https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B][NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B))},
title={Hermes-2-Theta-Llama-3-8B},
author={"Teknium", Charles Goddard, "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
}
```
|
{"base_model": "NousResearch/Hermes-2-Pro-Llama-3-8B", "datasets": ["teknium/OpenHermes-2.5"], "language": ["en"], "license": "apache-2.0", "tags": ["Llama-3", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "function calling", "json mode", "axolotl", "merges"], "widget": [{"example_title": "Hermes 2 Pro Llama-3 Instruct Merge", "messages": [{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."}, {"role": "user", "content": "Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world."}]}], "model-index": [{"name": "Hermes-2-Pro-Llama-3-Instruct-8B-Merge", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 41,942 |
nguyenkhoa2407/favsbot_filtersort_using_t5_summarization
|
nguyenkhoa2407
|
text2text-generation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:filter_sort",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-10-07T08:14:37Z |
2022-10-07T08:58:31+00:00
| 11 | 0 |
---
datasets:
- filter_sort
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: favsbot_filtersort_using_t5_summarization
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: filter_sort
type: filter_sort
config: default
split: train
args: default
metrics:
- type: rouge
value: 15.7351
name: Rouge1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# favsbot_filtersort_using_t5_summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the filter_sort dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3327
- Rouge1: 15.7351
- Rouge2: 0.0
- Rougel: 13.4803
- Rougelsum: 13.5134
- Gen Len: 12.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 5 | 3.8161 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.8789 | 2.0 | 10 | 3.6423 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.8789 | 3.0 | 15 | 3.4687 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.5407 | 4.0 | 20 | 3.3086 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.5407 | 5.0 | 25 | 3.1726 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.2216 | 6.0 | 30 | 3.0464 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 4.2216 | 7.0 | 35 | 2.9326 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 4.0021 | 8.0 | 40 | 2.8305 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 4.0021 | 9.0 | 45 | 2.7386 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.7634 | 10.0 | 50 | 2.6588 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.7634 | 11.0 | 55 | 2.5916 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.6224 | 12.0 | 60 | 2.5358 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.6224 | 13.0 | 65 | 2.4895 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.496 | 14.0 | 70 | 2.4486 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.496 | 15.0 | 75 | 2.4140 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.4157 | 16.0 | 80 | 2.3857 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.4157 | 17.0 | 85 | 2.3622 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.3964 | 18.0 | 90 | 2.3455 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.3964 | 19.0 | 95 | 2.3361 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.3502 | 20.0 | 100 | 2.3327 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# favsbot_filtersort_using_t5_summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the filter_sort dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3327
- Rouge1: 15.7351
- Rouge2: 0.0
- Rougel: 13.4803
- Rougelsum: 13.5134
- Gen Len: 12.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 5 | 3.8161 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.8789 | 2.0 | 10 | 3.6423 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.8789 | 3.0 | 15 | 3.4687 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.5407 | 4.0 | 20 | 3.3086 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.5407 | 5.0 | 25 | 3.1726 | 14.754 | 0.0 | 12.6197 | 12.6426 | 10.5 |
| 4.2216 | 6.0 | 30 | 3.0464 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 4.2216 | 7.0 | 35 | 2.9326 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 4.0021 | 8.0 | 40 | 2.8305 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 4.0021 | 9.0 | 45 | 2.7386 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.7634 | 10.0 | 50 | 2.6588 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.7634 | 11.0 | 55 | 2.5916 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.6224 | 12.0 | 60 | 2.5358 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.6224 | 13.0 | 65 | 2.4895 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.496 | 14.0 | 70 | 2.4486 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.496 | 15.0 | 75 | 2.4140 | 15.7792 | 0.0 | 13.5134 | 13.5411 | 12.6667 |
| 3.4157 | 16.0 | 80 | 2.3857 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.4157 | 17.0 | 85 | 2.3622 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.3964 | 18.0 | 90 | 2.3455 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.3964 | 19.0 | 95 | 2.3361 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
| 3.3502 | 20.0 | 100 | 2.3327 | 15.7351 | 0.0 | 13.4803 | 13.5134 | 12.6667 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
|
{"datasets": ["filter_sort"], "license": "apache-2.0", "metrics": ["rouge"], "tags": ["generated_from_trainer"], "model-index": [{"name": "favsbot_filtersort_using_t5_summarization", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "filter_sort", "type": "filter_sort", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "rouge", "value": 15.7351, "name": "Rouge1"}]}]}]}
|
task
|
[
"SUMMARIZATION"
] | 41,943 |
Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime
|
Mizuiro-sakura
|
text-classification
|
[
"transformers",
"pytorch",
"safetensors",
"luke",
"text-classification",
"sentiment-analysis",
"wrime",
"SentimentAnalysis",
"sentiment-classification",
"ja",
"dataset:shunk031/wrime",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-03-13T12:40:08Z |
2023-05-15T12:58:08+00:00
| 37,100 | 39 |
---
datasets: shunk031/wrime
language: ja
license: mit
tags:
- luke
- sentiment-analysis
- wrime
- SentimentAnalysis
- pytorch
- sentiment-classification
---
# このモデルはLuke-japanese-large-liteをファインチューニングしたものです。
このモデルは８つの感情（喜び、悲しみ、期待、驚き、怒り、恐れ、嫌悪、信頼）の内、どの感情が文章に含まれているのか分析することができます。
このモデルはwrimeデータセット（
https://huggingface.co/datasets/shunk031/wrime
）を用いて学習を行いました。
# This model is based on Luke-japanese-large-lite
This model is fine-tuned model which besed on studio-ousia/Luke-japanese-large-lite.
This could be able to analyze which emotions (joy or sadness or anticipation or surprise or anger or fear or disdust or trust ) are included.
This model was fine-tuned by using wrime dataset.
# what is Luke? Lukeとは？[1]
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transformer. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. LUKE adopts an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores.
LUKE achieves state-of-the-art results on five popular NLP benchmarks including SQuAD v1.1 (extractive question answering), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), TACRED (relation classification), and Open Entity (entity typing).
luke-japaneseは、単語とエンティティの知識拡張型訓練済み Transformer モデルLUKEの日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。
# how to use 使い方
ステップ1：pythonとpytorch, sentencepieceのインストールとtransformersのアップデート（バージョンが古すぎるとLukeTokenizerが入っていないため）
update transformers and install sentencepiece, python and pytorch
ステップ2：下記のコードを実行する
Please execute this code
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig
import torch
tokenizer = AutoTokenizer.from_pretrained("Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime")
config = LukeConfig.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', output_hidden_states=True)
model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', config=config)
text='すごく楽しかった。また行きたい。'
max_seq_length=512
token=tokenizer(text,
truncation=True,
max_length=max_seq_length,
padding="max_length")
output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0))
max_index=torch.argmax(torch.tensor(output.logits))
if max_index==0:
print('joy、うれしい')
elif max_index==1:
print('sadness、悲しい')
elif max_index==2:
print('anticipation、期待')
elif max_index==3:
print('surprise、驚き')
elif max_index==4:
print('anger、怒り')
elif max_index==5:
print('fear、恐れ')
elif max_index==6:
print('disgust、嫌悪')
elif max_index==7:
print('trust、信頼')
```
# Acknowledgments 謝辞
Lukeの開発者である山田先生とStudio ousiaさんには感謝いたします。
I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia.
# Citation
[1]@inproceedings{yamada2020luke,
title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention},
author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto},
booktitle={EMNLP},
year={2020}
}
| null |
Non_BioNLP
|
# このモデルはLuke-japanese-large-liteをファインチューニングしたものです。
このモデルは８つの感情（喜び、悲しみ、期待、驚き、怒り、恐れ、嫌悪、信頼）の内、どの感情が文章に含まれているのか分析することができます。
このモデルはwrimeデータセット（
https://huggingface.co/datasets/shunk031/wrime
）を用いて学習を行いました。
# This model is based on Luke-japanese-large-lite
This model is fine-tuned model which besed on studio-ousia/Luke-japanese-large-lite.
This could be able to analyze which emotions (joy or sadness or anticipation or surprise or anger or fear or disdust or trust ) are included.
This model was fine-tuned by using wrime dataset.
# what is Luke? Lukeとは？[1]
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transformer. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. LUKE adopts an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores.
LUKE achieves state-of-the-art results on five popular NLP benchmarks including SQuAD v1.1 (extractive question answering), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), TACRED (relation classification), and Open Entity (entity typing).
luke-japaneseは、単語とエンティティの知識拡張型訓練済み Transformer モデルLUKEの日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。
# how to use 使い方
ステップ1：pythonとpytorch, sentencepieceのインストールとtransformersのアップデート（バージョンが古すぎるとLukeTokenizerが入っていないため）
update transformers and install sentencepiece, python and pytorch
ステップ2：下記のコードを実行する
Please execute this code
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig
import torch
tokenizer = AutoTokenizer.from_pretrained("Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime")
config = LukeConfig.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', output_hidden_states=True)
model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', config=config)
text='すごく楽しかった。また行きたい。'
max_seq_length=512
token=tokenizer(text,
truncation=True,
max_length=max_seq_length,
padding="max_length")
output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0))
max_index=torch.argmax(torch.tensor(output.logits))
if max_index==0:
print('joy、うれしい')
elif max_index==1:
print('sadness、悲しい')
elif max_index==2:
print('anticipation、期待')
elif max_index==3:
print('surprise、驚き')
elif max_index==4:
print('anger、怒り')
elif max_index==5:
print('fear、恐れ')
elif max_index==6:
print('disgust、嫌悪')
elif max_index==7:
print('trust、信頼')
```
# Acknowledgments 謝辞
Lukeの開発者である山田先生とStudio ousiaさんには感謝いたします。
I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia.
# Citation
[1]@inproceedings{yamada2020luke,
title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention},
author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto},
booktitle={EMNLP},
year={2020}
}
|
{"datasets": "shunk031/wrime", "language": "ja", "license": "mit", "tags": ["luke", "sentiment-analysis", "wrime", "SentimentAnalysis", "pytorch", "sentiment-classification"]}
|
task
|
[
"NAMED_ENTITY_RECOGNITION",
"QUESTION_ANSWERING"
] | 41,944 |
fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-83904142
|
fine-tuned
|
feature-extraction
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"dataset:fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-83904142",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-29T00:40:00Z |
2024-05-29T00:41:00+00:00
| 6 | 0 |
---
datasets:
- fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-83904142
- allenai/c4
language:
- en
- en
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
---
This model is a fine-tuned version of [**BAAI/bge-m3**](https://huggingface.co/BAAI/bge-m3) designed for the following use case:
None
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-83904142',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
| null |
Non_BioNLP
|
This model is a fine-tuned version of [**BAAI/bge-m3**](https://huggingface.co/BAAI/bge-m3) designed for the following use case:
None
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-83904142',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
{"datasets": ["fine-tuned/ArguAna-512-192-gpt-4o-2024-05-13-83904142", "allenai/c4"], "language": ["en", "en"], "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,945 |
Helsinki-NLP/opus-mt-ja-hu
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ja",
"hu",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T11:59:13+00:00
| 269 | 2 |
---
language:
- ja
- hu
license: apache-2.0
tags:
- translation
---
### jpn-hun
* source group: Japanese
* target group: Hungarian
* OPUS readme: [jpn-hun](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md)
* model: transformer-align
* source language(s): jpn_Bopo jpn_Hani jpn_Hira jpn_Kana jpn_Yiii
* target language(s): hun
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.jpn.hun | 12.2 | 0.364 |
### System Info:
- hf_name: jpn-hun
- source_languages: jpn
- target_languages: hun
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ja', 'hu']
- src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'}
- tgt_constituents: {'hun'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.test.txt
- src_alpha3: jpn
- tgt_alpha3: hun
- short_pair: ja-hu
- chrF2_score: 0.364
- bleu: 12.2
- brevity_penalty: 1.0
- ref_len: 18625.0
- src_name: Japanese
- tgt_name: Hungarian
- train_date: 2020-06-17
- src_alpha2: ja
- tgt_alpha2: hu
- prefer_old: False
- long_pair: jpn-hun
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
| null |
Non_BioNLP
|
### jpn-hun
* source group: Japanese
* target group: Hungarian
* OPUS readme: [jpn-hun](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md)
* model: transformer-align
* source language(s): jpn_Bopo jpn_Hani jpn_Hira jpn_Kana jpn_Yiii
* target language(s): hun
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.jpn.hun | 12.2 | 0.364 |
### System Info:
- hf_name: jpn-hun
- source_languages: jpn
- target_languages: hun
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-hun/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ja', 'hu']
- src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'}
- tgt_constituents: {'hun'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-hun/opus-2020-06-17.test.txt
- src_alpha3: jpn
- tgt_alpha3: hun
- short_pair: ja-hu
- chrF2_score: 0.364
- bleu: 12.2
- brevity_penalty: 1.0
- ref_len: 18625.0
- src_name: Japanese
- tgt_name: Hungarian
- train_date: 2020-06-17
- src_alpha2: ja
- tgt_alpha2: hu
- prefer_old: False
- long_pair: jpn-hun
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
{"language": ["ja", "hu"], "license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,946 |
keras/bart_base_en
|
keras
|
text-classification
|
[
"keras-hub",
"keras",
"text-classification",
"en",
"arxiv:1910.13461",
"license:apache-2.0",
"region:us"
] | 2024-10-28T21:58:42Z |
2025-03-24T22:43:13+00:00
| 7 | 0 |
---
language:
- en
library_name: keras-hub
license: apache-2.0
pipeline_tag: text-classification
tags:
- keras
- text-classification
---
### Model Overview
BART encoder-decoder network.
This class implements a Transformer-based encoder-decoder model as
described in
["BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"](https://arxiv.org/abs/1910.13461).
The default constructor gives a fully customizable, randomly initialized BART
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the `from_preset` constructor.
Disclaimer: Pre-trained models are provided on an "as is" basis, without
warranties or conditions of any kind. The underlying model is provided by a
third party and subject to a separate license, available
[here](https://github.com/facebookresearch/fairseq/).
## Links
* [BART Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/bart-quickstart-notebook)
* [BART API Documentation](https://keras.io/keras_hub/api/models/bart/)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
## Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|----------------|------------|--------------------------------------------------|
| bart_base_en | 139.42M | 6-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl |
| bart_large_en | 406.29M | 12-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl. |
| bart_large_en_cnn | 406.29M | The bart_large_en backbone model fine-tuned on the CNN+DM summarization dataset. |
__Arguments__
- __vocabulary_size__: int. The size of the token vocabulary.
- __num_layers__: int. The number of transformer encoder layers and
transformer decoder layers.
- __num_heads__: int. The number of attention heads for each transformer.
The hidden size must be divisible by the number of attention heads.
- __hidden_dim__: int. The size of the transformer encoding and pooler layers.
- __intermediate_dim__: int. The output dimension of the first Dense layer in
a two-layer feedforward network for each transformer.
- __dropout__: float. Dropout probability for the Transformer encoder.
- __max_sequence_length__: int. The maximum sequence length that this encoder
can consume. If None, `max_sequence_length` uses the value from
sequence length. This determines the variable shape for positional
embeddings.
## Example Usage
```python
import keras
import keras_hub
import numpy as np
```
Use `generate()` to do text generation, given an input context.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.generate("The quick brown fox", max_length=30)
# Generate with batched inputs.
bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)
```
Compile the `generate()` function with a custom sampler.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.compile(sampler="greedy")
bart_lm.generate("The quick brown fox", max_length=30)
```
Use `generate()` with encoder inputs and an incomplete decoder input (prompt).
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.generate(
{
"encoder_text": "The quick brown fox",
"decoder_text": "The fast"
}
)
```
Use `generate()` without preprocessing.
```python
# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
"encoder_padding_mask": np.array(
[[True, True, True, True, True, True, False, False]]
),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
"decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"bart_base_en",
preprocessor=None,
)
bart_lm.generate(prompt)
```
Call `fit()` on a single batch.
```python
features = {
"encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
"decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.fit(x=features, batch_size=2)
```
Call `fit()` without preprocessing.
```python
x = {
"encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
"encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
"decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"bart_base_en",
preprocessor=None,
)
bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
```
## Example Usage with Hugging Face URI
```python
import keras
import keras_hub
import numpy as np
```
Use `generate()` to do text generation, given an input context.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.generate("The quick brown fox", max_length=30)
# Generate with batched inputs.
bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)
```
Compile the `generate()` function with a custom sampler.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.compile(sampler="greedy")
bart_lm.generate("The quick brown fox", max_length=30)
```
Use `generate()` with encoder inputs and an incomplete decoder input (prompt).
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.generate(
{
"encoder_text": "The quick brown fox",
"decoder_text": "The fast"
}
)
```
Use `generate()` without preprocessing.
```python
# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
"encoder_padding_mask": np.array(
[[True, True, True, True, True, True, False, False]]
),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
"decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"hf://keras/bart_base_en",
preprocessor=None,
)
bart_lm.generate(prompt)
```
Call `fit()` on a single batch.
```python
features = {
"encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
"decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.fit(x=features, batch_size=2)
```
Call `fit()` without preprocessing.
```python
x = {
"encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
"encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
"decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"hf://keras/bart_base_en",
preprocessor=None,
)
bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
```
| null |
Non_BioNLP
|
### Model Overview
BART encoder-decoder network.
This class implements a Transformer-based encoder-decoder model as
described in
["BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"](https://arxiv.org/abs/1910.13461).
The default constructor gives a fully customizable, randomly initialized BART
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the `from_preset` constructor.
Disclaimer: Pre-trained models are provided on an "as is" basis, without
warranties or conditions of any kind. The underlying model is provided by a
third party and subject to a separate license, available
[here](https://github.com/facebookresearch/fairseq/).
## Links
* [BART Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/bart-quickstart-notebook)
* [BART API Documentation](https://keras.io/keras_hub/api/models/bart/)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
## Presets
The following model checkpoints are provided by the Keras team. Full code examples for each are available below.
| Preset name | Parameters | Description |
|----------------|------------|--------------------------------------------------|
| bart_base_en | 139.42M | 6-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl |
| bart_large_en | 406.29M | 12-layer BART model where case is maintained. Trained on BookCorpus, English Wikipedia and CommonCrawl. |
| bart_large_en_cnn | 406.29M | The bart_large_en backbone model fine-tuned on the CNN+DM summarization dataset. |
__Arguments__
- __vocabulary_size__: int. The size of the token vocabulary.
- __num_layers__: int. The number of transformer encoder layers and
transformer decoder layers.
- __num_heads__: int. The number of attention heads for each transformer.
The hidden size must be divisible by the number of attention heads.
- __hidden_dim__: int. The size of the transformer encoding and pooler layers.
- __intermediate_dim__: int. The output dimension of the first Dense layer in
a two-layer feedforward network for each transformer.
- __dropout__: float. Dropout probability for the Transformer encoder.
- __max_sequence_length__: int. The maximum sequence length that this encoder
can consume. If None, `max_sequence_length` uses the value from
sequence length. This determines the variable shape for positional
embeddings.
## Example Usage
```python
import keras
import keras_hub
import numpy as np
```
Use `generate()` to do text generation, given an input context.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.generate("The quick brown fox", max_length=30)
# Generate with batched inputs.
bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)
```
Compile the `generate()` function with a custom sampler.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.compile(sampler="greedy")
bart_lm.generate("The quick brown fox", max_length=30)
```
Use `generate()` with encoder inputs and an incomplete decoder input (prompt).
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.generate(
{
"encoder_text": "The quick brown fox",
"decoder_text": "The fast"
}
)
```
Use `generate()` without preprocessing.
```python
# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
"encoder_padding_mask": np.array(
[[True, True, True, True, True, True, False, False]]
),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
"decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"bart_base_en",
preprocessor=None,
)
bart_lm.generate(prompt)
```
Call `fit()` on a single batch.
```python
features = {
"encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
"decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("bart_base_en")
bart_lm.fit(x=features, batch_size=2)
```
Call `fit()` without preprocessing.
```python
x = {
"encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
"encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
"decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"bart_base_en",
preprocessor=None,
)
bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
```
## Example Usage with Hugging Face URI
```python
import keras
import keras_hub
import numpy as np
```
Use `generate()` to do text generation, given an input context.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.generate("The quick brown fox", max_length=30)
# Generate with batched inputs.
bart_lm.generate(["The quick brown fox", "The whale"], max_length=30)
```
Compile the `generate()` function with a custom sampler.
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.compile(sampler="greedy")
bart_lm.generate("The quick brown fox", max_length=30)
```
Use `generate()` with encoder inputs and an incomplete decoder input (prompt).
```python
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.generate(
{
"encoder_text": "The quick brown fox",
"decoder_text": "The fast"
}
)
```
Use `generate()` without preprocessing.
```python
# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "The fast". Use
# `"padding_mask"` to indicate values that should not be overridden.
prompt = {
"encoder_token_ids": np.array([[0, 133, 2119, 6219, 23602, 2, 1, 1]]),
"encoder_padding_mask": np.array(
[[True, True, True, True, True, True, False, False]]
),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2, 1, 1]]),
"decoder_padding_mask": np.array([[True, True, True, True, False, False]])
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"hf://keras/bart_base_en",
preprocessor=None,
)
bart_lm.generate(prompt)
```
Call `fit()` on a single batch.
```python
features = {
"encoder_text": ["The quick brown fox jumped.", "I forgot my homework."],
"decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset("hf://keras/bart_base_en")
bart_lm.fit(x=features, batch_size=2)
```
Call `fit()` without preprocessing.
```python
x = {
"encoder_token_ids": np.array([[0, 133, 2119, 2, 1]] * 2),
"encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
"decoder_token_ids": np.array([[2, 0, 133, 1769, 2]] * 2),
"decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[0, 133, 1769, 2, 1]] * 2)
sw = np.array([[1, 1, 1, 1, 0]] * 2)
bart_lm = keras_hub.models.BartSeq2SeqLM.from_preset(
"hf://keras/bart_base_en",
preprocessor=None,
)
bart_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
```
|
{"language": ["en"], "library_name": "keras-hub", "license": "apache-2.0", "pipeline_tag": "text-classification", "tags": ["keras", "text-classification"]}
|
task
|
[
"TRANSLATION",
"SUMMARIZATION"
] | 41,948 |
mini1013/master_cate_lh4
|
mini1013
|
text-classification
|
[
"setfit",
"safetensors",
"roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:mini1013/master_domain",
"base_model:finetune:mini1013/master_domain",
"model-index",
"region:us"
] | 2024-11-20T15:01:35Z |
2024-11-20T15:01:58+00:00
| 374 | 0 |
---
base_model: mini1013/master_domain
library_name: setfit
metrics:
- metric
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 동아제약 가그린 오리지널 가글 750ml (1개) 가그린 오리지널 820ml L스토어
- text: 스켈링 입냄새 스케일러 치석제거기 구강청결기 치아 별이 빛나는 하늘 보라색 사치(sachi)
- text: 텅브러쉬 4개세트 혀클리너 입냄새제거 혀백태제거 혀칫솔 i MinSellAmount 펀키보이
- text: '[갤러리아] 폴리덴트 의치 부착재 민트향 70g x5개 한화갤러리아(주)'
- text: 애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마 플렉스세븐
inference: true
model-index:
- name: SetFit with mini1013/master_domain
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: metric
value: 0.9477272727272728
name: Metric
---
# SetFit with mini1013/master_domain
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 10 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 9.0 | <ul><li>'롤리팝 에디슨 항균 혀클리너 4종 퍼플 파랑새랑'</li><li>'텅브러쉬 혀클리너 입냄새제거 백태제거 혀칫솔 MinSellAmount 펀키보이'</li><li>'[생활도감] 혀클리너 세트 그린2개+네이비2개 주식회사 생활도감'</li></ul> |
| 2.0 | <ul><li>'셀프 가정용 스테인레스 스케일링 치석제거기 청소 도구 304 핑크 6종 세트 주식회사 클라우드'</li><li>'도구 치경 제거 편도석 제거기 입똥 편도결석 목똥 셀프 발광 귀걸이x수납함 로얄산티아고'</li><li>'소형 구취 측정기 테스트기 휴대용 냄새 악취 호흡 구강 입냄새측정기 자가진단 자가 가스 표준모델 _ 검정 행복초지'</li></ul> |
| 0.0 | <ul><li>'존슨앤존슨 구강청결 리스테린 쿨민트 250ml 후레쉬버스트 250ml - 1개 디아크코리아'</li><li>'일회용 여행용 가그린 라임10g 1개 휴대용 오리지널 가글스틱 오리지널 1개 예그린스페이스'</li><li>'가그린 제로 1200ML 쓱1day배송'</li></ul> |
| 4.0 | <ul><li>'투스노트 화이트닝겔 하루 2번 30분 투자로 누런이를 하얗게 투스노트 화이트닝겔 2주분 주식회사 네이처폴'</li><li>'루치펠로 미스틱포레스트 치약 180g 5개 원라이브팩토리'</li><li>'대형 치아모형 치아 모델 구조 인체 구강 치과 C. 구강 2배 확대(하아 제거 가능) 마켓 스페이스토끼'</li></ul> |
| 8.0 | <ul><li>'미소덴탈 교정장치보관함 교정기케이스 교정기통 교정기보관함-옐로우 (주)톡톡그린'</li><li>'성심 덴트크린 틀니세정제 36개입 2개 교정기 세척 희망메디'</li><li>'폴리덴트 맥스 씰 의치 부착재(의치 접착제) 70gx5개+샘플 1개 더마켓'</li></ul> |
| 6.0 | <ul><li>'백선생 왕타칫솔 베이직 스톤 10P 왕타'</li><li>'켄트칫솔 클래식 6개입 부드러운 칫솔 미세모 치아관리 어금니 치과칫솔 켄트 클래식 6개_켄트 탄 초극세모 1개(랜덤)_치간칫솔 8개입 1세트(레드 0.7mm) (주)지로인터내셔널'</li><li>'쿤달 딥 클린 탄력 항균 이중미세모 칫솔 부드러운모, 16입, 1개 구분 : 부드러운모 슈팅배송'</li></ul> |
| 3.0 | <ul><li>'오랄비 P&G 왁스치실 민트향 50m 01.왁스 치실 민트향 50m TH상사'</li><li>'오랄비 C자형 일회용 치실 30개입 1팩 NEW)치실C자 30개입[O121] 한국피앤지판매유한회사'</li><li>'오랄비 왁스치실 (50m 1개) 민트 디엔지유통'</li></ul> |
| 5.0 | <ul><li>'LG생활건강 죽염 명약원 골든프로폴리스 치약 플러스 120g MinSellAmount 오늘도연구소'</li><li>'엘지생활건강 죽염 잇몸고 치약 120g 1개 유니스'</li><li>'센소다인 오리지널 플러스 치약 100g 1개 dm 다임커머스'</li></ul> |
| 7.0 | <ul><li>'[유한양행]닥터버들 치약+칫솔 여행용세트 6개 신세계몰'</li><li>'[유한양행]닥터버들 휴대용 칫솔치약세트 1개 신세계몰'</li><li>'투톤 휴대용 칫솔 치약 케이스 캡슐형 답례품 투톤용 칫솔통 보관함 홀더 칫솔캡 캡슐칫 화이트블루 쏭리빙'</li></ul> |
| 1.0 | <ul><li>'일제 형상기억 마우스피스 아리더샾'</li><li>'혀용 코골이 방지 용품 대책용 마우스피스 8 개 세트 이와이리테일(EY리테일)'</li><li>'이갈이방지 치아 앞니 보호 유지 셀프 마우스피스 교정 2단계 코스모스'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9477 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_lh4")
# Run inference
preds = model("애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마 플렉스세븐")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 10.026 | 23 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 50 |
| 6.0 | 50 |
| 7.0 | 50 |
| 8.0 | 50 |
| 9.0 | 50 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0127 | 1 | 0.4686 | - |
| 0.6329 | 50 | 0.2751 | - |
| 1.2658 | 100 | 0.1179 | - |
| 1.8987 | 150 | 0.0739 | - |
| 2.5316 | 200 | 0.0687 | - |
| 3.1646 | 250 | 0.0466 | - |
| 3.7975 | 300 | 0.0591 | - |
| 4.4304 | 350 | 0.0232 | - |
| 5.0633 | 400 | 0.0125 | - |
| 5.6962 | 450 | 0.0134 | - |
| 6.3291 | 500 | 0.0152 | - |
| 6.9620 | 550 | 0.0175 | - |
| 7.5949 | 600 | 0.0118 | - |
| 8.2278 | 650 | 0.007 | - |
| 8.8608 | 700 | 0.0003 | - |
| 9.4937 | 750 | 0.0002 | - |
| 10.1266 | 800 | 0.0001 | - |
| 10.7595 | 850 | 0.0001 | - |
| 11.3924 | 900 | 0.0001 | - |
| 12.0253 | 950 | 0.0001 | - |
| 12.6582 | 1000 | 0.0001 | - |
| 13.2911 | 1050 | 0.0001 | - |
| 13.9241 | 1100 | 0.0001 | - |
| 14.5570 | 1150 | 0.0001 | - |
| 15.1899 | 1200 | 0.0001 | - |
| 15.8228 | 1250 | 0.0001 | - |
| 16.4557 | 1300 | 0.0001 | - |
| 17.0886 | 1350 | 0.0001 | - |
| 17.7215 | 1400 | 0.0001 | - |
| 18.3544 | 1450 | 0.0001 | - |
| 18.9873 | 1500 | 0.0 | - |
| 19.6203 | 1550 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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| null |
Non_BioNLP
|
# SetFit with mini1013/master_domain
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 10 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 9.0 | <ul><li>'롤리팝 에디슨 항균 혀클리너 4종 퍼플 파랑새랑'</li><li>'텅브러쉬 혀클리너 입냄새제거 백태제거 혀칫솔 MinSellAmount 펀키보이'</li><li>'[생활도감] 혀클리너 세트 그린2개+네이비2개 주식회사 생활도감'</li></ul> |
| 2.0 | <ul><li>'셀프 가정용 스테인레스 스케일링 치석제거기 청소 도구 304 핑크 6종 세트 주식회사 클라우드'</li><li>'도구 치경 제거 편도석 제거기 입똥 편도결석 목똥 셀프 발광 귀걸이x수납함 로얄산티아고'</li><li>'소형 구취 측정기 테스트기 휴대용 냄새 악취 호흡 구강 입냄새측정기 자가진단 자가 가스 표준모델 _ 검정 행복초지'</li></ul> |
| 0.0 | <ul><li>'존슨앤존슨 구강청결 리스테린 쿨민트 250ml 후레쉬버스트 250ml - 1개 디아크코리아'</li><li>'일회용 여행용 가그린 라임10g 1개 휴대용 오리지널 가글스틱 오리지널 1개 예그린스페이스'</li><li>'가그린 제로 1200ML 쓱1day배송'</li></ul> |
| 4.0 | <ul><li>'투스노트 화이트닝겔 하루 2번 30분 투자로 누런이를 하얗게 투스노트 화이트닝겔 2주분 주식회사 네이처폴'</li><li>'루치펠로 미스틱포레스트 치약 180g 5개 원라이브팩토리'</li><li>'대형 치아모형 치아 모델 구조 인체 구강 치과 C. 구강 2배 확대(하아 제거 가능) 마켓 스페이스토끼'</li></ul> |
| 8.0 | <ul><li>'미소덴탈 교정장치보관함 교정기케이스 교정기통 교정기보관함-옐로우 (주)톡톡그린'</li><li>'성심 덴트크린 틀니세정제 36개입 2개 교정기 세척 희망메디'</li><li>'폴리덴트 맥스 씰 의치 부착재(의치 접착제) 70gx5개+샘플 1개 더마켓'</li></ul> |
| 6.0 | <ul><li>'백선생 왕타칫솔 베이직 스톤 10P 왕타'</li><li>'켄트칫솔 클래식 6개입 부드러운 칫솔 미세모 치아관리 어금니 치과칫솔 켄트 클래식 6개_켄트 탄 초극세모 1개(랜덤)_치간칫솔 8개입 1세트(레드 0.7mm) (주)지로인터내셔널'</li><li>'쿤달 딥 클린 탄력 항균 이중미세모 칫솔 부드러운모, 16입, 1개 구분 : 부드러운모 슈팅배송'</li></ul> |
| 3.0 | <ul><li>'오랄비 P&G 왁스치실 민트향 50m 01.왁스 치실 민트향 50m TH상사'</li><li>'오랄비 C자형 일회용 치실 30개입 1팩 NEW)치실C자 30개입[O121] 한국피앤지판매유한회사'</li><li>'오랄비 왁스치실 (50m 1개) 민트 디엔지유통'</li></ul> |
| 5.0 | <ul><li>'LG생활건강 죽염 명약원 골든프로폴리스 치약 플러스 120g MinSellAmount 오늘도연구소'</li><li>'엘지생활건강 죽염 잇몸고 치약 120g 1개 유니스'</li><li>'센소다인 오리지널 플러스 치약 100g 1개 dm 다임커머스'</li></ul> |
| 7.0 | <ul><li>'[유한양행]닥터버들 치약+칫솔 여행용세트 6개 신세계몰'</li><li>'[유한양행]닥터버들 휴대용 칫솔치약세트 1개 신세계몰'</li><li>'투톤 휴대용 칫솔 치약 케이스 캡슐형 답례품 투톤용 칫솔통 보관함 홀더 칫솔캡 캡슐칫 화이트블루 쏭리빙'</li></ul> |
| 1.0 | <ul><li>'일제 형상기억 마우스피스 아리더샾'</li><li>'혀용 코골이 방지 용품 대책용 마우스피스 8 개 세트 이와이리테일(EY리테일)'</li><li>'이갈이방지 치아 앞니 보호 유지 셀프 마우스피스 교정 2단계 코스모스'</li></ul> |
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.9477 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_lh4")
# Run inference
preds = model("애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마 플렉스세븐")
```
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### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 10.026 | 23 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 50 |
| 1.0 | 50 |
| 2.0 | 50 |
| 3.0 | 50 |
| 4.0 | 50 |
| 5.0 | 50 |
| 6.0 | 50 |
| 7.0 | 50 |
| 8.0 | 50 |
| 9.0 | 50 |
### Training Hyperparameters
- batch_size: (512, 512)
- num_epochs: (20, 20)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0127 | 1 | 0.4686 | - |
| 0.6329 | 50 | 0.2751 | - |
| 1.2658 | 100 | 0.1179 | - |
| 1.8987 | 150 | 0.0739 | - |
| 2.5316 | 200 | 0.0687 | - |
| 3.1646 | 250 | 0.0466 | - |
| 3.7975 | 300 | 0.0591 | - |
| 4.4304 | 350 | 0.0232 | - |
| 5.0633 | 400 | 0.0125 | - |
| 5.6962 | 450 | 0.0134 | - |
| 6.3291 | 500 | 0.0152 | - |
| 6.9620 | 550 | 0.0175 | - |
| 7.5949 | 600 | 0.0118 | - |
| 8.2278 | 650 | 0.007 | - |
| 8.8608 | 700 | 0.0003 | - |
| 9.4937 | 750 | 0.0002 | - |
| 10.1266 | 800 | 0.0001 | - |
| 10.7595 | 850 | 0.0001 | - |
| 11.3924 | 900 | 0.0001 | - |
| 12.0253 | 950 | 0.0001 | - |
| 12.6582 | 1000 | 0.0001 | - |
| 13.2911 | 1050 | 0.0001 | - |
| 13.9241 | 1100 | 0.0001 | - |
| 14.5570 | 1150 | 0.0001 | - |
| 15.1899 | 1200 | 0.0001 | - |
| 15.8228 | 1250 | 0.0001 | - |
| 16.4557 | 1300 | 0.0001 | - |
| 17.0886 | 1350 | 0.0001 | - |
| 17.7215 | 1400 | 0.0001 | - |
| 18.3544 | 1450 | 0.0001 | - |
| 18.9873 | 1500 | 0.0 | - |
| 19.6203 | 1550 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.1.1
- Transformers: 4.46.1
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.20.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
{"base_model": "mini1013/master_domain", "library_name": "setfit", "metrics": ["metric"], "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "widget": [{"text": "동아제약 가그린 오리지널 가글 750ml (1개) 가그린 오리지널 820ml L스토어"}, {"text": "스켈링 입냄새 스케일러 치석제거기 구강청결기 치아 별이 빛나는 하늘 보라색 사치(sachi)"}, {"text": "텅브러쉬 4개세트 혀클리너 입냄새제거 혀백태제거 혀칫솔 i MinSellAmount 펀키보이"}, {"text": "[갤러리아] 폴리덴트 의치 부착재 민트향 70g x5개 한화갤러리아(주)"}, {"text": "애터미 치약 프로폴리스 200g 입냄새 제거 미백 콜마 플렉스세븐"}], "inference": true, "model-index": [{"name": "SetFit with mini1013/master_domain", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "metric", "value": 0.9477272727272728, "name": "Metric"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,949 |
CalvinHuang/mt5-small-finetuned-amazon-en-es
|
CalvinHuang
|
summarization
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2022-02-02T03:50:37+00:00
| 138 | 1 |
---
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0393
- Rouge1: 17.2936
- Rouge2: 8.0678
- Rougel: 16.8129
- Rougelsum: 16.9991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.6665 | 1.0 | 1209 | 3.2917 | 13.912 | 5.595 | 13.2984 | 13.4171 |
| 3.8961 | 2.0 | 2418 | 3.1711 | 16.2845 | 8.6033 | 15.5509 | 15.7383 |
| 3.5801 | 3.0 | 3627 | 3.0917 | 17.316 | 8.122 | 16.697 | 16.773 |
| 3.4258 | 4.0 | 4836 | 3.0583 | 16.1347 | 7.7829 | 15.6475 | 15.7804 |
| 3.3154 | 5.0 | 6045 | 3.0573 | 17.5918 | 8.7349 | 17.0537 | 17.2216 |
| 3.2438 | 6.0 | 7254 | 3.0479 | 17.2294 | 8.0383 | 16.8141 | 16.9858 |
| 3.2024 | 7.0 | 8463 | 3.0377 | 17.2918 | 8.139 | 16.8178 | 16.9671 |
| 3.1745 | 8.0 | 9672 | 3.0393 | 17.2936 | 8.0678 | 16.8129 | 16.9991 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0393
- Rouge1: 17.2936
- Rouge2: 8.0678
- Rougel: 16.8129
- Rougelsum: 16.9991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.6665 | 1.0 | 1209 | 3.2917 | 13.912 | 5.595 | 13.2984 | 13.4171 |
| 3.8961 | 2.0 | 2418 | 3.1711 | 16.2845 | 8.6033 | 15.5509 | 15.7383 |
| 3.5801 | 3.0 | 3627 | 3.0917 | 17.316 | 8.122 | 16.697 | 16.773 |
| 3.4258 | 4.0 | 4836 | 3.0583 | 16.1347 | 7.7829 | 15.6475 | 15.7804 |
| 3.3154 | 5.0 | 6045 | 3.0573 | 17.5918 | 8.7349 | 17.0537 | 17.2216 |
| 3.2438 | 6.0 | 7254 | 3.0479 | 17.2294 | 8.0383 | 16.8141 | 16.9858 |
| 3.2024 | 7.0 | 8463 | 3.0377 | 17.2918 | 8.139 | 16.8178 | 16.9671 |
| 3.1745 | 8.0 | 9672 | 3.0393 | 17.2936 | 8.0678 | 16.8129 | 16.9991 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "metrics": ["rouge"], "tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]}
|
task
|
[
"SUMMARIZATION"
] | 41,950 |
fine-tuned/jinaai_jina-embeddings-v2-base-de-922024-pwti-webapp
|
fine-tuned
|
feature-extraction
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Academic",
"Research",
"Papers",
"Information",
"System",
"custom_code",
"en",
"dataset:fine-tuned/jinaai_jina-embeddings-v2-base-de-922024-pwti-webapp",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-02T09:01:17Z |
2024-09-02T09:01:34+00:00
| 6 | 0 |
---
datasets:
- fine-tuned/jinaai_jina-embeddings-v2-base-de-922024-pwti-webapp
- allenai/c4
language:
- en
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Academic
- Research
- Papers
- Information
- System
---
This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-de**](https://huggingface.co/jinaai/jina-embeddings-v2-base-de) designed for the following use case:
information retrieval system for academic research papers
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/jinaai_jina-embeddings-v2-base-de-922024-pwti-webapp',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
| null |
Non_BioNLP
|
This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-de**](https://huggingface.co/jinaai/jina-embeddings-v2-base-de) designed for the following use case:
information retrieval system for academic research papers
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/jinaai_jina-embeddings-v2-base-de-922024-pwti-webapp',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
{"datasets": ["fine-tuned/jinaai_jina-embeddings-v2-base-de-922024-pwti-webapp", "allenai/c4"], "language": ["en"], "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "Academic", "Research", "Papers", "Information", "System"]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,951 |
neuralmagic/granite-3.1-8b-instruct-FP8-dynamic
|
neuralmagic
|
text-generation
|
[
"transformers",
"safetensors",
"granite",
"text-generation",
"fp8",
"vllm",
"conversational",
"en",
"base_model:ibm-granite/granite-3.1-8b-instruct",
"base_model:quantized:ibm-granite/granite-3.1-8b-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] | 2025-01-07T18:43:34Z |
2025-01-25T01:27:16+00:00
| 43 | 1 |
---
base_model: ibm-granite/granite-3.1-8b-instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
tags:
- fp8
- vllm
---
# granite-3.1-8b-instruct-FP8-dynamic
## Model Overview
- **Model Architecture:** granite-3.1-8b-instruct
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 1/8/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
It achieves an average score of 70.57 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 70.30.
### Model Optimizations
This model was obtained by quantizing the weights and activations of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 4096, 1
model_name = "neuralmagic/granite-3.1-8b-instruct-FP8-dynamic"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
<details>
<summary>Model Creation Code</summary>
```bash
python quantize.py --model_id ibm-granite/granite-3.1-8b-instruct --save_path "output_dir/"
```
```python
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
import os
def main():
parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
parser.add_argument('--model_id', type=str, required=True,
help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
parser.add_argument('--save_path', type=str, default='.',
help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
args = parser.parse_args()
# Load model
model = AutoModelForCausalLM.from_pretrained(
args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_id)
# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)
# Apply quantization
oneshot(model=model, recipe=recipe)
save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
os.makedirs(save_path, exist_ok=True)
# Save to disk in compressed-tensors format
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")
if __name__ == "__main__":
main()
```
</details>
## Evaluation
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
<details>
<summary>Evaluation Commands</summary>
OpenLLM Leaderboard V1:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/granite-3.1-8b-instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks openllm \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
OpenLLM Leaderboard V2:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/granite-3.1-8b-instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks leaderboard \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
#### HumanEval
##### Generation
```
python3 codegen/generate.py \
--model neuralmagic/granite-3.1-8b-instruct-FP8-dynamic \
--bs 16 \
--temperature 0.2 \
--n_samples 50 \
--root "." \
--dataset humaneval
```
##### Sanitization
```
python3 evalplus/sanitize.py \
humaneval/neuralmagic--granite-3.1-8b-instruct-FP8-dynamic_vllm_temp_0.2
```
##### Evaluation
```
evalplus.evaluate \
--dataset humaneval \
--samples humaneval/neuralmagic--granite-3.1-8b-instruct-FP8-dynamic_vllm_temp_0.2-sanitized
```
</details>
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>ibm-granite/granite-3.1-8b-instruct</th>
<th>neuralmagic/granite-3.1-8b-instruct-FP8-dynamic</th>
<th>Recovery (%)</th>
</tr>
</thead>
<tbody>
<!-- OpenLLM Leaderboard V1 -->
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>66.81</td>
<td>66.81</td>
<td>100.00</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>64.52</td>
<td>66.64</td>
<td>103.29</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>84.18</td>
<td>84.16</td>
<td>99.98</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>65.52</td>
<td>65.36</td>
<td>99.76</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>60.57</td>
<td>60.52</td>
<td>99.92</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>80.19</td>
<td>79.95</td>
<td>99.70</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>70.30</b></td>
<td><b>70.57</b></td>
<td><b>100.39</b></td>
</tr>
<!-- OpenLLM Leaderboard V2 -->
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>74.10</td>
<td>73.62</td>
<td>99.35</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>53.19</td>
<td>53.26</td>
<td>100.13</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>14.77</td>
<td>16.79</td>
<td>113.66</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>31.76</td>
<td>32.58</td>
<td>102.58</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.01</td>
<td>47.34</td>
<td>102.89</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>35.81</td>
<td>35.72</td>
<td>99.75</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>42.61</b></td>
<td><b>43.22</b></td>
<td><b>101.43</b></td>
</tr>
<!-- HumanEval -->
<tr>
<td rowspan="2"><b>Coding</b></td>
<td>HumanEval Pass@1</td>
<td>71.00</td>
<td>69.90</td>
<td><b>98.45</b></td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.5x speedup in single-stream deployment and up to 1.1x speedup in multi-stream asynchronous deployment on L40 GPUs.
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/granite-3.1-8b-instruct-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
```
</details>
### Single-stream performance (measured with vLLM version 0.6.6.post1)
<table>
<tr>
<td></td>
<td></td>
<td></td>
<th style="text-align: center;" colspan="7" >Latency (s)</th>
</tr>
<tr>
<th>GPU class</th>
<th>Model</th>
<th>Speedup</th>
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >L40</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>25.1</td>
<td>3.2</td>
<td>25.3</td>
<td>3.2</td>
<td>3.2</td>
<td>6.3</td>
<td>13.4</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-FP8-dynamic<br>(this model)</td>
<td>1.47</td>
<td>16.8</td>
<td>2.2</td>
<td>17.1</td>
<td>2.2</td>
<td>2.1</td>
<td>4.2</td>
<td>9.3</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16</td>
<td>2.72</td>
<td>8.9</td>
<td>1.2</td>
<td>9.2</td>
<td>1.2</td>
<td>1.1</td>
<td>2.3</td>
<td>5.3</td>
</tr>
</table>
### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1)
<table>
<tr>
<td></td>
<td></td>
<td></td>
<th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th>
</tr>
<tr>
<th>GPU class</th>
<th>Model</th>
<th>Speedup</th>
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >L40</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>1.4</td>
<td>7.8</td>
<td>1.1</td>
<td>6.2</td>
<td>15.5</td>
<td>6.0</td>
<td>0.7</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-FP8-dynamic<br>(this model)</td>
<td>1.12</td>
<td>2.1</td>
<td>7.4</td>
<td>1.3</td>
<td>5.9</td>
<td>15.3</td>
<td>6.9</td>
<td>0.8</td>
</tr>
<tr>
<td>granite-3.1-2b-instruct-quantized.w4a16</td>
<td>1.29</td>
<td>2.4</td>
<td>8.9</td>
<td>1.4</td>
<td>7.1</td>
<td>17.8</td>
<td>7.8</td>
<td>1.0</td>
</tr>
</table>
| null |
Non_BioNLP
|
# granite-3.1-8b-instruct-FP8-dynamic
## Model Overview
- **Model Architecture:** granite-3.1-8b-instruct
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 1/8/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
It achieves an average score of 70.57 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 70.30.
### Model Optimizations
This model was obtained by quantizing the weights and activations of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 4096, 1
model_name = "neuralmagic/granite-3.1-8b-instruct-FP8-dynamic"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
<details>
<summary>Model Creation Code</summary>
```bash
python quantize.py --model_id ibm-granite/granite-3.1-8b-instruct --save_path "output_dir/"
```
```python
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
import os
def main():
parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
parser.add_argument('--model_id', type=str, required=True,
help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
parser.add_argument('--save_path', type=str, default='.',
help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
args = parser.parse_args()
# Load model
model = AutoModelForCausalLM.from_pretrained(
args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_id)
# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)
# Apply quantization
oneshot(model=model, recipe=recipe)
save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
os.makedirs(save_path, exist_ok=True)
# Save to disk in compressed-tensors format
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")
if __name__ == "__main__":
main()
```
</details>
## Evaluation
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
<details>
<summary>Evaluation Commands</summary>
OpenLLM Leaderboard V1:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/granite-3.1-8b-instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks openllm \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
OpenLLM Leaderboard V2:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/granite-3.1-8b-instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks leaderboard \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
#### HumanEval
##### Generation
```
python3 codegen/generate.py \
--model neuralmagic/granite-3.1-8b-instruct-FP8-dynamic \
--bs 16 \
--temperature 0.2 \
--n_samples 50 \
--root "." \
--dataset humaneval
```
##### Sanitization
```
python3 evalplus/sanitize.py \
humaneval/neuralmagic--granite-3.1-8b-instruct-FP8-dynamic_vllm_temp_0.2
```
##### Evaluation
```
evalplus.evaluate \
--dataset humaneval \
--samples humaneval/neuralmagic--granite-3.1-8b-instruct-FP8-dynamic_vllm_temp_0.2-sanitized
```
</details>
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>ibm-granite/granite-3.1-8b-instruct</th>
<th>neuralmagic/granite-3.1-8b-instruct-FP8-dynamic</th>
<th>Recovery (%)</th>
</tr>
</thead>
<tbody>
<!-- OpenLLM Leaderboard V1 -->
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>66.81</td>
<td>66.81</td>
<td>100.00</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>64.52</td>
<td>66.64</td>
<td>103.29</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>84.18</td>
<td>84.16</td>
<td>99.98</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>65.52</td>
<td>65.36</td>
<td>99.76</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>60.57</td>
<td>60.52</td>
<td>99.92</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>80.19</td>
<td>79.95</td>
<td>99.70</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>70.30</b></td>
<td><b>70.57</b></td>
<td><b>100.39</b></td>
</tr>
<!-- OpenLLM Leaderboard V2 -->
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>74.10</td>
<td>73.62</td>
<td>99.35</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>53.19</td>
<td>53.26</td>
<td>100.13</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>14.77</td>
<td>16.79</td>
<td>113.66</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>31.76</td>
<td>32.58</td>
<td>102.58</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.01</td>
<td>47.34</td>
<td>102.89</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>35.81</td>
<td>35.72</td>
<td>99.75</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>42.61</b></td>
<td><b>43.22</b></td>
<td><b>101.43</b></td>
</tr>
<!-- HumanEval -->
<tr>
<td rowspan="2"><b>Coding</b></td>
<td>HumanEval Pass@1</td>
<td>71.00</td>
<td>69.90</td>
<td><b>98.45</b></td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.5x speedup in single-stream deployment and up to 1.1x speedup in multi-stream asynchronous deployment on L40 GPUs.
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/granite-3.1-8b-instruct-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
```
</details>
### Single-stream performance (measured with vLLM version 0.6.6.post1)
<table>
<tr>
<td></td>
<td></td>
<td></td>
<th style="text-align: center;" colspan="7" >Latency (s)</th>
</tr>
<tr>
<th>GPU class</th>
<th>Model</th>
<th>Speedup</th>
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >L40</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>25.1</td>
<td>3.2</td>
<td>25.3</td>
<td>3.2</td>
<td>3.2</td>
<td>6.3</td>
<td>13.4</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-FP8-dynamic<br>(this model)</td>
<td>1.47</td>
<td>16.8</td>
<td>2.2</td>
<td>17.1</td>
<td>2.2</td>
<td>2.1</td>
<td>4.2</td>
<td>9.3</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16</td>
<td>2.72</td>
<td>8.9</td>
<td>1.2</td>
<td>9.2</td>
<td>1.2</td>
<td>1.1</td>
<td>2.3</td>
<td>5.3</td>
</tr>
</table>
### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1)
<table>
<tr>
<td></td>
<td></td>
<td></td>
<th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th>
</tr>
<tr>
<th>GPU class</th>
<th>Model</th>
<th>Speedup</th>
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >L40</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>1.4</td>
<td>7.8</td>
<td>1.1</td>
<td>6.2</td>
<td>15.5</td>
<td>6.0</td>
<td>0.7</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-FP8-dynamic<br>(this model)</td>
<td>1.12</td>
<td>2.1</td>
<td>7.4</td>
<td>1.3</td>
<td>5.9</td>
<td>15.3</td>
<td>6.9</td>
<td>0.8</td>
</tr>
<tr>
<td>granite-3.1-2b-instruct-quantized.w4a16</td>
<td>1.29</td>
<td>2.4</td>
<td>8.9</td>
<td>1.4</td>
<td>7.1</td>
<td>17.8</td>
<td>7.8</td>
<td>1.0</td>
</tr>
</table>
|
{"base_model": "ibm-granite/granite-3.1-8b-instruct", "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_link": "https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md", "tags": ["fp8", "vllm"]}
|
task
|
[
"SUMMARIZATION"
] | 41,952 |
Helsinki-NLP/opus-mt-bg-eo
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"bg",
"eo",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T11:26:15+00:00
| 42 | 0 |
---
language:
- bg
- eo
license: apache-2.0
tags:
- translation
---
### bul-epo
* source group: Bulgarian
* target group: Esperanto
* OPUS readme: [bul-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md)
* model: transformer-align
* source language(s): bul
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.epo | 24.5 | 0.438 |
### System Info:
- hf_name: bul-epo
- source_languages: bul
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'eo']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.test.txt
- src_alpha3: bul
- tgt_alpha3: epo
- short_pair: bg-eo
- chrF2_score: 0.43799999999999994
- bleu: 24.5
- brevity_penalty: 0.9670000000000001
- ref_len: 4043.0
- src_name: Bulgarian
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: bg
- tgt_alpha2: eo
- prefer_old: False
- long_pair: bul-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
| null |
Non_BioNLP
|
### bul-epo
* source group: Bulgarian
* target group: Esperanto
* OPUS readme: [bul-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md)
* model: transformer-align
* source language(s): bul
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bul.epo | 24.5 | 0.438 |
### System Info:
- hf_name: bul-epo
- source_languages: bul
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bul-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['bg', 'eo']
- src_constituents: {'bul', 'bul_Latn'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/bul-epo/opus-2020-06-16.test.txt
- src_alpha3: bul
- tgt_alpha3: epo
- short_pair: bg-eo
- chrF2_score: 0.43799999999999994
- bleu: 24.5
- brevity_penalty: 0.9670000000000001
- ref_len: 4043.0
- src_name: Bulgarian
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: bg
- tgt_alpha2: eo
- prefer_old: False
- long_pair: bul-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
{"language": ["bg", "eo"], "license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,953 |
paulrojasg/NER-finetuned-BETO
|
paulrojasg
|
token-classification
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"es",
"dataset:eriktks/conll2002",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-05T08:40:10Z |
2024-06-05T09:34:38+00:00
| 4 | 0 |
---
datasets:
- eriktks/conll2002
language:
- es
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
---
# Model Name: NER-finetuned-BETO
This is a BERT model fine-tuned for Named Entity Recognition (NER).
# Model Description
This is a fine-tuned BERT model for Named Entity Recognition (NER) task using CONLL2002 dataset.
In the first part, the dataset must be pre-processed in order to give it to the model. This is done using the 🤗 Transformers and BERT tokenizers. Once this is done, finetuning is applied from *[bert-base-cased](https://huggingface.co/google-bert/bert-base-cased)* and using the 🤗 *AutoModelForTokenClassification*.
Finally, the model is trained obtaining the neccesary metrics for evaluating its performance (Precision, Recall, F1 and Accuracy)
Summary of executed tests can be found in: https://docs.google.com/spreadsheets/d/1lI7skNIvRurwq3LA5ps7JFK5TxToEx4s7Kaah3ezyQc/edit?usp=sharing
Model can be found in: https://huggingface.co/paulrojasg/NER-finetuned-BETO
Github repository: https://github.com/paulrojasg/nlp_4th_workshop
# Training
## Training Details
- Epochs: 10
- Learning Rate: 2e-05
- Weight Decay: 0.01
- Batch Size (Train): 16
- Batch Size (Eval): 8
## Training Metrics
| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy |
|:----:|:-------------:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 1 | 0.0065| 0.2077 | 0.8436 | 0.8564 | 0.8499 | 0.9712 |
| 2 | 0.0062| 0.2345 | 0.8318 | 0.8513 | 0.8415 | 0.9683 |
| 3 | 0.0069| 0.2156 | 0.8464 | 0.8470 | 0.8467 | 0.9674 |
| 4 | 0.0064| 0.2189 | 0.8356 | 0.8490 | 0.8423 | 0.9686 |
| 5 | 0.0055| 0.2383 | 0.8373 | 0.8488 | 0.8430 | 0.9687 |
| 6 | 0.0050| 0.2315 | 0.8334 | 0.8543 | 0.8438 | 0.9694 |
| 7 | 0.0037| 0.2343 | 0.8428 | 0.8573 | 0.8500 | 0.9703 |
| 8 | 0.0031| 0.2493 | 0.8400 | 0.8555 | 0.8477 | 0.9694 |
| 9 | 0.0024| 0.2421 | 0.8478 | 0.8617 | 0.8547 | 0.9704 |
| 10 | 0.0023| 0.2497 | 0.8432 | 0.8598 | 0.8514 | 0.9703 |
# Authors
Made by:
- Paul Rodrigo Rojas Guerrero
- Jose Luis Hincapie Bucheli
- Sebastián Idrobo Avirama
With help from:
- [Raúl Ernesto Gutiérrez](https://huggingface.co/raulgdp)
| null |
Non_BioNLP
|
# Model Name: NER-finetuned-BETO
This is a BERT model fine-tuned for Named Entity Recognition (NER).
# Model Description
This is a fine-tuned BERT model for Named Entity Recognition (NER) task using CONLL2002 dataset.
In the first part, the dataset must be pre-processed in order to give it to the model. This is done using the 🤗 Transformers and BERT tokenizers. Once this is done, finetuning is applied from *[bert-base-cased](https://huggingface.co/google-bert/bert-base-cased)* and using the 🤗 *AutoModelForTokenClassification*.
Finally, the model is trained obtaining the neccesary metrics for evaluating its performance (Precision, Recall, F1 and Accuracy)
Summary of executed tests can be found in: https://docs.google.com/spreadsheets/d/1lI7skNIvRurwq3LA5ps7JFK5TxToEx4s7Kaah3ezyQc/edit?usp=sharing
Model can be found in: https://huggingface.co/paulrojasg/NER-finetuned-BETO
Github repository: https://github.com/paulrojasg/nlp_4th_workshop
# Training
## Training Details
- Epochs: 10
- Learning Rate: 2e-05
- Weight Decay: 0.01
- Batch Size (Train): 16
- Batch Size (Eval): 8
## Training Metrics
| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy |
|:----:|:-------------:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 1 | 0.0065| 0.2077 | 0.8436 | 0.8564 | 0.8499 | 0.9712 |
| 2 | 0.0062| 0.2345 | 0.8318 | 0.8513 | 0.8415 | 0.9683 |
| 3 | 0.0069| 0.2156 | 0.8464 | 0.8470 | 0.8467 | 0.9674 |
| 4 | 0.0064| 0.2189 | 0.8356 | 0.8490 | 0.8423 | 0.9686 |
| 5 | 0.0055| 0.2383 | 0.8373 | 0.8488 | 0.8430 | 0.9687 |
| 6 | 0.0050| 0.2315 | 0.8334 | 0.8543 | 0.8438 | 0.9694 |
| 7 | 0.0037| 0.2343 | 0.8428 | 0.8573 | 0.8500 | 0.9703 |
| 8 | 0.0031| 0.2493 | 0.8400 | 0.8555 | 0.8477 | 0.9694 |
| 9 | 0.0024| 0.2421 | 0.8478 | 0.8617 | 0.8547 | 0.9704 |
| 10 | 0.0023| 0.2497 | 0.8432 | 0.8598 | 0.8514 | 0.9703 |
# Authors
Made by:
- Paul Rodrigo Rojas Guerrero
- Jose Luis Hincapie Bucheli
- Sebastián Idrobo Avirama
With help from:
- [Raúl Ernesto Gutiérrez](https://huggingface.co/raulgdp)
|
{"datasets": ["eriktks/conll2002"], "language": ["es"], "license": "apache-2.0", "metrics": ["precision", "recall", "f1", "accuracy"], "pipeline_tag": "token-classification"}
|
task
|
[
"NAMED_ENTITY_RECOGNITION"
] | 41,954 |
Realgon/N_bert_agnews_padding30model
|
Realgon
|
text-classification
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-12-27T12:15:46Z |
2023-12-27T14:36:31+00:00
| 26 | 0 |
---
base_model: bert-base-uncased
datasets:
- ag_news
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: N_bert_agnews_padding30model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- type: accuracy
value: 0.9464473684210526
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_bert_agnews_padding30model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5638
- Accuracy: 0.9464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1818 | 1.0 | 7500 | 0.1926 | 0.9422 |
| 0.1395 | 2.0 | 15000 | 0.2087 | 0.9454 |
| 0.1138 | 3.0 | 22500 | 0.2287 | 0.9446 |
| 0.0858 | 4.0 | 30000 | 0.2681 | 0.9475 |
| 0.0569 | 5.0 | 37500 | 0.2953 | 0.9451 |
| 0.0421 | 6.0 | 45000 | 0.3934 | 0.9408 |
| 0.0363 | 7.0 | 52500 | 0.3943 | 0.9408 |
| 0.0283 | 8.0 | 60000 | 0.4069 | 0.9414 |
| 0.0165 | 9.0 | 67500 | 0.4448 | 0.9433 |
| 0.0142 | 10.0 | 75000 | 0.4708 | 0.9445 |
| 0.0134 | 11.0 | 82500 | 0.4708 | 0.9432 |
| 0.0089 | 12.0 | 90000 | 0.5035 | 0.9414 |
| 0.0083 | 13.0 | 97500 | 0.5031 | 0.9430 |
| 0.0064 | 14.0 | 105000 | 0.4990 | 0.9432 |
| 0.0046 | 15.0 | 112500 | 0.5265 | 0.945 |
| 0.0032 | 16.0 | 120000 | 0.5370 | 0.9449 |
| 0.0035 | 17.0 | 127500 | 0.5445 | 0.9447 |
| 0.0018 | 18.0 | 135000 | 0.5548 | 0.9462 |
| 0.0031 | 19.0 | 142500 | 0.5627 | 0.9454 |
| 0.0002 | 20.0 | 150000 | 0.5638 | 0.9464 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_bert_agnews_padding30model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5638
- Accuracy: 0.9464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1818 | 1.0 | 7500 | 0.1926 | 0.9422 |
| 0.1395 | 2.0 | 15000 | 0.2087 | 0.9454 |
| 0.1138 | 3.0 | 22500 | 0.2287 | 0.9446 |
| 0.0858 | 4.0 | 30000 | 0.2681 | 0.9475 |
| 0.0569 | 5.0 | 37500 | 0.2953 | 0.9451 |
| 0.0421 | 6.0 | 45000 | 0.3934 | 0.9408 |
| 0.0363 | 7.0 | 52500 | 0.3943 | 0.9408 |
| 0.0283 | 8.0 | 60000 | 0.4069 | 0.9414 |
| 0.0165 | 9.0 | 67500 | 0.4448 | 0.9433 |
| 0.0142 | 10.0 | 75000 | 0.4708 | 0.9445 |
| 0.0134 | 11.0 | 82500 | 0.4708 | 0.9432 |
| 0.0089 | 12.0 | 90000 | 0.5035 | 0.9414 |
| 0.0083 | 13.0 | 97500 | 0.5031 | 0.9430 |
| 0.0064 | 14.0 | 105000 | 0.4990 | 0.9432 |
| 0.0046 | 15.0 | 112500 | 0.5265 | 0.945 |
| 0.0032 | 16.0 | 120000 | 0.5370 | 0.9449 |
| 0.0035 | 17.0 | 127500 | 0.5445 | 0.9447 |
| 0.0018 | 18.0 | 135000 | 0.5548 | 0.9462 |
| 0.0031 | 19.0 | 142500 | 0.5627 | 0.9454 |
| 0.0002 | 20.0 | 150000 | 0.5638 | 0.9464 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
{"base_model": "bert-base-uncased", "datasets": ["ag_news"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "N_bert_agnews_padding30model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9464473684210526, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,955 |
VERSIL91/703d40a6-f8fb-409b-90fe-08640c43adfb
|
VERSIL91
| null |
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | 2024-12-24T12:18:39Z |
2024-12-24T12:23:50+00:00
| 1 | 0 |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
license: apache-2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 703d40a6-f8fb-409b-90fe-08640c43adfb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
accelerate_config:
dynamo_backend: inductor
mixed_precision: bf16
num_machines: 1
num_processes: auto
use_cpu: false
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ffeb9f962f13287e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ffeb9f962f13287e_train_data.json
type:
field_instruction: glosses
field_output: translation
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: VERSIL91/703d40a6-f8fb-409b-90fe-08640c43adfb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 70GiB
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ffeb9f962f13287e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
quantization_config:
llm_int8_enable_fp32_cpu_offload: true
load_in_8bit: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 703d40a6-f8fb-409b-90fe-08640c43adfb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 703d40a6-f8fb-409b-90fe-08640c43adfb
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 703d40a6-f8fb-409b-90fe-08640c43adfb
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5982
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.2274 | 0.0032 | 1 | 6.0508 |
| 5.7836 | 0.0096 | 3 | 5.9795 |
| 5.202 | 0.0192 | 6 | 5.8843 |
| 5.3865 | 0.0288 | 9 | 5.5982 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
accelerate_config:
dynamo_backend: inductor
mixed_precision: bf16
num_machines: 1
num_processes: auto
use_cpu: false
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ffeb9f962f13287e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ffeb9f962f13287e_train_data.json
type:
field_instruction: glosses
field_output: translation
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: VERSIL91/703d40a6-f8fb-409b-90fe-08640c43adfb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 70GiB
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ffeb9f962f13287e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
quantization_config:
llm_int8_enable_fp32_cpu_offload: true
load_in_8bit: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 703d40a6-f8fb-409b-90fe-08640c43adfb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 703d40a6-f8fb-409b-90fe-08640c43adfb
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 703d40a6-f8fb-409b-90fe-08640c43adfb
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5982
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.2274 | 0.0032 | 1 | 6.0508 |
| 5.7836 | 0.0096 | 3 | 5.9795 |
| 5.202 | 0.0192 | 6 | 5.8843 |
| 5.3865 | 0.0288 | 9 | 5.5982 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
{"base_model": "Qwen/Qwen2.5-Coder-7B-Instruct", "library_name": "peft", "license": "apache-2.0", "tags": ["axolotl", "generated_from_trainer"], "model-index": [{"name": "703d40a6-f8fb-409b-90fe-08640c43adfb", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 41,956 |
tmnam20/xlm-roberta-base-wnli-100
|
tmnam20
|
text-classification
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"en",
"dataset:tmnam20/VieGLUE",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-01-16T11:40:34Z |
2024-01-16T11:42:23+00:00
| 6 | 0 |
---
base_model: xlm-roberta-base
datasets:
- tmnam20/VieGLUE
language:
- en
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-wnli-100
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tmnam20/VieGLUE/WNLI
type: tmnam20/VieGLUE
config: wnli
split: validation
args: wnli
metrics:
- type: accuracy
value: 0.5633802816901409
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-wnli-100
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6868
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-wnli-100
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tmnam20/VieGLUE/WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6868
- Accuracy: 0.5634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
{"base_model": "xlm-roberta-base", "datasets": ["tmnam20/VieGLUE"], "language": ["en"], "license": "mit", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-wnli-100", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tmnam20/VieGLUE/WNLI", "type": "tmnam20/VieGLUE", "config": "wnli", "split": "validation", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,958 |
jimmyhezhang/distilbert-base-uncased-finetuned-emotion
|
jimmyhezhang
|
text-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-04-05T19:05:21Z |
2023-04-05T20:41:54+00:00
| 11 | 0 |
---
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.924
name: Accuracy
- type: f1
value: 0.9240733671679012
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2123
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7932 | 1.0 | 250 | 0.2895 | 0.915 | 0.9138 |
| 0.238 | 2.0 | 500 | 0.2123 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2123
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7932 | 1.0 | 250 | 0.2895 | 0.915 | 0.9138 |
| 0.238 | 2.0 | 500 | 0.2123 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
|
{"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.924, "name": "Accuracy"}, {"type": "f1", "value": 0.9240733671679012, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,959 |
ESGBERT/EnvironmentalBERT-forest
|
ESGBERT
|
text-classification
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"ESG",
"environmental",
"forest",
"en",
"dataset:ESGBERT/WaterForestBiodiversityNature_2200",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-12-21T05:54:05Z |
2024-01-14T15:52:49+00:00
| 142 | 0 |
---
datasets:
- ESGBERT/WaterForestBiodiversityNature_2200
language: en
license: apache-2.0
tags:
- ESG
- environmental
- forest
---
# Model Card for EnvironmentalBERT-water
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715), this is the EnvironmentalBERT-forest language model. A language model that is trained to better classify forest texts in the ESG/nature domain.
Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-forest Language Model is additionally fine-trained on a 2.2k forest dataset to detect forest text samples.
## How to Get Started With the Model
See these tutorials on Medium for a guide on [model usage](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-1-report-analysis-towards-esg-risks-and-opportunities-8daa2695f6c5?source=friends_link&sk=423e30ac2f50ee4695d258c2c4d54aa5), [large-scale analysis](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-2-large-scale-analyses-of-environmental-actions-0735cc8dc9c2?source=friends_link&sk=13a5aa1999fbb11e9eed4a0c26c40efa), and [fine-tuning](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-3-fine-tune-your-own-models-e3692fc0b3c0?source=friends_link&sk=49dc9f00768e43242fc1a76aa0969c70).
It is highly recommended to first classify a sentence to be "environmental" or not with the [EnvironmentalBERT-environmental](https://huggingface.co/ESGBERT/EnvironmentalBERT-environmental) model before classifying whether it is "forest" or not.
You can use the model with a pipeline for text classification:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
tokenizer_name = "ESGBERT/EnvironmentalBERT-forest"
model_name = "ESGBERT/EnvironmentalBERT-forest"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) # set device=0 to use GPU
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
print(pipe("A large portion of trees in the Amazonas is dying each year.", padding=True, truncation=True))
```
## More details can be found in the paper
```bibtex
@article{Schimanski23ExploringNature,
title={{Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures}},
author={Tobias Schimanski and Chiara Colesanti Senni and Glen Gostlow and Jingwei Ni and Tingyu Yu and Markus Leippold},
year={2023},
journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715},
}
```
| null |
Non_BioNLP
|
# Model Card for EnvironmentalBERT-water
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715), this is the EnvironmentalBERT-forest language model. A language model that is trained to better classify forest texts in the ESG/nature domain.
Using the [EnvironmentalBERT-base](https://huggingface.co/ESGBERT/EnvironmentalBERT-base) model as a starting point, the EnvironmentalBERT-forest Language Model is additionally fine-trained on a 2.2k forest dataset to detect forest text samples.
## How to Get Started With the Model
See these tutorials on Medium for a guide on [model usage](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-1-report-analysis-towards-esg-risks-and-opportunities-8daa2695f6c5?source=friends_link&sk=423e30ac2f50ee4695d258c2c4d54aa5), [large-scale analysis](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-2-large-scale-analyses-of-environmental-actions-0735cc8dc9c2?source=friends_link&sk=13a5aa1999fbb11e9eed4a0c26c40efa), and [fine-tuning](https://medium.com/@schimanski.tobi/analyzing-esg-with-ai-and-nlp-tutorial-3-fine-tune-your-own-models-e3692fc0b3c0?source=friends_link&sk=49dc9f00768e43242fc1a76aa0969c70).
It is highly recommended to first classify a sentence to be "environmental" or not with the [EnvironmentalBERT-environmental](https://huggingface.co/ESGBERT/EnvironmentalBERT-environmental) model before classifying whether it is "forest" or not.
You can use the model with a pipeline for text classification:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
tokenizer_name = "ESGBERT/EnvironmentalBERT-forest"
model_name = "ESGBERT/EnvironmentalBERT-forest"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) # set device=0 to use GPU
# See https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
print(pipe("A large portion of trees in the Amazonas is dying each year.", padding=True, truncation=True))
```
## More details can be found in the paper
```bibtex
@article{Schimanski23ExploringNature,
title={{Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures}},
author={Tobias Schimanski and Chiara Colesanti Senni and Glen Gostlow and Jingwei Ni and Tingyu Yu and Markus Leippold},
year={2023},
journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4665715},
}
```
|
{"datasets": ["ESGBERT/WaterForestBiodiversityNature_2200"], "language": "en", "license": "apache-2.0", "tags": ["ESG", "environmental", "forest"]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,960 |
klcsp/llama2-13b-lora-summarization-11-v1
|
klcsp
| null |
[
"peft",
"tensorboard",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:adapter:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | 2024-11-22T17:06:58Z |
2024-11-22T17:22:57+00:00
| 7 | 0 |
---
base_model: meta-llama/Llama-2-13b-hf
datasets:
- generator
library_name: peft
license: llama2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama2-13b-lora-summarization-11-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2-13b-lora-summarization-11-v1
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4849 | 0.9925 | 66 | 2.0513 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama2-13b-lora-summarization-11-v1
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4849 | 0.9925 | 66 | 2.0513 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
{"base_model": "meta-llama/Llama-2-13b-hf", "datasets": ["generator"], "library_name": "peft", "license": "llama2", "tags": ["trl", "sft", "generated_from_trainer"], "model-index": [{"name": "llama2-13b-lora-summarization-11-v1", "results": []}]}
|
task
|
[
"SUMMARIZATION"
] | 41,961 |
Suchandra/bengali_language_NER
|
Suchandra
|
token-classification
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"bn",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05Z |
2023-06-05T10:08:42+00:00
| 25 | 1 |
---
datasets:
- wikiann
language: bn
widget:
- text: মারভিন দি মারসিয়ান
example_title: Sentence_1
- text: লিওনার্দো দা ভিঞ্চি
example_title: Sentence_2
- text: বসনিয়া ও হার্জেগোভিনা
example_title: Sentence_3
- text: সাউথ ইস্ট ইউনিভার্সিটি
example_title: Sentence_4
- text: মানিক বন্দ্যোপাধ্যায় লেখক
example_title: Sentence_5
---
<h1>Bengali Named Entity Recognition</h1>
Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.
## Label ID and its corresponding label name
| Label ID | Label Name|
| -------- | ----- |
|0 | O |
| 1 | B-PER |
| 2 | I-PER |
| 3 | B-ORG|
| 4 | I-ORG |
| 5 | B-LOC |
| 6 | I-LOC |
<h1>Results</h1>
| Name | Overall F1 | LOC F1 | ORG F1 | PER F1 |
| ---- | -------- | ----- | ---- | ---- |
| Train set | 0.997927 | 0.998246 | 0.996613 | 0.998769 |
| Validation set | 0.970187 | 0.969212 | 0.956831 | 0.982079 |
| Test set | 0.9673011 | 0.967120 | 0.963614 | 0.970938 |
Example
```py
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Suchandra/bengali_language_NER")
model = AutoModelForTokenClassification.from_pretrained("Suchandra/bengali_language_NER")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "মারভিন দি মারসিয়ান"
ner_results = nlp(example)
ner_results
```
| null |
Non_BioNLP
|
<h1>Bengali Named Entity Recognition</h1>
Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.
## Label ID and its corresponding label name
| Label ID | Label Name|
| -------- | ----- |
|0 | O |
| 1 | B-PER |
| 2 | I-PER |
| 3 | B-ORG|
| 4 | I-ORG |
| 5 | B-LOC |
| 6 | I-LOC |
<h1>Results</h1>
| Name | Overall F1 | LOC F1 | ORG F1 | PER F1 |
| ---- | -------- | ----- | ---- | ---- |
| Train set | 0.997927 | 0.998246 | 0.996613 | 0.998769 |
| Validation set | 0.970187 | 0.969212 | 0.956831 | 0.982079 |
| Test set | 0.9673011 | 0.967120 | 0.963614 | 0.970938 |
Example
```py
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("Suchandra/bengali_language_NER")
model = AutoModelForTokenClassification.from_pretrained("Suchandra/bengali_language_NER")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "মারভিন দি মারসিয়ান"
ner_results = nlp(example)
ner_results
```
|
{"datasets": ["wikiann"], "language": "bn", "widget": [{"text": "মারভিন দি মারসিয়ান", "example_title": "Sentence_1"}, {"text": "লিওনার্দো দা ভিঞ্চি", "example_title": "Sentence_2"}, {"text": "বসনিয়া ও হার্জেগোভিনা", "example_title": "Sentence_3"}, {"text": "সাউথ ইস্ট ইউনিভার্সিটি", "example_title": "Sentence_4"}, {"text": "মানিক বন্দ্যোপাধ্যায় লেখক", "example_title": "Sentence_5"}]}
|
task
|
[
"NAMED_ENTITY_RECOGNITION"
] | 41,964 |
henryscheible/bert-base-uncased_crows_pairs_finetuned
|
henryscheible
|
text-classification
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:crows_pairs",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-03-12T03:49:48Z |
2023-03-12T04:05:06+00:00
| 17 | 0 |
---
datasets:
- crows_pairs
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased_crows_pairs_finetuned
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: crows_pairs
type: crows_pairs
config: crows_pairs
split: test
args: crows_pairs
metrics:
- type: accuracy
value: 0.7649006622516556
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased_crows_pairs_finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1731
- Accuracy: 0.7649
- Tp: 0.3344
- Tn: 0.4305
- Fp: 0.1126
- Fn: 0.1225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.703 | 1.05 | 20 | 0.6889 | 0.5430 | 0.0 | 0.5430 | 0.0 | 0.4570 |
| 0.6884 | 2.11 | 40 | 0.6886 | 0.5 | 0.3013 | 0.1987 | 0.3444 | 0.1556 |
| 0.5388 | 3.16 | 60 | 0.6347 | 0.7053 | 0.1821 | 0.5232 | 0.0199 | 0.2748 |
| 0.2228 | 4.21 | 80 | 0.9802 | 0.6987 | 0.1887 | 0.5099 | 0.0331 | 0.2682 |
| 0.1644 | 5.26 | 100 | 0.7523 | 0.7583 | 0.3675 | 0.3907 | 0.1523 | 0.0894 |
| 0.0478 | 6.32 | 120 | 1.5712 | 0.6954 | 0.2053 | 0.4901 | 0.0530 | 0.2517 |
| 0.0465 | 7.37 | 140 | 1.2587 | 0.7351 | 0.2781 | 0.4570 | 0.0861 | 0.1788 |
| 0.0313 | 8.42 | 160 | 1.5825 | 0.7450 | 0.3775 | 0.3675 | 0.1755 | 0.0795 |
| 0.0137 | 9.47 | 180 | 1.3570 | 0.7318 | 0.2815 | 0.4503 | 0.0927 | 0.1755 |
| 0.04 | 10.53 | 200 | 2.1377 | 0.6921 | 0.1887 | 0.5033 | 0.0397 | 0.2682 |
| 0.0041 | 11.58 | 220 | 1.6776 | 0.7351 | 0.3278 | 0.4073 | 0.1358 | 0.1291 |
| 0.0042 | 12.63 | 240 | 1.8873 | 0.7086 | 0.2980 | 0.4106 | 0.1325 | 0.1589 |
| 0.0009 | 13.68 | 260 | 2.2464 | 0.6987 | 0.3543 | 0.3444 | 0.1987 | 0.1026 |
| 0.014 | 14.74 | 280 | 1.9753 | 0.7252 | 0.3245 | 0.4007 | 0.1424 | 0.1325 |
| 0.0026 | 15.79 | 300 | 1.8852 | 0.7417 | 0.2914 | 0.4503 | 0.0927 | 0.1656 |
| 0.0147 | 16.84 | 320 | 2.0273 | 0.7351 | 0.3113 | 0.4238 | 0.1192 | 0.1457 |
| 0.0009 | 17.89 | 340 | 1.7328 | 0.7483 | 0.3278 | 0.4205 | 0.1225 | 0.1291 |
| 0.0085 | 18.95 | 360 | 2.0146 | 0.7450 | 0.2815 | 0.4636 | 0.0795 | 0.1755 |
| 0.0001 | 20.0 | 380 | 2.0808 | 0.7450 | 0.3113 | 0.4338 | 0.1093 | 0.1457 |
| 0.0001 | 21.05 | 400 | 2.2655 | 0.7417 | 0.3609 | 0.3808 | 0.1623 | 0.0960 |
| 0.0034 | 22.11 | 420 | 2.0298 | 0.7583 | 0.3079 | 0.4503 | 0.0927 | 0.1490 |
| 0.0082 | 23.16 | 440 | 2.0650 | 0.7550 | 0.3344 | 0.4205 | 0.1225 | 0.1225 |
| 0.0001 | 24.21 | 460 | 2.2472 | 0.7450 | 0.2748 | 0.4702 | 0.0728 | 0.1821 |
| 0.0001 | 25.26 | 480 | 2.3655 | 0.7351 | 0.3709 | 0.3642 | 0.1788 | 0.0861 |
| 0.0004 | 26.32 | 500 | 2.1407 | 0.7550 | 0.3510 | 0.4040 | 0.1391 | 0.1060 |
| 0.0001 | 27.37 | 520 | 2.1168 | 0.7450 | 0.3642 | 0.3808 | 0.1623 | 0.0927 |
| 0.0002 | 28.42 | 540 | 2.2050 | 0.7517 | 0.3775 | 0.3742 | 0.1689 | 0.0795 |
| 0.0 | 29.47 | 560 | 2.0560 | 0.7682 | 0.3212 | 0.4470 | 0.0960 | 0.1358 |
| 0.0 | 30.53 | 580 | 2.0859 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 31.58 | 600 | 2.0958 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 32.63 | 620 | 2.1039 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 33.68 | 640 | 2.1113 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 34.74 | 660 | 2.1180 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 35.79 | 680 | 2.1127 | 0.7715 | 0.3278 | 0.4437 | 0.0993 | 0.1291 |
| 0.0 | 36.84 | 700 | 2.1376 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 37.89 | 720 | 2.1460 | 0.7616 | 0.3377 | 0.4238 | 0.1192 | 0.1192 |
| 0.0 | 38.95 | 740 | 2.1507 | 0.7649 | 0.3377 | 0.4272 | 0.1159 | 0.1192 |
| 0.0 | 40.0 | 760 | 2.1548 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 41.05 | 780 | 2.1586 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 42.11 | 800 | 2.1620 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 43.16 | 820 | 2.1649 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 44.21 | 840 | 2.1674 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 45.26 | 860 | 2.1690 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 46.32 | 880 | 2.1705 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 47.37 | 900 | 2.1717 | 0.7649 | 0.3344 | 0.4305 | 0.1126 | 0.1225 |
| 0.0 | 48.42 | 920 | 2.1726 | 0.7649 | 0.3344 | 0.4305 | 0.1126 | 0.1225 |
| 0.0 | 49.47 | 940 | 2.1731 | 0.7649 | 0.3344 | 0.4305 | 0.1126 | 0.1225 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased_crows_pairs_finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1731
- Accuracy: 0.7649
- Tp: 0.3344
- Tn: 0.4305
- Fp: 0.1126
- Fn: 0.1225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.703 | 1.05 | 20 | 0.6889 | 0.5430 | 0.0 | 0.5430 | 0.0 | 0.4570 |
| 0.6884 | 2.11 | 40 | 0.6886 | 0.5 | 0.3013 | 0.1987 | 0.3444 | 0.1556 |
| 0.5388 | 3.16 | 60 | 0.6347 | 0.7053 | 0.1821 | 0.5232 | 0.0199 | 0.2748 |
| 0.2228 | 4.21 | 80 | 0.9802 | 0.6987 | 0.1887 | 0.5099 | 0.0331 | 0.2682 |
| 0.1644 | 5.26 | 100 | 0.7523 | 0.7583 | 0.3675 | 0.3907 | 0.1523 | 0.0894 |
| 0.0478 | 6.32 | 120 | 1.5712 | 0.6954 | 0.2053 | 0.4901 | 0.0530 | 0.2517 |
| 0.0465 | 7.37 | 140 | 1.2587 | 0.7351 | 0.2781 | 0.4570 | 0.0861 | 0.1788 |
| 0.0313 | 8.42 | 160 | 1.5825 | 0.7450 | 0.3775 | 0.3675 | 0.1755 | 0.0795 |
| 0.0137 | 9.47 | 180 | 1.3570 | 0.7318 | 0.2815 | 0.4503 | 0.0927 | 0.1755 |
| 0.04 | 10.53 | 200 | 2.1377 | 0.6921 | 0.1887 | 0.5033 | 0.0397 | 0.2682 |
| 0.0041 | 11.58 | 220 | 1.6776 | 0.7351 | 0.3278 | 0.4073 | 0.1358 | 0.1291 |
| 0.0042 | 12.63 | 240 | 1.8873 | 0.7086 | 0.2980 | 0.4106 | 0.1325 | 0.1589 |
| 0.0009 | 13.68 | 260 | 2.2464 | 0.6987 | 0.3543 | 0.3444 | 0.1987 | 0.1026 |
| 0.014 | 14.74 | 280 | 1.9753 | 0.7252 | 0.3245 | 0.4007 | 0.1424 | 0.1325 |
| 0.0026 | 15.79 | 300 | 1.8852 | 0.7417 | 0.2914 | 0.4503 | 0.0927 | 0.1656 |
| 0.0147 | 16.84 | 320 | 2.0273 | 0.7351 | 0.3113 | 0.4238 | 0.1192 | 0.1457 |
| 0.0009 | 17.89 | 340 | 1.7328 | 0.7483 | 0.3278 | 0.4205 | 0.1225 | 0.1291 |
| 0.0085 | 18.95 | 360 | 2.0146 | 0.7450 | 0.2815 | 0.4636 | 0.0795 | 0.1755 |
| 0.0001 | 20.0 | 380 | 2.0808 | 0.7450 | 0.3113 | 0.4338 | 0.1093 | 0.1457 |
| 0.0001 | 21.05 | 400 | 2.2655 | 0.7417 | 0.3609 | 0.3808 | 0.1623 | 0.0960 |
| 0.0034 | 22.11 | 420 | 2.0298 | 0.7583 | 0.3079 | 0.4503 | 0.0927 | 0.1490 |
| 0.0082 | 23.16 | 440 | 2.0650 | 0.7550 | 0.3344 | 0.4205 | 0.1225 | 0.1225 |
| 0.0001 | 24.21 | 460 | 2.2472 | 0.7450 | 0.2748 | 0.4702 | 0.0728 | 0.1821 |
| 0.0001 | 25.26 | 480 | 2.3655 | 0.7351 | 0.3709 | 0.3642 | 0.1788 | 0.0861 |
| 0.0004 | 26.32 | 500 | 2.1407 | 0.7550 | 0.3510 | 0.4040 | 0.1391 | 0.1060 |
| 0.0001 | 27.37 | 520 | 2.1168 | 0.7450 | 0.3642 | 0.3808 | 0.1623 | 0.0927 |
| 0.0002 | 28.42 | 540 | 2.2050 | 0.7517 | 0.3775 | 0.3742 | 0.1689 | 0.0795 |
| 0.0 | 29.47 | 560 | 2.0560 | 0.7682 | 0.3212 | 0.4470 | 0.0960 | 0.1358 |
| 0.0 | 30.53 | 580 | 2.0859 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 31.58 | 600 | 2.0958 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 32.63 | 620 | 2.1039 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 33.68 | 640 | 2.1113 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 34.74 | 660 | 2.1180 | 0.7715 | 0.3179 | 0.4536 | 0.0894 | 0.1391 |
| 0.0 | 35.79 | 680 | 2.1127 | 0.7715 | 0.3278 | 0.4437 | 0.0993 | 0.1291 |
| 0.0 | 36.84 | 700 | 2.1376 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 37.89 | 720 | 2.1460 | 0.7616 | 0.3377 | 0.4238 | 0.1192 | 0.1192 |
| 0.0 | 38.95 | 740 | 2.1507 | 0.7649 | 0.3377 | 0.4272 | 0.1159 | 0.1192 |
| 0.0 | 40.0 | 760 | 2.1548 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 41.05 | 780 | 2.1586 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 42.11 | 800 | 2.1620 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 43.16 | 820 | 2.1649 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 44.21 | 840 | 2.1674 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 45.26 | 860 | 2.1690 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 46.32 | 880 | 2.1705 | 0.7682 | 0.3377 | 0.4305 | 0.1126 | 0.1192 |
| 0.0 | 47.37 | 900 | 2.1717 | 0.7649 | 0.3344 | 0.4305 | 0.1126 | 0.1225 |
| 0.0 | 48.42 | 920 | 2.1726 | 0.7649 | 0.3344 | 0.4305 | 0.1126 | 0.1225 |
| 0.0 | 49.47 | 940 | 2.1731 | 0.7649 | 0.3344 | 0.4305 | 0.1126 | 0.1225 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
{"datasets": ["crows_pairs"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased_crows_pairs_finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "crows_pairs", "type": "crows_pairs", "config": "crows_pairs", "split": "test", "args": "crows_pairs"}, "metrics": [{"type": "accuracy", "value": 0.7649006622516556, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,965 |
mmnga/webbigdata-ALMA-7B-Ja-gguf
|
mmnga
| null |
[
"gguf",
"arxiv:2309.11674",
"license:other",
"endpoints_compatible",
"region:us"
] | 2023-10-10T14:55:14Z |
2023-11-06T13:14:52+00:00
| 781 | 2 |
---
license: other
---
# webbigdata-ALMA-7B-Ja-gguf
[webbigdataさんが公開しているALMA-7B-Ja](https://huggingface.co/webbigdata/ALMA-7B-Ja)のggufフォーマット変換版です。
v1のggufの各量子化の評価がwebbigdataさんのblogで公開されています
[webbigdata/１．日英・英日機械翻訳モデルALMA-7B-Ja-V2の公開](https://webbigdata.jp/post-21151/)
モデル一覧
[mmnga/webbigdata-ALMA-7B-Ja-V2-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-V2-gguf)
[mmnga/webbigdata-ALMA-7B-Ja-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-gguf)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'webbigdata-ALMA-7B-Ja-q4_0.gguf' -n 128 -p 'Translate this from Japanese to English:\nJapanese: 今日の夕食はピザです。\nEnglish:'
```
### 引用 Citations
~~~tex
@misc{xu2023paradigm,
title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models},
author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla},
year={2023},
eprint={2309.11674},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
~~~
| null |
Non_BioNLP
|
# webbigdata-ALMA-7B-Ja-gguf
[webbigdataさんが公開しているALMA-7B-Ja](https://huggingface.co/webbigdata/ALMA-7B-Ja)のggufフォーマット変換版です。
v1のggufの各量子化の評価がwebbigdataさんのblogで公開されています
[webbigdata/１．日英・英日機械翻訳モデルALMA-7B-Ja-V2の公開](https://webbigdata.jp/post-21151/)
モデル一覧
[mmnga/webbigdata-ALMA-7B-Ja-V2-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-V2-gguf)
[mmnga/webbigdata-ALMA-7B-Ja-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-gguf)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'webbigdata-ALMA-7B-Ja-q4_0.gguf' -n 128 -p 'Translate this from Japanese to English:\nJapanese: 今日の夕食はピザです。\nEnglish:'
```
### 引用 Citations
~~~tex
@misc{xu2023paradigm,
title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models},
author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla},
year={2023},
eprint={2309.11674},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
~~~
|
{"license": "other"}
|
task
|
[
"TRANSLATION"
] | 41,966 |
NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF
|
NikolayKozloff
|
text-generation
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"summarization",
"translation",
"question-answering",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"uz",
"en",
"dataset:yahma/alpaca-cleaned",
"dataset:behbudiy/alpaca-cleaned-uz",
"dataset:behbudiy/translation-instruction",
"base_model:behbudiy/Llama-3.1-8B-Instuct-Uz",
"base_model:quantized:behbudiy/Llama-3.1-8B-Instuct-Uz",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | 2024-09-17T22:13:44Z |
2024-09-17T22:14:20+00:00
| 6 | 1 |
---
base_model: behbudiy/Llama-3.1-8B-Instuct-Uz
datasets:
- yahma/alpaca-cleaned
- behbudiy/alpaca-cleaned-uz
- behbudiy/translation-instruction
language:
- uz
- en
library_name: transformers
license: llama3.1
metrics:
- bleu
- comet
- accuracy
pipeline_tag: text-generation
tags:
- llama
- text-generation-inference
- summarization
- translation
- question-answering
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF
This model was converted to GGUF format from [`behbudiy/Llama-3.1-8B-Instuct-Uz`](https://huggingface.co/behbudiy/Llama-3.1-8B-Instuct-Uz) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/behbudiy/Llama-3.1-8B-Instuct-Uz) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -c 2048
```
| null |
Non_BioNLP
|
# NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF
This model was converted to GGUF format from [`behbudiy/Llama-3.1-8B-Instuct-Uz`](https://huggingface.co/behbudiy/Llama-3.1-8B-Instuct-Uz) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/behbudiy/Llama-3.1-8B-Instuct-Uz) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Llama-3.1-8B-Instuct-Uz-Q8_0-GGUF --hf-file llama-3.1-8b-instuct-uz-q8_0.gguf -c 2048
```
|
{"base_model": "behbudiy/Llama-3.1-8B-Instuct-Uz", "datasets": ["yahma/alpaca-cleaned", "behbudiy/alpaca-cleaned-uz", "behbudiy/translation-instruction"], "language": ["uz", "en"], "library_name": "transformers", "license": "llama3.1", "metrics": ["bleu", "comet", "accuracy"], "pipeline_tag": "text-generation", "tags": ["llama", "text-generation-inference", "summarization", "translation", "question-answering", "llama-cpp", "gguf-my-repo"]}
|
task
|
[
"TRANSLATION",
"SUMMARIZATION"
] | 41,967 |
Jalajkx/all_mpnetcric-setfit-model
|
Jalajkx
|
text-classification
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpnet-base-v2",
"model-index",
"region:us"
] | 2023-12-05T09:23:20Z |
2024-01-03T08:25:58+00:00
| 3 | 0 |
---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: so the representative presentative that i talked to a nine o'clock this morning
but it said that they were gonna call the customer service my service or whatever
that they were gonna call to to see if they dispute it and i didn't get a call
back bags i'm a mother of three kids i worked were two job i can't have a phone
that that's not on and i don't have the money the money to pay a hundred and fifty
six eighty six dollars you guys want me to pay right now it's restore my service
- text: i understand kelly yes let me send you a little bit about be a moment okay
is is that the pin number that you have the four days four digits that you have
with us is
- text: yeah 'cause that that's that's really that's ridiculous you know that's ridiculous
for her to do that i like i have you know all the time in the world okay all right
okay okay no i got my card no cathy she asked me for my last time and i did not
have it i'm
- text: ma'am uh thanks for holding for holding by the way everything upon checking
i'm checking here a record ma'am aah for your for your current a hot spot you
said you said you still have twenty gigabytes left
- text: and then when i asked i didn't even get to speak to the supervisor provider
i just got hung up on
inference: true
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.5463414634146342
name: Accuracy
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 64 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>"yeah and in case you you forget it's actually future date your data so that the one aah you know you know when the bill cycle comes you would just need to pay the the amount of the new of the new plan and you should be good to go"</li><li>"you are eligible for is four one for one upright discount if you want to have once you have a cell phone for free you can uh bring it bring a number from in our account our company explode in verizon i'm i'm sorry it's closed exploding an a t and t"</li><li>"okay so i'm gonna look into look into this of an s that i'll just have have to check in our system if the phone is right one is already eligible for the unlock code okay i'll just have to place you on hold for a minute or two to i'll gonna get right back to you just don't hang up all right all right so"</li></ul> |
| 20 | <ul><li>'hmm hmm alright alright just a moment alright great like rechecking further as of you will be checking your e b b b alright so for your your e bebe this will be applied to your to your account on the twenty third third of august yet in the amount of fifty dollars dollars so currently aah for your because your cycle has has started so your autopay has they have taken an amount of seventy one twenty one dollars for your bill now when when that fifty dollar will come in that will be will be charged to the next billing so on so on your next billing so your your aah billed you would only be twenty one ninety one dollars'</li><li>'will be charged'</li><li>"thank you right right so while i'm pulling up your account here like the like to tell you about aah autopay so for so for this one here autopay is actually an auto an auto payment in which aah they they uh we take the aah payment on and on your card a day before your due date due date so where this one here upon checking here checking use your next two days is on the sixth of september number which means that your card will be will be charged on the fifth of the number and and on the fourth you will actually be texted and i'll be reminded that our system system we'll take the payment on the card a baby a day before the due date okay"</li></ul> |
| 11 | <ul><li>'shit sorry can you sorry can you say that one more time'</li><li>'oh bitch pay'</li><li>'moron'</li></ul> |
| 16 | <ul><li>'i was not trying to rush you into a decision'</li><li>'understand your concern'</li><li>'your concern is valid'</li></ul> |
| 35 | <ul><li>"aah uh okay if ever though aah aah you decide to aah get a new a new phone you're eligible for this time that aah the phones from our cricket store and from our website website aah just get a quick a cricket phone to make sure that we can really get we guarantee that are hot spot will work on it you can aah get discounted cricket phone the phones okay"</li><li>"able to have it replaced for these um usage of a year here so since you do have your a device we simply been activated that you're actually currently um eligible for the warranty"</li><li>'aah okay i okay aah yeah sure um yes um peggy take your account is eligible for um which bridge pay'</li></ul> |
| 21 | <ul><li>'thank you would you mind if i can add not going to ask to put you on hold for a manager okay'</li><li>'thank you the four digit pin under your account'</li><li>'thank you what is the what is the four digit pin on the trip that account'</li></ul> |
| 46 | <ul><li>"just wanna make sure that sure the seat like to get you an answer to see if it's maybe just something just something in the maintenance because if"</li><li>'yes sir thank you so much for making your bill your payment that we already have receive what happened you did you did you receive the message on for me sign in your end and just to make sure'</li><li>"okay so you're so you're trying to process on the i v r on the automated system which system"</li></ul> |
| 47 | <ul><li>'network is not stable'</li><li>"can't connect"</li><li>'drop my call'</li></ul> |
| 6 | <ul><li>"i'm sorry is that and emelia"</li><li>'i d i do understand you let me go ahead and check your information here on your phone okay'</li><li>"i see i see right now oh i got something here thing here already i noticed it by the way let me assist you with since you with this i'm sorry that happened but we'll see what's going on going on i get an account and get the name of calvin birth been burns west"</li></ul> |
| 51 | <ul><li>"hmm hmm hmm hmm i can barely hear you sir i'm sorry"</li><li>"i can't hear you"</li><li>'no i say that again'</li></ul> |
| 27 | <ul><li>'oh i understand and yes i can definitely assist you with be doing that assist me so can i can i please have your phone number'</li><li>"if you're having having a hard time to do it on your phone uh to call with me with me then you can just go ahead and aah visit a visit at the store and they can actually assist you with and even they can go ahead and uh you know uh click on it with uh forget pass got password they're using your device and that's and that's the best option for you um to call if you're having have the option to do"</li><li>"thanks for calling to try to me destination my name is that where it's how can i help you"</li></ul> |
| 19 | <ul><li>'buy cable protector'</li><li>'get screen protection'</li><li>'need cable protector'</li></ul> |
| 62 | <ul><li>'arsehole'</li><li>'fool'</li><li>'bastard'</li></ul> |
| 12 | <ul><li>"i just want i just want the number it's this not not a transfer anything for a new phone it's phone it's the same phone"</li><li>'when i got i got the other new phones on'</li><li>'aah yeah do you need to you need to put my new aah i m e i number in from in for my new phone'</li></ul> |
| 13 | <ul><li>'web site was confusing'</li><li>'placeholder'</li><li>'could not find app'</li></ul> |
| 56 | <ul><li>"yes yesterday yesterday i called to add her mobile hot spot to my phone and then phone and it isn't working"</li><li>"remember to sent the customer for their business hi frank uh this is aah meredith there it is i was calling aah to see to see what we can do about my cell phone i got it hmm uh maybe a week or two ago i mean i and it's coming up restricted or private five it when i called people it's not even showing my number my number and i know it's nothing in my phone in my phone or if it is i haven't learned how to fix it"</li><li>"uh huh oh shit i'm by the little boy almost does it is there no knowing some come up come up please stay on the line yes that's what i need my gas for for people that so have no z four for people in the philippines they don't they don't know what they don't know what do you then hit no other information phone number nothing to do all that yeah with the mail when i called what other m b a"</li></ul> |
| 31 | <ul><li>"yes of course aah she can just you know really visa i mean visit our website cause it round while dot com and you know see a c a check for this phone and you can just and you can just you know um select the option that and that can customer and i'm upgrading my phone phone and just add to the card and you know you know it will continuously have have it place you'll just need to put your bill you know billing address that you're info so that you could have you can have it successfully"</li><li>'yeah yeah you can go to the store uh by twenty the twenty first'</li><li>"gigabytes okay okay she's aah just still work still working on it by the way if you have any have any increase you can also visit our website and you can and you can make a payment online are our cricket up for you to avoid for four dollars of assistance fee"</li></ul> |
| 49 | <ul><li>"let me double check hold check hold on um we actually actually have as well the samsung galaxy i'll see a twelve for or aah one hundred nine dollars and ninety nine cents nine cents"</li><li>'thank you very much let me go ahead and process the payment the payment for just allow me one moment'</li><li>'okay so let me see okay mister and what you say you were trying to ask to activate a new device right'</li></ul> |
| 15 | <ul><li>"all that's all okay thank you so much how much glad i was able to help out online lucky your device your device once again the supervisor from cricket do you have a great day and stay safe bye now"</li><li>'no no no when i was um um the last person that i talked to the one i hung up hung up on me was supposed to be the supervisor okay'</li><li>"and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on"</li></ul> |
| 60 | <ul><li>'okay okay do you have this this number this number next to you right now this device'</li><li>'uh i mean do you want to speak with a spam with a spanish the sedative'</li><li>'to go and content the number her aah let me know if you get the i d the i m e i or if the call will went through through and then um once you have do you have it just put me on hold and i will be waiting for the for the i m e i d okay'</li></ul> |
| 7 | <ul><li>"i i can't fight it but it's frustrating you know"</li><li>"and and sometimes it's like the weather i hear i hear a horrible well"</li><li>"i just need to work working phone and and it's it's been like oh like a ridiculous you two months about this office and then when i called and they told me they told me that i have the proof of this whole time but it doesn't make sense because i just was never never told that"</li></ul> |
| 33 | <ul><li>'i hate the new system'</li><li>'do what ever you want'</li><li>'i do not have time for your stories'</li></ul> |
| 4 | <ul><li>'hello welcome to correct to cricket this is my nonna can i get your name please please'</li><li>'welcome to cricket click admission my name is john can i have your name please name please'</li><li>"thank you for calling verizon wireless my name yes my name is better which one will i be having a pleasure let's you're speaking with"</li></ul> |
| 3 | <ul><li>'thank you so very much for waiting waiting and i was able to verify all this information in the house and the hot spot has been successfully add it just to make sure to make sure the services are working aah uh i can give you a call back but what you will need to do be do be to restart your phone turn it off and then turn it back turn it back on and then i can give you a call back in call back in two to three minutes'</li><li>"okay okay you know you're almost there it's until september september the twenty sixth that this phone is due is due to be unlocked"</li><li>'okay can you try to go to go and not just like to internet google google good up or something like that there'</li></ul> |
| 61 | <ul><li>'very good thank you so much'</li><li>'so much higher i appreciate that'</li><li>"okay well thank you so i'm not sure"</li></ul> |
| 22 | <ul><li>'i was put on hold'</li><li>'i v r is terrible'</li><li>'i v r has too many options'</li></ul> |
| 30 | <ul><li>'service has been a bit unpredictable'</li><li>'outage will continue'</li><li>'network outages'</li></ul> |
| 48 | <ul><li>"was able to resolve it it's of your concern today but simply performing it troubleshooting shipping for your service uh miss is everything"</li><li>"you're welcome and before i let you go to i just want to just want to let you know i'm happy i was able to resolve your concern your concern"</li><li>"hmm but the question is how come i wasn't able to able to when i go there and now i'm not able to connect when i so um"</li></ul> |
| 39 | <ul><li>'send the document'</li><li>'email the document'</li><li>'email the details'</li></ul> |
| 10 | <ul><li>'all right well thank you so much for that one for that one month um can you please tell me how can i assist you today'</li><li>"you're welcome other back thank you so much for your patience patience um i already restore the line"</li><li>"okay okay all righty aah aah we'll do that today thank you so much"</li></ul> |
| 57 | <ul><li>'to add line'</li><li>'add a new one'</li><li>'like to add a line'</li></ul> |
| 50 | <ul><li>"all right okay that's fine so on the records answer the and before we can to cancel the line of what i can see it here you do you have three lines lines active for fifty five dollar plan a plan so on the monthly basis you're paying one one hundred one dollars because there is like an insurance sure it's on the line and then once it would be me remove the insurance also be removed it as well this line okay and once it will be you will be removed there's also changes on the bill so if the line will be canceled it will be out of the automated that it was aah the effective today though okay that will be cancelling the line and then the line the line going to be ported out anymore and also so then you service as of the line will not be refund refunded nor credited and that's what i can see here here on the eighteenth would be your do right"</li><li>"or the kitchen so before before we can do to where it's supposed to be used let me pull me once you cancel the line if you change your change your mind you will need to pay fifteen dollars it's for the reactivation"</li><li>"okay so before we cancel the line is there some there's something else on your account that i can help you with or with or only will be the cancellation"</li></ul> |
| 8 | <ul><li>'okay just stay on the line while i work on your on your account can i put you on a brief hold a logging into logging'</li><li>'okay let me let me put you on put you on a brief hold for a moment okay'</li><li>'please christopher open aah confirm it here material quick i place you on hold for a minute one moment'</li></ul> |
| 36 | <ul><li>"yeah sure thing sure thank you so much for your patience waiting on the line and and and i'm so sorry for the long hold while i while i really do appreciate the again i understand how important port and this is for you and appreciate also that time time that you really drop by on the tickets to worry about this about this concern but as i check for back further here um as i can it's confirmed it it's really not in the sim card but okay but instead on the device that you're using right using right now aah the i m e i i of the device ending in zero nine zero nine zero five this phone is no longer no longer compatible here with a service it it said cricket and that's the reason right now now that the service will not work call because of the phone so we can we can confirm right here that on the like lines that things that you have right here on the account this is the only one one that is currently an active so i even way like it like either to inquire on the crickets for it's for because we can see right here if maybe you are you are qualified for a device upgrade or maybe if you have if you have a spare phone now we can check if this is the this is still compatible with the service at cricket because we can update the i m e i of i of this phone"</li><li>"hmm okay so if you're if you were to have it activated right right now you the the phone will be eligible for an locked bye by upto per ten"</li><li>"if you upgrade as per as per checking here you're eligible for a device that hi discount so that means if you plan you plan on upgrading your device you can definitely and i only have a discount as long as you're logged log in your account over cricket wireless dot com com you can choose your device will recover device device if you should be able to receive a discount discount"</li></ul> |
| 54 | <ul><li>'thank you got this one not this one here and this is for me to send to get your get your account can i have the four digit pin please'</li><li>'the forgot user name'</li><li>'oh four alrighty all righty thank you and how about the four digit pin digit pin on the account to verify'</li></ul> |
| 63 | <ul><li>'um are there any way i can call back back in a few minutes'</li><li>"the aah assurance taken off of the other line other line that's on my account but aah okay you got already a letter to the end of it told me that because my account was account was credited eight dollars from the check in the insurance off i thought i only two dollars so when i call back uh to so the card information to you to pay it it tells me that aah or whoever whoever told me that there is no no um i haven't shown in the system but there was any changes made"</li><li>"i will give you all a call back when i'm ready i'm ready"</li></ul> |
| 43 | <ul><li>'i know who i am seeing'</li><li>"add add in that case i'm glad i was able to provide the details this is my callaghan from cricket stay safe always and bye for now"</li><li>'welcome to cricket the coordination this is josie may i have your name please please'</li></ul> |
| 55 | <ul><li>'and one day at one on my account not the not and they brought a blocked or lot layest balance and she said it said it would phone was sound to call back all that and they did take the block fully at all all'</li><li>"thank you for free you were picking up the phone call but anyway if there's anything else feel free feel free to call us back where aah always open aah before also nine"</li><li>"in this case then um mister mister walker where we'll need to do is i will need you to wanting you to turn off your device for uh um five minutes and then turn it back on and i and i'll be calling them back in around ten minutes okay for you not you not to call back customer care new for you not to wait and to wait anymore okay"</li></ul> |
| 5 | <ul><li>"yes sir in this case uh from meanwhile it we're doing this jamie name is uh you can also get enroll or roll on the autopay you have aah you have a single line uh recommend you to ready to do that so you can have the five dollar discount setup in"</li><li>"okay okay and you may add on a debit or credit card with them them and aah that will provide you five dollar discount a discount on your next month's bill right"</li><li>"okay and usually usually um regarding here on the account do you have autopay autopay on the account and that's why i'm just aah we're just paying for fifty eight dollars a month instead of start of sixty three so here it's aah um too early for us to like give the credit credit on the account because we're crediting over give were giving the five dollar discount or credit aah aah five days before the due date to tech so technically that will be on the twentieth twenty second of august the starting that day what day you can see that the new amount is just it's just fifty eight instead of sixty three okay"</li></ul> |
| 14 | <ul><li>'hold on hold on twenty alright allen so i'</li><li>'okay yeah then well hold on hold on a shit to that should i would like to me maybe somebody know lane okay aah are you ready'</li><li>"so hold on hold on we're on the side okay i sent a text message search that you receive uh any text message text message coming from cricket"</li></ul> |
| 2 | <ul><li>"wasn't aware of the fee"</li><li>'a lot of hidden cost'</li><li>'high price'</li></ul> |
| 58 | <ul><li>"i'm gonna go ahead and you transfer"</li><li>"i'm happy to transfer you"</li><li>'uh yes i can transfer transfer you to record to the insurance aah i can aah i can give you the number there'</li></ul> |
| 37 | <ul><li>'okay tell me apart from that anything else that you might want you might want to apply to your account'</li><li>'this do you have any other concerns'</li><li>'no problem and yeah and yes now that we have successfully aah process aah process the payment andrea do you have any other question the questions or concerns'</li></ul> |
| 23 | <ul><li>"okay just timing can i be lohman here okay perfect just a moment one moment here it may i please you in a brief hold ma'am"</li><li>'okay just stay on the line while i work on your on your account can i put you on a brief hold a logging into logging'</li><li>"okay thank you so much for that information shannon for your help as well now let me go ahead go ahead and just work on my system i cage if you don't mind don't mind going to put you just on a brief hold once i have the i have"</li></ul> |
| 52 | <ul><li>"thank you you're welcome"</li><li>"all i'm really i'm really sorry that your son is having issues with this so the services no worries let me pull up your account account so i could help you check it may i have your i have your"</li><li>"no worries no worries no worries thank you very much for verifying the confirmation make sure that i hear me okay yeah no worries take your time when you're ready tony okay tony okay"</li></ul> |
| 28 | <ul><li>"i do not i do not have the wifi on um and i only have my daughter on like i been i've been i've been dealing with us from going on three three days and i'm like on so so frustrated cricket stores i tried calling calling them right please are closed today cause labor day okay"</li><li>"yeah well aah i i think you need to have your manager go back and listen to the into the call that's recorded because clearly he told me on help me on the phone that i could say uh my bank is thank you should probably have your manager go back and listen to the to the phone call so she can hear what he said"</li><li>'aah if you will enroll that counterfeits which way you can get seven extension mention aah for example you an android that kind of which the bitch paid today your aah your your um your page pay your extension this time start on the twenty second upon but yeah one two nine two three four five six saffron up until the twenty eight twenty eight of august'</li></ul> |
| 59 | <ul><li>'instructions not clear'</li><li>'waiting for installation'</li><li>'slow installation process'</li></ul> |
| 53 | <ul><li>"thank you so much and i'm glad i was and i was able to resolve your concern by uh now so that you can now enjoy the retail services once once again nicole this is berna and thank you purchasing cricket and have a great day bye"</li><li>'thank you sir thank you so much so right now sage i would just like the car to clarify are you trying to pay your bill on your bill on your online cricket account however you forgot your forgot your password or you forgot'</li><li>"okay perfect so thank you so much for being part we type precondition my name is marlin and i hope you have a wonderful day ma'am"</li></ul> |
| 45 | <ul><li>'brian high ryan how are you doing how are you doing today'</li><li>"i'm great sir um um how about you"</li><li>'agent how are you today'</li></ul> |
| 41 | <ul><li>'all right just can i can i have the card number to process the payment one more time'</li><li>'okay hmm um we will also going to uh verify that one to for you okay for you okay the way alright so let me check check here and it seems like that that the um card that decline here can you please give me please give me one more time the card number just to make sure i have'</li><li>'you know that person that you really really you know who will actually receive this phone they might be able to at least activate the one with one with cricket number that way they can actually surely continue the six months aah can you repeat it written requirements so they can use it with different carrier carrier'</li></ul> |
| 17 | <ul><li>'welcome to recap my name is sheila hello how can i help you today'</li><li>'just a great week quick recap um corban you calling because one because you wanted'</li><li>'i woke up to recap my name is sheila uh how can i help you today'</li></ul> |
| 34 | <ul><li>'i am definitely interested'</li><li>'what is the extended warranty'</li><li>'what are the accessories'</li></ul> |
| 25 | <ul><li>"my pleasure sir and and uh and uh remember also that all of your lines right now in this case this account i already active okay i don't know uh there's uh there's anything else i can do for you today mister word"</li><li>"i don't know if it's in there"</li><li>"hmm okay okay remember that some well oh i old i don't know if you have well well do you have a p s four o a place agent or an excellent"</li></ul> |
| 24 | <ul><li>'choosing cricket just my name is danielle how may i have the pleasure to speak to thank you'</li><li>'welcome to pick condition my name and my name is judy may i have your name please'</li><li>"me the opportunity to assist you in it see i explained to you about your services again my name is arlene enjoy the rest of the day ma'am stay safe and happy weekend"</li></ul> |
| 9 | <ul><li>'how many times to call'</li><li>'kept calling'</li><li>'did not get an answer before'</li></ul> |
| 1 | <ul><li>"okay i understand and i know it's kind of kind of frustrating to get uh try to just you just a services and that isn't working working but no worries is going to be my pleasure today sure today to check up your account and see what's going what's going on so can you please provide me your phone number number"</li><li>"hi victoria toria um thanks for patiently waiting all right so i check your account further further and i also check um the payment that you made paid and um the payment the payment was already posted um i believe leave um you made the payment today and it was so do as of um is it and it's um sixty dollars dollars right but um your account is already cancelled that's why the that's why the system will require the system will require required to pay aah are we activation fee of five days five dollars okay so looking into the account right now right now there is a balance here or here on five dollars okay so this check just charge is valid this is for for the for the activation fee okay but don't worry okay um let me go ahead go ahead and ask permission from my support my supervisor if we can do something about the five the five dollars amount onto your account okay"</li><li>"joe your account to reach pay we will extend extend your services for seven days with a process processing the full payment on the account on the on the seven days which is the twenty second second you need to process the the whole amount or you need the price to process the second payment since you're rate plan is sixty five or your bill is sixty sixty five then the only paying payment that you going to process next time is the is the amount of fifty five dollars ten ten dollars go to your services five dollars there's to your account then as well as well as you have credit as well so i'll just set up you set up the bitch pay okay"</li></ul> |
| 29 | <ul><li>'when will the enrollment be done'</li><li>'when will the offer be activated'</li><li>'how long will it take to be delivered'</li></ul> |
| 32 | <ul><li>'always data issues'</li><li>"don't use this"</li><li>'data plan not suffice'</li></ul> |
| 18 | <ul><li>'not interested'</li><li>'i dont want it'</li><li>'not for me'</li></ul> |
| 44 | <ul><li>'customer service sucks here'</li><li>'service agent scheduled didnt come'</li><li>'i have been waiting on the automated system forever'</li></ul> |
| 26 | <ul><li>'yes lets go ahead'</li><li>'sure i like the deal'</li><li>'please proceed with the offer'</li></ul> |
| 42 | <ul><li>'includes more data'</li><li>"they don't charge for"</li><li>'plan was good'</li></ul> |
| 40 | <ul><li>'upgrading to the plan will resolve this issue'</li><li>'upgrade plan to resolve issue'</li></ul> |
| 38 | <ul><li>'not a great equipment'</li><li>'battery runs out'</li><li>'problems with your equipment'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5463 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Jalajkx/all_mpnetcric-setfit-model")
# Run inference
preds = model("and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 32.4224 | 283 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 36 |
| 1 | 36 |
| 2 | 36 |
| 3 | 36 |
| 4 | 36 |
| 5 | 36 |
| 6 | 36 |
| 7 | 36 |
| 8 | 36 |
| 9 | 6 |
| 10 | 36 |
| 11 | 36 |
| 12 | 36 |
| 13 | 9 |
| 14 | 36 |
| 15 | 36 |
| 16 | 17 |
| 17 | 36 |
| 18 | 4 |
| 19 | 29 |
| 20 | 30 |
| 21 | 36 |
| 22 | 25 |
| 23 | 36 |
| 24 | 36 |
| 25 | 36 |
| 26 | 4 |
| 27 | 36 |
| 28 | 36 |
| 29 | 4 |
| 30 | 8 |
| 31 | 36 |
| 32 | 4 |
| 33 | 36 |
| 34 | 11 |
| 35 | 36 |
| 36 | 36 |
| 37 | 36 |
| 38 | 10 |
| 39 | 13 |
| 40 | 2 |
| 41 | 36 |
| 42 | 9 |
| 43 | 36 |
| 44 | 10 |
| 45 | 36 |
| 46 | 36 |
| 47 | 14 |
| 48 | 36 |
| 49 | 36 |
| 50 | 36 |
| 51 | 36 |
| 52 | 36 |
| 53 | 36 |
| 54 | 36 |
| 55 | 36 |
| 56 | 36 |
| 57 | 36 |
| 58 | 36 |
| 59 | 8 |
| 60 | 36 |
| 61 | 36 |
| 62 | 36 |
| 63 | 36 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 25
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2196 | - |
| 0.0022 | 50 | 0.2183 | - |
| 0.0044 | 100 | 0.3574 | - |
| 0.0065 | 150 | 0.1756 | - |
| 0.0087 | 200 | 0.1396 | - |
| 0.0109 | 250 | 0.2875 | - |
| 0.0131 | 300 | 0.1307 | - |
| 0.0152 | 350 | 0.1465 | - |
| 0.0174 | 400 | 0.1503 | - |
| 0.0196 | 450 | 0.1579 | - |
| 0.0218 | 500 | 0.3216 | - |
| 0.0240 | 550 | 0.2399 | - |
| 0.0261 | 600 | 0.2824 | - |
| 0.0283 | 650 | 0.1217 | - |
| 0.0305 | 700 | 0.0647 | - |
| 0.0327 | 750 | 0.2651 | - |
| 0.0348 | 800 | 0.1792 | - |
| 0.0370 | 850 | 0.1461 | - |
| 0.0392 | 900 | 0.0256 | - |
| 0.0414 | 950 | 0.1175 | - |
| 0.0435 | 1000 | 0.2394 | - |
| 0.0457 | 1050 | 0.1582 | - |
| 0.0479 | 1100 | 0.2785 | - |
| 0.0501 | 1150 | 0.0611 | - |
| 0.0523 | 1200 | 0.1937 | - |
| 0.0544 | 1250 | 0.0804 | - |
| 0.0566 | 1300 | 0.0811 | - |
| 0.0588 | 1350 | 0.0663 | - |
| 0.0610 | 1400 | 0.2148 | - |
| 0.0631 | 1450 | 0.0428 | - |
| 0.0653 | 1500 | 0.0083 | - |
| 0.0675 | 1550 | 0.0884 | - |
| 0.0697 | 1600 | 0.1341 | - |
| 0.0719 | 1650 | 0.0949 | - |
| 0.0740 | 1700 | 0.1839 | - |
| 0.0762 | 1750 | 0.2244 | - |
| 0.0784 | 1800 | 0.0309 | - |
| 0.0806 | 1850 | 0.0277 | - |
| 0.0827 | 1900 | 0.2016 | - |
| 0.0849 | 1950 | 0.1174 | - |
| 0.0871 | 2000 | 0.0942 | - |
| 0.0893 | 2050 | 0.0483 | - |
| 0.0915 | 2100 | 0.2057 | - |
| 0.0936 | 2150 | 0.0151 | - |
| 0.0958 | 2200 | 0.023 | - |
| 0.0980 | 2250 | 0.0514 | - |
| 0.1002 | 2300 | 0.1541 | - |
| 0.1023 | 2350 | 0.1426 | - |
| 0.1045 | 2400 | 0.0187 | - |
| 0.1067 | 2450 | 0.0386 | - |
| 0.1089 | 2500 | 0.274 | - |
| 0.1110 | 2550 | 0.0723 | - |
| 0.1132 | 2600 | 0.0115 | - |
| 0.1154 | 2650 | 0.053 | - |
| 0.1176 | 2700 | 0.2371 | - |
| 0.1198 | 2750 | 0.2472 | - |
| 0.1219 | 2800 | 0.0386 | - |
| 0.1241 | 2850 | 0.0159 | - |
| 0.1263 | 2900 | 0.0276 | - |
| 0.1285 | 2950 | 0.1229 | - |
| 0.1306 | 3000 | 0.0037 | - |
| 0.1328 | 3050 | 0.0029 | - |
| 0.1350 | 3100 | 0.0037 | - |
| 0.1372 | 3150 | 0.022 | - |
| 0.1394 | 3200 | 0.0389 | - |
| 0.1415 | 3250 | 0.0146 | - |
| 0.1437 | 3300 | 0.0034 | - |
| 0.1459 | 3350 | 0.0721 | - |
| 0.1481 | 3400 | 0.0462 | - |
| 0.1502 | 3450 | 0.0039 | - |
| 0.1524 | 3500 | 0.1225 | - |
| 0.1546 | 3550 | 0.0009 | - |
| 0.1568 | 3600 | 0.1005 | - |
| 0.1590 | 3650 | 0.008 | - |
| 0.1611 | 3700 | 0.121 | - |
| 0.1633 | 3750 | 0.2982 | - |
| 0.1655 | 3800 | 0.008 | - |
| 0.1677 | 3850 | 0.001 | - |
| 0.1698 | 3900 | 0.216 | - |
| 0.1720 | 3950 | 0.0458 | - |
| 0.1742 | 4000 | 0.0155 | - |
| 0.1764 | 4050 | 0.1235 | - |
| 0.1785 | 4100 | 0.0059 | - |
| 0.1807 | 4150 | 0.2421 | - |
| 0.1829 | 4200 | 0.2232 | - |
| 0.1851 | 4250 | 0.0396 | - |
| 0.1873 | 4300 | 0.2164 | - |
| 0.1894 | 4350 | 0.0839 | - |
| 0.1916 | 4400 | 0.0116 | - |
| 0.1938 | 4450 | 0.2666 | - |
| 0.1960 | 4500 | 0.0648 | - |
| 0.1981 | 4550 | 0.074 | - |
| 0.2003 | 4600 | 0.077 | - |
| 0.2025 | 4650 | 0.0739 | - |
| 0.2047 | 4700 | 0.0029 | - |
| 0.2069 | 4750 | 0.0679 | - |
| 0.2090 | 4800 | 0.0049 | - |
| 0.2112 | 4850 | 0.0281 | - |
| 0.2134 | 4900 | 0.049 | - |
| 0.2156 | 4950 | 0.0052 | - |
| 0.2177 | 5000 | 0.1657 | - |
| 0.2199 | 5050 | 0.0005 | - |
| 0.2221 | 5100 | 0.0041 | - |
| 0.2243 | 5150 | 0.0008 | - |
| 0.2265 | 5200 | 0.0587 | - |
| 0.2286 | 5250 | 0.0753 | - |
| 0.2308 | 5300 | 0.1744 | - |
| 0.2330 | 5350 | 0.0055 | - |
| 0.2352 | 5400 | 0.0023 | - |
| 0.2373 | 5450 | 0.0002 | - |
| 0.2395 | 5500 | 0.0472 | - |
| 0.2417 | 5550 | 0.0042 | - |
| 0.2439 | 5600 | 0.0137 | - |
| 0.2460 | 5650 | 0.1646 | - |
| 0.2482 | 5700 | 0.0509 | - |
| 0.2504 | 5750 | 0.0062 | - |
| 0.2526 | 5800 | 0.0019 | - |
| 0.2548 | 5850 | 0.0048 | - |
| 0.2569 | 5900 | 0.0031 | - |
| 0.2591 | 5950 | 0.0011 | - |
| 0.2613 | 6000 | 0.004 | - |
| 0.2635 | 6050 | 0.0498 | - |
| 0.2656 | 6100 | 0.0042 | - |
| 0.2678 | 6150 | 0.0018 | - |
| 0.2700 | 6200 | 0.0061 | - |
| 0.2722 | 6250 | 0.1355 | - |
| 0.2744 | 6300 | 0.0039 | - |
| 0.2765 | 6350 | 0.0044 | - |
| 0.2787 | 6400 | 0.001 | - |
| 0.2809 | 6450 | 0.0011 | - |
| 0.2831 | 6500 | 0.0302 | - |
| 0.2852 | 6550 | 0.1502 | - |
| 0.2874 | 6600 | 0.0029 | - |
| 0.2896 | 6650 | 0.0016 | - |
| 0.2918 | 6700 | 0.0232 | - |
| 0.2940 | 6750 | 0.176 | - |
| 0.2961 | 6800 | 0.0323 | - |
| 0.2983 | 6850 | 0.0818 | - |
| 0.3005 | 6900 | 0.0427 | - |
| 0.3027 | 6950 | 0.1716 | - |
| 0.3048 | 7000 | 0.0137 | - |
| 0.3070 | 7050 | 0.0032 | - |
| 0.3092 | 7100 | 0.0095 | - |
| 0.3114 | 7150 | 0.177 | - |
| 0.3135 | 7200 | 0.0005 | - |
| 0.3157 | 7250 | 0.0157 | - |
| 0.3179 | 7300 | 0.0012 | - |
| 0.3201 | 7350 | 0.0027 | - |
| 0.3223 | 7400 | 0.1351 | - |
| 0.3244 | 7450 | 0.0019 | - |
| 0.3266 | 7500 | 0.0009 | - |
| 0.3288 | 7550 | 0.2017 | - |
| 0.3310 | 7600 | 0.0059 | - |
| 0.3331 | 7650 | 0.0013 | - |
| 0.3353 | 7700 | 0.0377 | - |
| 0.3375 | 7750 | 0.0056 | - |
| 0.3397 | 7800 | 0.0055 | - |
| 0.3419 | 7850 | 0.0745 | - |
| 0.3440 | 7900 | 0.0046 | - |
| 0.3462 | 7950 | 0.002 | - |
| 0.3484 | 8000 | 0.0355 | - |
| 0.3506 | 8050 | 0.0004 | - |
| 0.3527 | 8100 | 0.0004 | - |
| 0.3549 | 8150 | 0.0072 | - |
| 0.3571 | 8200 | 0.0013 | - |
| 0.3593 | 8250 | 0.0032 | - |
| 0.3615 | 8300 | 0.0006 | - |
| 0.3636 | 8350 | 0.0095 | - |
| 0.3658 | 8400 | 0.0006 | - |
| 0.3680 | 8450 | 0.0005 | - |
| 0.3702 | 8500 | 0.0004 | - |
| 0.3723 | 8550 | 0.0019 | - |
| 0.3745 | 8600 | 0.0002 | - |
| 0.3767 | 8650 | 0.0015 | - |
| 0.3789 | 8700 | 0.0117 | - |
| 0.3810 | 8750 | 0.002 | - |
| 0.3832 | 8800 | 0.0005 | - |
| 0.3854 | 8850 | 0.0009 | - |
| 0.3876 | 8900 | 0.0041 | - |
| 0.3898 | 8950 | 0.0484 | - |
| 0.3919 | 9000 | 0.0058 | - |
| 0.3941 | 9050 | 0.0027 | - |
| 0.3963 | 9100 | 0.0002 | - |
| 0.3985 | 9150 | 0.2323 | - |
| 0.4006 | 9200 | 0.0163 | - |
| 0.4028 | 9250 | 0.0333 | - |
| 0.4050 | 9300 | 0.0033 | - |
| 0.4072 | 9350 | 0.0023 | - |
| 0.4094 | 9400 | 0.0044 | - |
| 0.4115 | 9450 | 0.0142 | - |
| 0.4137 | 9500 | 0.0261 | - |
| 0.4159 | 9550 | 0.004 | - |
| 0.4181 | 9600 | 0.027 | - |
| 0.4202 | 9650 | 0.0104 | - |
| 0.4224 | 9700 | 0.0005 | - |
| 0.4246 | 9750 | 0.2452 | - |
| 0.4268 | 9800 | 0.0069 | - |
| 0.4290 | 9850 | 0.0245 | - |
| 0.4311 | 9900 | 0.0005 | - |
| 0.4333 | 9950 | 0.0041 | - |
| 0.4355 | 10000 | 0.1058 | - |
| 0.4377 | 10050 | 0.0009 | - |
| 0.4398 | 10100 | 0.0067 | - |
| 0.4420 | 10150 | 0.0832 | - |
| 0.4442 | 10200 | 0.0016 | - |
| 0.4464 | 10250 | 0.039 | - |
| 0.4485 | 10300 | 0.0078 | - |
| 0.4507 | 10350 | 0.0013 | - |
| 0.4529 | 10400 | 0.0003 | - |
| 0.4551 | 10450 | 0.0259 | - |
| 0.4573 | 10500 | 0.008 | - |
| 0.4594 | 10550 | 0.2137 | - |
| 0.4616 | 10600 | 0.0083 | - |
| 0.4638 | 10650 | 0.0206 | - |
| 0.4660 | 10700 | 0.0039 | - |
| 0.4681 | 10750 | 0.2205 | - |
| 0.4703 | 10800 | 0.0072 | - |
| 0.4725 | 10850 | 0.0436 | - |
| 0.4747 | 10900 | 0.071 | - |
| 0.4769 | 10950 | 0.0004 | - |
| 0.4790 | 11000 | 0.0147 | - |
| 0.4812 | 11050 | 0.0095 | - |
| 0.4834 | 11100 | 0.0069 | - |
| 0.4856 | 11150 | 0.0027 | - |
| 0.4877 | 11200 | 0.0151 | - |
| 0.4899 | 11250 | 0.0076 | - |
| 0.4921 | 11300 | 0.0016 | - |
| 0.4943 | 11350 | 0.1457 | - |
| 0.4965 | 11400 | 0.1454 | - |
| 0.4986 | 11450 | 0.0013 | - |
| 0.5008 | 11500 | 0.0027 | - |
| 0.5030 | 11550 | 0.0583 | - |
| 0.5052 | 11600 | 0.0029 | - |
| 0.5073 | 11650 | 0.0139 | - |
| 0.5095 | 11700 | 0.0004 | - |
| 0.5117 | 11750 | 0.0098 | - |
| 0.5139 | 11800 | 0.0009 | - |
| 0.5160 | 11850 | 0.0003 | - |
| 0.5182 | 11900 | 0.0009 | - |
| 0.5204 | 11950 | 0.0088 | - |
| 0.5226 | 12000 | 0.0006 | - |
| 0.5248 | 12050 | 0.0014 | - |
| 0.5269 | 12100 | 0.0008 | - |
| 0.5291 | 12150 | 0.0008 | - |
| 0.5313 | 12200 | 0.0008 | - |
| 0.5335 | 12250 | 0.0005 | - |
| 0.5356 | 12300 | 0.0028 | - |
| 0.5378 | 12350 | 0.0011 | - |
| 0.5400 | 12400 | 0.0136 | - |
| 0.5422 | 12450 | 0.0318 | - |
| 0.5444 | 12500 | 0.0037 | - |
| 0.5465 | 12550 | 0.0029 | - |
| 0.5487 | 12600 | 0.0073 | - |
| 0.5509 | 12650 | 0.0099 | - |
| 0.5531 | 12700 | 0.015 | - |
| 0.5552 | 12750 | 0.0047 | - |
| 0.5574 | 12800 | 0.0891 | - |
| 0.5596 | 12850 | 0.0007 | - |
| 0.5618 | 12900 | 0.0784 | - |
| 0.5640 | 12950 | 0.0636 | - |
| 0.5661 | 13000 | 0.0029 | - |
| 0.5683 | 13050 | 0.0048 | - |
| 0.5705 | 13100 | 0.0698 | - |
| 0.5727 | 13150 | 0.0002 | - |
| 0.5748 | 13200 | 0.0734 | - |
| 0.5770 | 13250 | 0.0004 | - |
| 0.5792 | 13300 | 0.0135 | - |
| 0.5814 | 13350 | 0.0034 | - |
| 0.5835 | 13400 | 0.0018 | - |
| 0.5857 | 13450 | 0.0175 | - |
| 0.5879 | 13500 | 0.0003 | - |
| 0.5901 | 13550 | 0.0002 | - |
| 0.5923 | 13600 | 0.0032 | - |
| 0.5944 | 13650 | 0.0007 | - |
| 0.5966 | 13700 | 0.0021 | - |
| 0.5988 | 13750 | 0.0019 | - |
| 0.6010 | 13800 | 0.0006 | - |
| 0.6031 | 13850 | 0.0014 | - |
| 0.6053 | 13900 | 0.0011 | - |
| 0.6075 | 13950 | 0.2383 | - |
| 0.6097 | 14000 | 0.0009 | - |
| 0.6119 | 14050 | 0.0863 | - |
| 0.6140 | 14100 | 0.0005 | - |
| 0.6162 | 14150 | 0.0017 | - |
| 0.6184 | 14200 | 0.0003 | - |
| 0.6206 | 14250 | 0.0025 | - |
| 0.6227 | 14300 | 0.0008 | - |
| 0.6249 | 14350 | 0.0005 | - |
| 0.6271 | 14400 | 0.0006 | - |
| 0.6293 | 14450 | 0.0517 | - |
| 0.6315 | 14500 | 0.0005 | - |
| 0.6336 | 14550 | 0.0075 | - |
| 0.6358 | 14600 | 0.0004 | - |
| 0.6380 | 14650 | 0.0003 | - |
| 0.6402 | 14700 | 0.0003 | - |
| 0.6423 | 14750 | 0.0045 | - |
| 0.6445 | 14800 | 0.0005 | - |
| 0.6467 | 14850 | 0.0002 | - |
| 0.6489 | 14900 | 0.0125 | - |
| 0.6510 | 14950 | 0.0015 | - |
| 0.6532 | 15000 | 0.0017 | - |
| 0.6554 | 15050 | 0.0011 | - |
| 0.6576 | 15100 | 0.0207 | - |
| 0.6598 | 15150 | 0.0002 | - |
| 0.6619 | 15200 | 0.0252 | - |
| 0.6641 | 15250 | 0.0006 | - |
| 0.6663 | 15300 | 0.0015 | - |
| 0.6685 | 15350 | 0.0018 | - |
| 0.6706 | 15400 | 0.0386 | - |
| 0.6728 | 15450 | 0.0011 | - |
| 0.6750 | 15500 | 0.0003 | - |
| 0.6772 | 15550 | 0.0007 | - |
| 0.6794 | 15600 | 0.0028 | - |
| 0.6815 | 15650 | 0.0056 | - |
| 0.6837 | 15700 | 0.0005 | - |
| 0.6859 | 15750 | 0.0002 | - |
| 0.6881 | 15800 | 0.0305 | - |
| 0.6902 | 15850 | 0.0005 | - |
| 0.6924 | 15900 | 0.0018 | - |
| 0.6946 | 15950 | 0.0011 | - |
| 0.6968 | 16000 | 0.0006 | - |
| 0.6990 | 16050 | 0.0072 | - |
| 0.7011 | 16100 | 0.0224 | - |
| 0.7033 | 16150 | 0.0011 | - |
| 0.7055 | 16200 | 0.0005 | - |
| 0.7077 | 16250 | 0.0007 | - |
| 0.7098 | 16300 | 0.0005 | - |
| 0.7120 | 16350 | 0.0028 | - |
| 0.7142 | 16400 | 0.0017 | - |
| 0.7164 | 16450 | 0.2294 | - |
| 0.7185 | 16500 | 0.0253 | - |
| 0.7207 | 16550 | 0.0122 | - |
| 0.7229 | 16600 | 0.0001 | - |
| 0.7251 | 16650 | 0.0327 | - |
| 0.7273 | 16700 | 0.0042 | - |
| 0.7294 | 16750 | 0.0008 | - |
| 0.7316 | 16800 | 0.0004 | - |
| 0.7338 | 16850 | 0.0003 | - |
| 0.7360 | 16900 | 0.0005 | - |
| 0.7381 | 16950 | 0.0003 | - |
| 0.7403 | 17000 | 0.0021 | - |
| 0.7425 | 17050 | 0.2041 | - |
| 0.7447 | 17100 | 0.0002 | - |
| 0.7469 | 17150 | 0.0006 | - |
| 0.7490 | 17200 | 0.0002 | - |
| 0.7512 | 17250 | 0.0008 | - |
| 0.7534 | 17300 | 0.068 | - |
| 0.7556 | 17350 | 0.0016 | - |
| 0.7577 | 17400 | 0.0006 | - |
| 0.7599 | 17450 | 0.0005 | - |
| 0.7621 | 17500 | 0.0011 | - |
| 0.7643 | 17550 | 0.2192 | - |
| 0.7665 | 17600 | 0.0006 | - |
| 0.7686 | 17650 | 0.0003 | - |
| 0.7708 | 17700 | 0.0017 | - |
| 0.7730 | 17750 | 0.0033 | - |
| 0.7752 | 17800 | 0.0001 | - |
| 0.7773 | 17850 | 0.0011 | - |
| 0.7795 | 17900 | 0.0302 | - |
| 0.7817 | 17950 | 0.0004 | - |
| 0.7839 | 18000 | 0.2921 | - |
| 0.7860 | 18050 | 0.0001 | - |
| 0.7882 | 18100 | 0.006 | - |
| 0.7904 | 18150 | 0.0164 | - |
| 0.7926 | 18200 | 0.0003 | - |
| 0.7948 | 18250 | 0.0021 | - |
| 0.7969 | 18300 | 0.0094 | - |
| 0.7991 | 18350 | 0.002 | - |
| 0.8013 | 18400 | 0.0405 | - |
| 0.8035 | 18450 | 0.001 | - |
| 0.8056 | 18500 | 0.2594 | - |
| 0.8078 | 18550 | 0.0075 | - |
| 0.8100 | 18600 | 0.0003 | - |
| 0.8122 | 18650 | 0.0009 | - |
| 0.8144 | 18700 | 0.0018 | - |
| 0.8165 | 18750 | 0.0007 | - |
| 0.8187 | 18800 | 0.0006 | - |
| 0.8209 | 18850 | 0.0009 | - |
| 0.8231 | 18900 | 0.0003 | - |
| 0.8252 | 18950 | 0.0006 | - |
| 0.8274 | 19000 | 0.0002 | - |
| 0.8296 | 19050 | 0.0004 | - |
| 0.8318 | 19100 | 0.0018 | - |
| 0.8340 | 19150 | 0.0007 | - |
| 0.8361 | 19200 | 0.0005 | - |
| 0.8383 | 19250 | 0.0206 | - |
| 0.8405 | 19300 | 0.0005 | - |
| 0.8427 | 19350 | 0.1918 | - |
| 0.8448 | 19400 | 0.0093 | - |
| 0.8470 | 19450 | 0.0032 | - |
| 0.8492 | 19500 | 0.0004 | - |
| 0.8514 | 19550 | 0.1727 | - |
| 0.8535 | 19600 | 0.2034 | - |
| 0.8557 | 19650 | 0.0007 | - |
| 0.8579 | 19700 | 0.0004 | - |
| 0.8601 | 19750 | 0.0001 | - |
| 0.8623 | 19800 | 0.0024 | - |
| 0.8644 | 19850 | 0.0122 | - |
| 0.8666 | 19900 | 0.0003 | - |
| 0.8688 | 19950 | 0.0093 | - |
| 0.8710 | 20000 | 0.0003 | - |
| 0.8731 | 20050 | 0.0007 | - |
| 0.8753 | 20100 | 0.0044 | - |
| 0.8775 | 20150 | 0.0006 | - |
| 0.8797 | 20200 | 0.0002 | - |
| 0.8819 | 20250 | 0.0003 | - |
| 0.8840 | 20300 | 0.0024 | - |
| 0.8862 | 20350 | 0.0051 | - |
| 0.8884 | 20400 | 0.0767 | - |
| 0.8906 | 20450 | 0.0004 | - |
| 0.8927 | 20500 | 0.0002 | - |
| 0.8949 | 20550 | 0.0007 | - |
| 0.8971 | 20600 | 0.0012 | - |
| 0.8993 | 20650 | 0.0004 | - |
| 0.9015 | 20700 | 0.0003 | - |
| 0.9036 | 20750 | 0.0002 | - |
| 0.9058 | 20800 | 0.0005 | - |
| 0.9080 | 20850 | 0.0007 | - |
| 0.9102 | 20900 | 0.0006 | - |
| 0.9123 | 20950 | 0.2469 | - |
| 0.9145 | 21000 | 0.0002 | - |
| 0.9167 | 21050 | 0.0009 | - |
| 0.9189 | 21100 | 0.002 | - |
| 0.9210 | 21150 | 0.0027 | - |
| 0.9232 | 21200 | 0.0007 | - |
| 0.9254 | 21250 | 0.0008 | - |
| 0.9276 | 21300 | 0.0265 | - |
| 0.9298 | 21350 | 0.0019 | - |
| 0.9319 | 21400 | 0.0003 | - |
| 0.9341 | 21450 | 0.0064 | - |
| 0.9363 | 21500 | 0.0003 | - |
| 0.9385 | 21550 | 0.0015 | - |
| 0.9406 | 21600 | 0.0002 | - |
| 0.9428 | 21650 | 0.0015 | - |
| 0.9450 | 21700 | 0.1497 | - |
| 0.9472 | 21750 | 0.1422 | - |
| 0.9494 | 21800 | 0.0001 | - |
| 0.9515 | 21850 | 0.0007 | - |
| 0.9537 | 21900 | 0.0053 | - |
| 0.9559 | 21950 | 0.0002 | - |
| 0.9581 | 22000 | 0.0003 | - |
| 0.9602 | 22050 | 0.1234 | - |
| 0.9624 | 22100 | 0.2087 | - |
| 0.9646 | 22150 | 0.0005 | - |
| 0.9668 | 22200 | 0.0001 | - |
| 0.9690 | 22250 | 0.0003 | - |
| 0.9711 | 22300 | 0.0004 | - |
| 0.9733 | 22350 | 0.0014 | - |
| 0.9755 | 22400 | 0.0021 | - |
| 0.9777 | 22450 | 0.0105 | - |
| 0.9798 | 22500 | 0.0009 | - |
| 0.9820 | 22550 | 0.0003 | - |
| 0.9842 | 22600 | 0.0006 | - |
| 0.9864 | 22650 | 0.0007 | - |
| 0.9885 | 22700 | 0.0021 | - |
| 0.9907 | 22750 | 0.003 | - |
| 0.9929 | 22800 | 0.0099 | - |
| 0.9951 | 22850 | 0.001 | - |
| 0.9973 | 22900 | 0.0521 | - |
| 0.9994 | 22950 | 0.0003 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.2
- PyTorch: 2.0.1
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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| null |
Non_BioNLP
|
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 64 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>"yeah and in case you you forget it's actually future date your data so that the one aah you know you know when the bill cycle comes you would just need to pay the the amount of the new of the new plan and you should be good to go"</li><li>"you are eligible for is four one for one upright discount if you want to have once you have a cell phone for free you can uh bring it bring a number from in our account our company explode in verizon i'm i'm sorry it's closed exploding an a t and t"</li><li>"okay so i'm gonna look into look into this of an s that i'll just have have to check in our system if the phone is right one is already eligible for the unlock code okay i'll just have to place you on hold for a minute or two to i'll gonna get right back to you just don't hang up all right all right so"</li></ul> |
| 20 | <ul><li>'hmm hmm alright alright just a moment alright great like rechecking further as of you will be checking your e b b b alright so for your your e bebe this will be applied to your to your account on the twenty third third of august yet in the amount of fifty dollars dollars so currently aah for your because your cycle has has started so your autopay has they have taken an amount of seventy one twenty one dollars for your bill now when when that fifty dollar will come in that will be will be charged to the next billing so on so on your next billing so your your aah billed you would only be twenty one ninety one dollars'</li><li>'will be charged'</li><li>"thank you right right so while i'm pulling up your account here like the like to tell you about aah autopay so for so for this one here autopay is actually an auto an auto payment in which aah they they uh we take the aah payment on and on your card a day before your due date due date so where this one here upon checking here checking use your next two days is on the sixth of september number which means that your card will be will be charged on the fifth of the number and and on the fourth you will actually be texted and i'll be reminded that our system system we'll take the payment on the card a baby a day before the due date okay"</li></ul> |
| 11 | <ul><li>'shit sorry can you sorry can you say that one more time'</li><li>'oh bitch pay'</li><li>'moron'</li></ul> |
| 16 | <ul><li>'i was not trying to rush you into a decision'</li><li>'understand your concern'</li><li>'your concern is valid'</li></ul> |
| 35 | <ul><li>"aah uh okay if ever though aah aah you decide to aah get a new a new phone you're eligible for this time that aah the phones from our cricket store and from our website website aah just get a quick a cricket phone to make sure that we can really get we guarantee that are hot spot will work on it you can aah get discounted cricket phone the phones okay"</li><li>"able to have it replaced for these um usage of a year here so since you do have your a device we simply been activated that you're actually currently um eligible for the warranty"</li><li>'aah okay i okay aah yeah sure um yes um peggy take your account is eligible for um which bridge pay'</li></ul> |
| 21 | <ul><li>'thank you would you mind if i can add not going to ask to put you on hold for a manager okay'</li><li>'thank you the four digit pin under your account'</li><li>'thank you what is the what is the four digit pin on the trip that account'</li></ul> |
| 46 | <ul><li>"just wanna make sure that sure the seat like to get you an answer to see if it's maybe just something just something in the maintenance because if"</li><li>'yes sir thank you so much for making your bill your payment that we already have receive what happened you did you did you receive the message on for me sign in your end and just to make sure'</li><li>"okay so you're so you're trying to process on the i v r on the automated system which system"</li></ul> |
| 47 | <ul><li>'network is not stable'</li><li>"can't connect"</li><li>'drop my call'</li></ul> |
| 6 | <ul><li>"i'm sorry is that and emelia"</li><li>'i d i do understand you let me go ahead and check your information here on your phone okay'</li><li>"i see i see right now oh i got something here thing here already i noticed it by the way let me assist you with since you with this i'm sorry that happened but we'll see what's going on going on i get an account and get the name of calvin birth been burns west"</li></ul> |
| 51 | <ul><li>"hmm hmm hmm hmm i can barely hear you sir i'm sorry"</li><li>"i can't hear you"</li><li>'no i say that again'</li></ul> |
| 27 | <ul><li>'oh i understand and yes i can definitely assist you with be doing that assist me so can i can i please have your phone number'</li><li>"if you're having having a hard time to do it on your phone uh to call with me with me then you can just go ahead and aah visit a visit at the store and they can actually assist you with and even they can go ahead and uh you know uh click on it with uh forget pass got password they're using your device and that's and that's the best option for you um to call if you're having have the option to do"</li><li>"thanks for calling to try to me destination my name is that where it's how can i help you"</li></ul> |
| 19 | <ul><li>'buy cable protector'</li><li>'get screen protection'</li><li>'need cable protector'</li></ul> |
| 62 | <ul><li>'arsehole'</li><li>'fool'</li><li>'bastard'</li></ul> |
| 12 | <ul><li>"i just want i just want the number it's this not not a transfer anything for a new phone it's phone it's the same phone"</li><li>'when i got i got the other new phones on'</li><li>'aah yeah do you need to you need to put my new aah i m e i number in from in for my new phone'</li></ul> |
| 13 | <ul><li>'web site was confusing'</li><li>'placeholder'</li><li>'could not find app'</li></ul> |
| 56 | <ul><li>"yes yesterday yesterday i called to add her mobile hot spot to my phone and then phone and it isn't working"</li><li>"remember to sent the customer for their business hi frank uh this is aah meredith there it is i was calling aah to see to see what we can do about my cell phone i got it hmm uh maybe a week or two ago i mean i and it's coming up restricted or private five it when i called people it's not even showing my number my number and i know it's nothing in my phone in my phone or if it is i haven't learned how to fix it"</li><li>"uh huh oh shit i'm by the little boy almost does it is there no knowing some come up come up please stay on the line yes that's what i need my gas for for people that so have no z four for people in the philippines they don't they don't know what they don't know what do you then hit no other information phone number nothing to do all that yeah with the mail when i called what other m b a"</li></ul> |
| 31 | <ul><li>"yes of course aah she can just you know really visa i mean visit our website cause it round while dot com and you know see a c a check for this phone and you can just and you can just you know um select the option that and that can customer and i'm upgrading my phone phone and just add to the card and you know you know it will continuously have have it place you'll just need to put your bill you know billing address that you're info so that you could have you can have it successfully"</li><li>'yeah yeah you can go to the store uh by twenty the twenty first'</li><li>"gigabytes okay okay she's aah just still work still working on it by the way if you have any have any increase you can also visit our website and you can and you can make a payment online are our cricket up for you to avoid for four dollars of assistance fee"</li></ul> |
| 49 | <ul><li>"let me double check hold check hold on um we actually actually have as well the samsung galaxy i'll see a twelve for or aah one hundred nine dollars and ninety nine cents nine cents"</li><li>'thank you very much let me go ahead and process the payment the payment for just allow me one moment'</li><li>'okay so let me see okay mister and what you say you were trying to ask to activate a new device right'</li></ul> |
| 15 | <ul><li>"all that's all okay thank you so much how much glad i was able to help out online lucky your device your device once again the supervisor from cricket do you have a great day and stay safe bye now"</li><li>'no no no when i was um um the last person that i talked to the one i hung up hung up on me was supposed to be the supervisor okay'</li><li>"and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on"</li></ul> |
| 60 | <ul><li>'okay okay do you have this this number this number next to you right now this device'</li><li>'uh i mean do you want to speak with a spam with a spanish the sedative'</li><li>'to go and content the number her aah let me know if you get the i d the i m e i or if the call will went through through and then um once you have do you have it just put me on hold and i will be waiting for the for the i m e i d okay'</li></ul> |
| 7 | <ul><li>"i i can't fight it but it's frustrating you know"</li><li>"and and sometimes it's like the weather i hear i hear a horrible well"</li><li>"i just need to work working phone and and it's it's been like oh like a ridiculous you two months about this office and then when i called and they told me they told me that i have the proof of this whole time but it doesn't make sense because i just was never never told that"</li></ul> |
| 33 | <ul><li>'i hate the new system'</li><li>'do what ever you want'</li><li>'i do not have time for your stories'</li></ul> |
| 4 | <ul><li>'hello welcome to correct to cricket this is my nonna can i get your name please please'</li><li>'welcome to cricket click admission my name is john can i have your name please name please'</li><li>"thank you for calling verizon wireless my name yes my name is better which one will i be having a pleasure let's you're speaking with"</li></ul> |
| 3 | <ul><li>'thank you so very much for waiting waiting and i was able to verify all this information in the house and the hot spot has been successfully add it just to make sure to make sure the services are working aah uh i can give you a call back but what you will need to do be do be to restart your phone turn it off and then turn it back turn it back on and then i can give you a call back in call back in two to three minutes'</li><li>"okay okay you know you're almost there it's until september september the twenty sixth that this phone is due is due to be unlocked"</li><li>'okay can you try to go to go and not just like to internet google google good up or something like that there'</li></ul> |
| 61 | <ul><li>'very good thank you so much'</li><li>'so much higher i appreciate that'</li><li>"okay well thank you so i'm not sure"</li></ul> |
| 22 | <ul><li>'i was put on hold'</li><li>'i v r is terrible'</li><li>'i v r has too many options'</li></ul> |
| 30 | <ul><li>'service has been a bit unpredictable'</li><li>'outage will continue'</li><li>'network outages'</li></ul> |
| 48 | <ul><li>"was able to resolve it it's of your concern today but simply performing it troubleshooting shipping for your service uh miss is everything"</li><li>"you're welcome and before i let you go to i just want to just want to let you know i'm happy i was able to resolve your concern your concern"</li><li>"hmm but the question is how come i wasn't able to able to when i go there and now i'm not able to connect when i so um"</li></ul> |
| 39 | <ul><li>'send the document'</li><li>'email the document'</li><li>'email the details'</li></ul> |
| 10 | <ul><li>'all right well thank you so much for that one for that one month um can you please tell me how can i assist you today'</li><li>"you're welcome other back thank you so much for your patience patience um i already restore the line"</li><li>"okay okay all righty aah aah we'll do that today thank you so much"</li></ul> |
| 57 | <ul><li>'to add line'</li><li>'add a new one'</li><li>'like to add a line'</li></ul> |
| 50 | <ul><li>"all right okay that's fine so on the records answer the and before we can to cancel the line of what i can see it here you do you have three lines lines active for fifty five dollar plan a plan so on the monthly basis you're paying one one hundred one dollars because there is like an insurance sure it's on the line and then once it would be me remove the insurance also be removed it as well this line okay and once it will be you will be removed there's also changes on the bill so if the line will be canceled it will be out of the automated that it was aah the effective today though okay that will be cancelling the line and then the line the line going to be ported out anymore and also so then you service as of the line will not be refund refunded nor credited and that's what i can see here here on the eighteenth would be your do right"</li><li>"or the kitchen so before before we can do to where it's supposed to be used let me pull me once you cancel the line if you change your change your mind you will need to pay fifteen dollars it's for the reactivation"</li><li>"okay so before we cancel the line is there some there's something else on your account that i can help you with or with or only will be the cancellation"</li></ul> |
| 8 | <ul><li>'okay just stay on the line while i work on your on your account can i put you on a brief hold a logging into logging'</li><li>'okay let me let me put you on put you on a brief hold for a moment okay'</li><li>'please christopher open aah confirm it here material quick i place you on hold for a minute one moment'</li></ul> |
| 36 | <ul><li>"yeah sure thing sure thank you so much for your patience waiting on the line and and and i'm so sorry for the long hold while i while i really do appreciate the again i understand how important port and this is for you and appreciate also that time time that you really drop by on the tickets to worry about this about this concern but as i check for back further here um as i can it's confirmed it it's really not in the sim card but okay but instead on the device that you're using right using right now aah the i m e i i of the device ending in zero nine zero nine zero five this phone is no longer no longer compatible here with a service it it said cricket and that's the reason right now now that the service will not work call because of the phone so we can we can confirm right here that on the like lines that things that you have right here on the account this is the only one one that is currently an active so i even way like it like either to inquire on the crickets for it's for because we can see right here if maybe you are you are qualified for a device upgrade or maybe if you have if you have a spare phone now we can check if this is the this is still compatible with the service at cricket because we can update the i m e i of i of this phone"</li><li>"hmm okay so if you're if you were to have it activated right right now you the the phone will be eligible for an locked bye by upto per ten"</li><li>"if you upgrade as per as per checking here you're eligible for a device that hi discount so that means if you plan you plan on upgrading your device you can definitely and i only have a discount as long as you're logged log in your account over cricket wireless dot com com you can choose your device will recover device device if you should be able to receive a discount discount"</li></ul> |
| 54 | <ul><li>'thank you got this one not this one here and this is for me to send to get your get your account can i have the four digit pin please'</li><li>'the forgot user name'</li><li>'oh four alrighty all righty thank you and how about the four digit pin digit pin on the account to verify'</li></ul> |
| 63 | <ul><li>'um are there any way i can call back back in a few minutes'</li><li>"the aah assurance taken off of the other line other line that's on my account but aah okay you got already a letter to the end of it told me that because my account was account was credited eight dollars from the check in the insurance off i thought i only two dollars so when i call back uh to so the card information to you to pay it it tells me that aah or whoever whoever told me that there is no no um i haven't shown in the system but there was any changes made"</li><li>"i will give you all a call back when i'm ready i'm ready"</li></ul> |
| 43 | <ul><li>'i know who i am seeing'</li><li>"add add in that case i'm glad i was able to provide the details this is my callaghan from cricket stay safe always and bye for now"</li><li>'welcome to cricket the coordination this is josie may i have your name please please'</li></ul> |
| 55 | <ul><li>'and one day at one on my account not the not and they brought a blocked or lot layest balance and she said it said it would phone was sound to call back all that and they did take the block fully at all all'</li><li>"thank you for free you were picking up the phone call but anyway if there's anything else feel free feel free to call us back where aah always open aah before also nine"</li><li>"in this case then um mister mister walker where we'll need to do is i will need you to wanting you to turn off your device for uh um five minutes and then turn it back on and i and i'll be calling them back in around ten minutes okay for you not you not to call back customer care new for you not to wait and to wait anymore okay"</li></ul> |
| 5 | <ul><li>"yes sir in this case uh from meanwhile it we're doing this jamie name is uh you can also get enroll or roll on the autopay you have aah you have a single line uh recommend you to ready to do that so you can have the five dollar discount setup in"</li><li>"okay okay and you may add on a debit or credit card with them them and aah that will provide you five dollar discount a discount on your next month's bill right"</li><li>"okay and usually usually um regarding here on the account do you have autopay autopay on the account and that's why i'm just aah we're just paying for fifty eight dollars a month instead of start of sixty three so here it's aah um too early for us to like give the credit credit on the account because we're crediting over give were giving the five dollar discount or credit aah aah five days before the due date to tech so technically that will be on the twentieth twenty second of august the starting that day what day you can see that the new amount is just it's just fifty eight instead of sixty three okay"</li></ul> |
| 14 | <ul><li>'hold on hold on twenty alright allen so i'</li><li>'okay yeah then well hold on hold on a shit to that should i would like to me maybe somebody know lane okay aah are you ready'</li><li>"so hold on hold on we're on the side okay i sent a text message search that you receive uh any text message text message coming from cricket"</li></ul> |
| 2 | <ul><li>"wasn't aware of the fee"</li><li>'a lot of hidden cost'</li><li>'high price'</li></ul> |
| 58 | <ul><li>"i'm gonna go ahead and you transfer"</li><li>"i'm happy to transfer you"</li><li>'uh yes i can transfer transfer you to record to the insurance aah i can aah i can give you the number there'</li></ul> |
| 37 | <ul><li>'okay tell me apart from that anything else that you might want you might want to apply to your account'</li><li>'this do you have any other concerns'</li><li>'no problem and yeah and yes now that we have successfully aah process aah process the payment andrea do you have any other question the questions or concerns'</li></ul> |
| 23 | <ul><li>"okay just timing can i be lohman here okay perfect just a moment one moment here it may i please you in a brief hold ma'am"</li><li>'okay just stay on the line while i work on your on your account can i put you on a brief hold a logging into logging'</li><li>"okay thank you so much for that information shannon for your help as well now let me go ahead go ahead and just work on my system i cage if you don't mind don't mind going to put you just on a brief hold once i have the i have"</li></ul> |
| 52 | <ul><li>"thank you you're welcome"</li><li>"all i'm really i'm really sorry that your son is having issues with this so the services no worries let me pull up your account account so i could help you check it may i have your i have your"</li><li>"no worries no worries no worries thank you very much for verifying the confirmation make sure that i hear me okay yeah no worries take your time when you're ready tony okay tony okay"</li></ul> |
| 28 | <ul><li>"i do not i do not have the wifi on um and i only have my daughter on like i been i've been i've been dealing with us from going on three three days and i'm like on so so frustrated cricket stores i tried calling calling them right please are closed today cause labor day okay"</li><li>"yeah well aah i i think you need to have your manager go back and listen to the into the call that's recorded because clearly he told me on help me on the phone that i could say uh my bank is thank you should probably have your manager go back and listen to the to the phone call so she can hear what he said"</li><li>'aah if you will enroll that counterfeits which way you can get seven extension mention aah for example you an android that kind of which the bitch paid today your aah your your um your page pay your extension this time start on the twenty second upon but yeah one two nine two three four five six saffron up until the twenty eight twenty eight of august'</li></ul> |
| 59 | <ul><li>'instructions not clear'</li><li>'waiting for installation'</li><li>'slow installation process'</li></ul> |
| 53 | <ul><li>"thank you so much and i'm glad i was and i was able to resolve your concern by uh now so that you can now enjoy the retail services once once again nicole this is berna and thank you purchasing cricket and have a great day bye"</li><li>'thank you sir thank you so much so right now sage i would just like the car to clarify are you trying to pay your bill on your bill on your online cricket account however you forgot your forgot your password or you forgot'</li><li>"okay perfect so thank you so much for being part we type precondition my name is marlin and i hope you have a wonderful day ma'am"</li></ul> |
| 45 | <ul><li>'brian high ryan how are you doing how are you doing today'</li><li>"i'm great sir um um how about you"</li><li>'agent how are you today'</li></ul> |
| 41 | <ul><li>'all right just can i can i have the card number to process the payment one more time'</li><li>'okay hmm um we will also going to uh verify that one to for you okay for you okay the way alright so let me check check here and it seems like that that the um card that decline here can you please give me please give me one more time the card number just to make sure i have'</li><li>'you know that person that you really really you know who will actually receive this phone they might be able to at least activate the one with one with cricket number that way they can actually surely continue the six months aah can you repeat it written requirements so they can use it with different carrier carrier'</li></ul> |
| 17 | <ul><li>'welcome to recap my name is sheila hello how can i help you today'</li><li>'just a great week quick recap um corban you calling because one because you wanted'</li><li>'i woke up to recap my name is sheila uh how can i help you today'</li></ul> |
| 34 | <ul><li>'i am definitely interested'</li><li>'what is the extended warranty'</li><li>'what are the accessories'</li></ul> |
| 25 | <ul><li>"my pleasure sir and and uh and uh remember also that all of your lines right now in this case this account i already active okay i don't know uh there's uh there's anything else i can do for you today mister word"</li><li>"i don't know if it's in there"</li><li>"hmm okay okay remember that some well oh i old i don't know if you have well well do you have a p s four o a place agent or an excellent"</li></ul> |
| 24 | <ul><li>'choosing cricket just my name is danielle how may i have the pleasure to speak to thank you'</li><li>'welcome to pick condition my name and my name is judy may i have your name please'</li><li>"me the opportunity to assist you in it see i explained to you about your services again my name is arlene enjoy the rest of the day ma'am stay safe and happy weekend"</li></ul> |
| 9 | <ul><li>'how many times to call'</li><li>'kept calling'</li><li>'did not get an answer before'</li></ul> |
| 1 | <ul><li>"okay i understand and i know it's kind of kind of frustrating to get uh try to just you just a services and that isn't working working but no worries is going to be my pleasure today sure today to check up your account and see what's going what's going on so can you please provide me your phone number number"</li><li>"hi victoria toria um thanks for patiently waiting all right so i check your account further further and i also check um the payment that you made paid and um the payment the payment was already posted um i believe leave um you made the payment today and it was so do as of um is it and it's um sixty dollars dollars right but um your account is already cancelled that's why the that's why the system will require the system will require required to pay aah are we activation fee of five days five dollars okay so looking into the account right now right now there is a balance here or here on five dollars okay so this check just charge is valid this is for for the for the activation fee okay but don't worry okay um let me go ahead go ahead and ask permission from my support my supervisor if we can do something about the five the five dollars amount onto your account okay"</li><li>"joe your account to reach pay we will extend extend your services for seven days with a process processing the full payment on the account on the on the seven days which is the twenty second second you need to process the the whole amount or you need the price to process the second payment since you're rate plan is sixty five or your bill is sixty sixty five then the only paying payment that you going to process next time is the is the amount of fifty five dollars ten ten dollars go to your services five dollars there's to your account then as well as well as you have credit as well so i'll just set up you set up the bitch pay okay"</li></ul> |
| 29 | <ul><li>'when will the enrollment be done'</li><li>'when will the offer be activated'</li><li>'how long will it take to be delivered'</li></ul> |
| 32 | <ul><li>'always data issues'</li><li>"don't use this"</li><li>'data plan not suffice'</li></ul> |
| 18 | <ul><li>'not interested'</li><li>'i dont want it'</li><li>'not for me'</li></ul> |
| 44 | <ul><li>'customer service sucks here'</li><li>'service agent scheduled didnt come'</li><li>'i have been waiting on the automated system forever'</li></ul> |
| 26 | <ul><li>'yes lets go ahead'</li><li>'sure i like the deal'</li><li>'please proceed with the offer'</li></ul> |
| 42 | <ul><li>'includes more data'</li><li>"they don't charge for"</li><li>'plan was good'</li></ul> |
| 40 | <ul><li>'upgrading to the plan will resolve this issue'</li><li>'upgrade plan to resolve issue'</li></ul> |
| 38 | <ul><li>'not a great equipment'</li><li>'battery runs out'</li><li>'problems with your equipment'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5463 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Jalajkx/all_mpnetcric-setfit-model")
# Run inference
preds = model("and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 1 | 32.4224 | 283 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 36 |
| 1 | 36 |
| 2 | 36 |
| 3 | 36 |
| 4 | 36 |
| 5 | 36 |
| 6 | 36 |
| 7 | 36 |
| 8 | 36 |
| 9 | 6 |
| 10 | 36 |
| 11 | 36 |
| 12 | 36 |
| 13 | 9 |
| 14 | 36 |
| 15 | 36 |
| 16 | 17 |
| 17 | 36 |
| 18 | 4 |
| 19 | 29 |
| 20 | 30 |
| 21 | 36 |
| 22 | 25 |
| 23 | 36 |
| 24 | 36 |
| 25 | 36 |
| 26 | 4 |
| 27 | 36 |
| 28 | 36 |
| 29 | 4 |
| 30 | 8 |
| 31 | 36 |
| 32 | 4 |
| 33 | 36 |
| 34 | 11 |
| 35 | 36 |
| 36 | 36 |
| 37 | 36 |
| 38 | 10 |
| 39 | 13 |
| 40 | 2 |
| 41 | 36 |
| 42 | 9 |
| 43 | 36 |
| 44 | 10 |
| 45 | 36 |
| 46 | 36 |
| 47 | 14 |
| 48 | 36 |
| 49 | 36 |
| 50 | 36 |
| 51 | 36 |
| 52 | 36 |
| 53 | 36 |
| 54 | 36 |
| 55 | 36 |
| 56 | 36 |
| 57 | 36 |
| 58 | 36 |
| 59 | 8 |
| 60 | 36 |
| 61 | 36 |
| 62 | 36 |
| 63 | 36 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 25
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2196 | - |
| 0.0022 | 50 | 0.2183 | - |
| 0.0044 | 100 | 0.3574 | - |
| 0.0065 | 150 | 0.1756 | - |
| 0.0087 | 200 | 0.1396 | - |
| 0.0109 | 250 | 0.2875 | - |
| 0.0131 | 300 | 0.1307 | - |
| 0.0152 | 350 | 0.1465 | - |
| 0.0174 | 400 | 0.1503 | - |
| 0.0196 | 450 | 0.1579 | - |
| 0.0218 | 500 | 0.3216 | - |
| 0.0240 | 550 | 0.2399 | - |
| 0.0261 | 600 | 0.2824 | - |
| 0.0283 | 650 | 0.1217 | - |
| 0.0305 | 700 | 0.0647 | - |
| 0.0327 | 750 | 0.2651 | - |
| 0.0348 | 800 | 0.1792 | - |
| 0.0370 | 850 | 0.1461 | - |
| 0.0392 | 900 | 0.0256 | - |
| 0.0414 | 950 | 0.1175 | - |
| 0.0435 | 1000 | 0.2394 | - |
| 0.0457 | 1050 | 0.1582 | - |
| 0.0479 | 1100 | 0.2785 | - |
| 0.0501 | 1150 | 0.0611 | - |
| 0.0523 | 1200 | 0.1937 | - |
| 0.0544 | 1250 | 0.0804 | - |
| 0.0566 | 1300 | 0.0811 | - |
| 0.0588 | 1350 | 0.0663 | - |
| 0.0610 | 1400 | 0.2148 | - |
| 0.0631 | 1450 | 0.0428 | - |
| 0.0653 | 1500 | 0.0083 | - |
| 0.0675 | 1550 | 0.0884 | - |
| 0.0697 | 1600 | 0.1341 | - |
| 0.0719 | 1650 | 0.0949 | - |
| 0.0740 | 1700 | 0.1839 | - |
| 0.0762 | 1750 | 0.2244 | - |
| 0.0784 | 1800 | 0.0309 | - |
| 0.0806 | 1850 | 0.0277 | - |
| 0.0827 | 1900 | 0.2016 | - |
| 0.0849 | 1950 | 0.1174 | - |
| 0.0871 | 2000 | 0.0942 | - |
| 0.0893 | 2050 | 0.0483 | - |
| 0.0915 | 2100 | 0.2057 | - |
| 0.0936 | 2150 | 0.0151 | - |
| 0.0958 | 2200 | 0.023 | - |
| 0.0980 | 2250 | 0.0514 | - |
| 0.1002 | 2300 | 0.1541 | - |
| 0.1023 | 2350 | 0.1426 | - |
| 0.1045 | 2400 | 0.0187 | - |
| 0.1067 | 2450 | 0.0386 | - |
| 0.1089 | 2500 | 0.274 | - |
| 0.1110 | 2550 | 0.0723 | - |
| 0.1132 | 2600 | 0.0115 | - |
| 0.1154 | 2650 | 0.053 | - |
| 0.1176 | 2700 | 0.2371 | - |
| 0.1198 | 2750 | 0.2472 | - |
| 0.1219 | 2800 | 0.0386 | - |
| 0.1241 | 2850 | 0.0159 | - |
| 0.1263 | 2900 | 0.0276 | - |
| 0.1285 | 2950 | 0.1229 | - |
| 0.1306 | 3000 | 0.0037 | - |
| 0.1328 | 3050 | 0.0029 | - |
| 0.1350 | 3100 | 0.0037 | - |
| 0.1372 | 3150 | 0.022 | - |
| 0.1394 | 3200 | 0.0389 | - |
| 0.1415 | 3250 | 0.0146 | - |
| 0.1437 | 3300 | 0.0034 | - |
| 0.1459 | 3350 | 0.0721 | - |
| 0.1481 | 3400 | 0.0462 | - |
| 0.1502 | 3450 | 0.0039 | - |
| 0.1524 | 3500 | 0.1225 | - |
| 0.1546 | 3550 | 0.0009 | - |
| 0.1568 | 3600 | 0.1005 | - |
| 0.1590 | 3650 | 0.008 | - |
| 0.1611 | 3700 | 0.121 | - |
| 0.1633 | 3750 | 0.2982 | - |
| 0.1655 | 3800 | 0.008 | - |
| 0.1677 | 3850 | 0.001 | - |
| 0.1698 | 3900 | 0.216 | - |
| 0.1720 | 3950 | 0.0458 | - |
| 0.1742 | 4000 | 0.0155 | - |
| 0.1764 | 4050 | 0.1235 | - |
| 0.1785 | 4100 | 0.0059 | - |
| 0.1807 | 4150 | 0.2421 | - |
| 0.1829 | 4200 | 0.2232 | - |
| 0.1851 | 4250 | 0.0396 | - |
| 0.1873 | 4300 | 0.2164 | - |
| 0.1894 | 4350 | 0.0839 | - |
| 0.1916 | 4400 | 0.0116 | - |
| 0.1938 | 4450 | 0.2666 | - |
| 0.1960 | 4500 | 0.0648 | - |
| 0.1981 | 4550 | 0.074 | - |
| 0.2003 | 4600 | 0.077 | - |
| 0.2025 | 4650 | 0.0739 | - |
| 0.2047 | 4700 | 0.0029 | - |
| 0.2069 | 4750 | 0.0679 | - |
| 0.2090 | 4800 | 0.0049 | - |
| 0.2112 | 4850 | 0.0281 | - |
| 0.2134 | 4900 | 0.049 | - |
| 0.2156 | 4950 | 0.0052 | - |
| 0.2177 | 5000 | 0.1657 | - |
| 0.2199 | 5050 | 0.0005 | - |
| 0.2221 | 5100 | 0.0041 | - |
| 0.2243 | 5150 | 0.0008 | - |
| 0.2265 | 5200 | 0.0587 | - |
| 0.2286 | 5250 | 0.0753 | - |
| 0.2308 | 5300 | 0.1744 | - |
| 0.2330 | 5350 | 0.0055 | - |
| 0.2352 | 5400 | 0.0023 | - |
| 0.2373 | 5450 | 0.0002 | - |
| 0.2395 | 5500 | 0.0472 | - |
| 0.2417 | 5550 | 0.0042 | - |
| 0.2439 | 5600 | 0.0137 | - |
| 0.2460 | 5650 | 0.1646 | - |
| 0.2482 | 5700 | 0.0509 | - |
| 0.2504 | 5750 | 0.0062 | - |
| 0.2526 | 5800 | 0.0019 | - |
| 0.2548 | 5850 | 0.0048 | - |
| 0.2569 | 5900 | 0.0031 | - |
| 0.2591 | 5950 | 0.0011 | - |
| 0.2613 | 6000 | 0.004 | - |
| 0.2635 | 6050 | 0.0498 | - |
| 0.2656 | 6100 | 0.0042 | - |
| 0.2678 | 6150 | 0.0018 | - |
| 0.2700 | 6200 | 0.0061 | - |
| 0.2722 | 6250 | 0.1355 | - |
| 0.2744 | 6300 | 0.0039 | - |
| 0.2765 | 6350 | 0.0044 | - |
| 0.2787 | 6400 | 0.001 | - |
| 0.2809 | 6450 | 0.0011 | - |
| 0.2831 | 6500 | 0.0302 | - |
| 0.2852 | 6550 | 0.1502 | - |
| 0.2874 | 6600 | 0.0029 | - |
| 0.2896 | 6650 | 0.0016 | - |
| 0.2918 | 6700 | 0.0232 | - |
| 0.2940 | 6750 | 0.176 | - |
| 0.2961 | 6800 | 0.0323 | - |
| 0.2983 | 6850 | 0.0818 | - |
| 0.3005 | 6900 | 0.0427 | - |
| 0.3027 | 6950 | 0.1716 | - |
| 0.3048 | 7000 | 0.0137 | - |
| 0.3070 | 7050 | 0.0032 | - |
| 0.3092 | 7100 | 0.0095 | - |
| 0.3114 | 7150 | 0.177 | - |
| 0.3135 | 7200 | 0.0005 | - |
| 0.3157 | 7250 | 0.0157 | - |
| 0.3179 | 7300 | 0.0012 | - |
| 0.3201 | 7350 | 0.0027 | - |
| 0.3223 | 7400 | 0.1351 | - |
| 0.3244 | 7450 | 0.0019 | - |
| 0.3266 | 7500 | 0.0009 | - |
| 0.3288 | 7550 | 0.2017 | - |
| 0.3310 | 7600 | 0.0059 | - |
| 0.3331 | 7650 | 0.0013 | - |
| 0.3353 | 7700 | 0.0377 | - |
| 0.3375 | 7750 | 0.0056 | - |
| 0.3397 | 7800 | 0.0055 | - |
| 0.3419 | 7850 | 0.0745 | - |
| 0.3440 | 7900 | 0.0046 | - |
| 0.3462 | 7950 | 0.002 | - |
| 0.3484 | 8000 | 0.0355 | - |
| 0.3506 | 8050 | 0.0004 | - |
| 0.3527 | 8100 | 0.0004 | - |
| 0.3549 | 8150 | 0.0072 | - |
| 0.3571 | 8200 | 0.0013 | - |
| 0.3593 | 8250 | 0.0032 | - |
| 0.3615 | 8300 | 0.0006 | - |
| 0.3636 | 8350 | 0.0095 | - |
| 0.3658 | 8400 | 0.0006 | - |
| 0.3680 | 8450 | 0.0005 | - |
| 0.3702 | 8500 | 0.0004 | - |
| 0.3723 | 8550 | 0.0019 | - |
| 0.3745 | 8600 | 0.0002 | - |
| 0.3767 | 8650 | 0.0015 | - |
| 0.3789 | 8700 | 0.0117 | - |
| 0.3810 | 8750 | 0.002 | - |
| 0.3832 | 8800 | 0.0005 | - |
| 0.3854 | 8850 | 0.0009 | - |
| 0.3876 | 8900 | 0.0041 | - |
| 0.3898 | 8950 | 0.0484 | - |
| 0.3919 | 9000 | 0.0058 | - |
| 0.3941 | 9050 | 0.0027 | - |
| 0.3963 | 9100 | 0.0002 | - |
| 0.3985 | 9150 | 0.2323 | - |
| 0.4006 | 9200 | 0.0163 | - |
| 0.4028 | 9250 | 0.0333 | - |
| 0.4050 | 9300 | 0.0033 | - |
| 0.4072 | 9350 | 0.0023 | - |
| 0.4094 | 9400 | 0.0044 | - |
| 0.4115 | 9450 | 0.0142 | - |
| 0.4137 | 9500 | 0.0261 | - |
| 0.4159 | 9550 | 0.004 | - |
| 0.4181 | 9600 | 0.027 | - |
| 0.4202 | 9650 | 0.0104 | - |
| 0.4224 | 9700 | 0.0005 | - |
| 0.4246 | 9750 | 0.2452 | - |
| 0.4268 | 9800 | 0.0069 | - |
| 0.4290 | 9850 | 0.0245 | - |
| 0.4311 | 9900 | 0.0005 | - |
| 0.4333 | 9950 | 0.0041 | - |
| 0.4355 | 10000 | 0.1058 | - |
| 0.4377 | 10050 | 0.0009 | - |
| 0.4398 | 10100 | 0.0067 | - |
| 0.4420 | 10150 | 0.0832 | - |
| 0.4442 | 10200 | 0.0016 | - |
| 0.4464 | 10250 | 0.039 | - |
| 0.4485 | 10300 | 0.0078 | - |
| 0.4507 | 10350 | 0.0013 | - |
| 0.4529 | 10400 | 0.0003 | - |
| 0.4551 | 10450 | 0.0259 | - |
| 0.4573 | 10500 | 0.008 | - |
| 0.4594 | 10550 | 0.2137 | - |
| 0.4616 | 10600 | 0.0083 | - |
| 0.4638 | 10650 | 0.0206 | - |
| 0.4660 | 10700 | 0.0039 | - |
| 0.4681 | 10750 | 0.2205 | - |
| 0.4703 | 10800 | 0.0072 | - |
| 0.4725 | 10850 | 0.0436 | - |
| 0.4747 | 10900 | 0.071 | - |
| 0.4769 | 10950 | 0.0004 | - |
| 0.4790 | 11000 | 0.0147 | - |
| 0.4812 | 11050 | 0.0095 | - |
| 0.4834 | 11100 | 0.0069 | - |
| 0.4856 | 11150 | 0.0027 | - |
| 0.4877 | 11200 | 0.0151 | - |
| 0.4899 | 11250 | 0.0076 | - |
| 0.4921 | 11300 | 0.0016 | - |
| 0.4943 | 11350 | 0.1457 | - |
| 0.4965 | 11400 | 0.1454 | - |
| 0.4986 | 11450 | 0.0013 | - |
| 0.5008 | 11500 | 0.0027 | - |
| 0.5030 | 11550 | 0.0583 | - |
| 0.5052 | 11600 | 0.0029 | - |
| 0.5073 | 11650 | 0.0139 | - |
| 0.5095 | 11700 | 0.0004 | - |
| 0.5117 | 11750 | 0.0098 | - |
| 0.5139 | 11800 | 0.0009 | - |
| 0.5160 | 11850 | 0.0003 | - |
| 0.5182 | 11900 | 0.0009 | - |
| 0.5204 | 11950 | 0.0088 | - |
| 0.5226 | 12000 | 0.0006 | - |
| 0.5248 | 12050 | 0.0014 | - |
| 0.5269 | 12100 | 0.0008 | - |
| 0.5291 | 12150 | 0.0008 | - |
| 0.5313 | 12200 | 0.0008 | - |
| 0.5335 | 12250 | 0.0005 | - |
| 0.5356 | 12300 | 0.0028 | - |
| 0.5378 | 12350 | 0.0011 | - |
| 0.5400 | 12400 | 0.0136 | - |
| 0.5422 | 12450 | 0.0318 | - |
| 0.5444 | 12500 | 0.0037 | - |
| 0.5465 | 12550 | 0.0029 | - |
| 0.5487 | 12600 | 0.0073 | - |
| 0.5509 | 12650 | 0.0099 | - |
| 0.5531 | 12700 | 0.015 | - |
| 0.5552 | 12750 | 0.0047 | - |
| 0.5574 | 12800 | 0.0891 | - |
| 0.5596 | 12850 | 0.0007 | - |
| 0.5618 | 12900 | 0.0784 | - |
| 0.5640 | 12950 | 0.0636 | - |
| 0.5661 | 13000 | 0.0029 | - |
| 0.5683 | 13050 | 0.0048 | - |
| 0.5705 | 13100 | 0.0698 | - |
| 0.5727 | 13150 | 0.0002 | - |
| 0.5748 | 13200 | 0.0734 | - |
| 0.5770 | 13250 | 0.0004 | - |
| 0.5792 | 13300 | 0.0135 | - |
| 0.5814 | 13350 | 0.0034 | - |
| 0.5835 | 13400 | 0.0018 | - |
| 0.5857 | 13450 | 0.0175 | - |
| 0.5879 | 13500 | 0.0003 | - |
| 0.5901 | 13550 | 0.0002 | - |
| 0.5923 | 13600 | 0.0032 | - |
| 0.5944 | 13650 | 0.0007 | - |
| 0.5966 | 13700 | 0.0021 | - |
| 0.5988 | 13750 | 0.0019 | - |
| 0.6010 | 13800 | 0.0006 | - |
| 0.6031 | 13850 | 0.0014 | - |
| 0.6053 | 13900 | 0.0011 | - |
| 0.6075 | 13950 | 0.2383 | - |
| 0.6097 | 14000 | 0.0009 | - |
| 0.6119 | 14050 | 0.0863 | - |
| 0.6140 | 14100 | 0.0005 | - |
| 0.6162 | 14150 | 0.0017 | - |
| 0.6184 | 14200 | 0.0003 | - |
| 0.6206 | 14250 | 0.0025 | - |
| 0.6227 | 14300 | 0.0008 | - |
| 0.6249 | 14350 | 0.0005 | - |
| 0.6271 | 14400 | 0.0006 | - |
| 0.6293 | 14450 | 0.0517 | - |
| 0.6315 | 14500 | 0.0005 | - |
| 0.6336 | 14550 | 0.0075 | - |
| 0.6358 | 14600 | 0.0004 | - |
| 0.6380 | 14650 | 0.0003 | - |
| 0.6402 | 14700 | 0.0003 | - |
| 0.6423 | 14750 | 0.0045 | - |
| 0.6445 | 14800 | 0.0005 | - |
| 0.6467 | 14850 | 0.0002 | - |
| 0.6489 | 14900 | 0.0125 | - |
| 0.6510 | 14950 | 0.0015 | - |
| 0.6532 | 15000 | 0.0017 | - |
| 0.6554 | 15050 | 0.0011 | - |
| 0.6576 | 15100 | 0.0207 | - |
| 0.6598 | 15150 | 0.0002 | - |
| 0.6619 | 15200 | 0.0252 | - |
| 0.6641 | 15250 | 0.0006 | - |
| 0.6663 | 15300 | 0.0015 | - |
| 0.6685 | 15350 | 0.0018 | - |
| 0.6706 | 15400 | 0.0386 | - |
| 0.6728 | 15450 | 0.0011 | - |
| 0.6750 | 15500 | 0.0003 | - |
| 0.6772 | 15550 | 0.0007 | - |
| 0.6794 | 15600 | 0.0028 | - |
| 0.6815 | 15650 | 0.0056 | - |
| 0.6837 | 15700 | 0.0005 | - |
| 0.6859 | 15750 | 0.0002 | - |
| 0.6881 | 15800 | 0.0305 | - |
| 0.6902 | 15850 | 0.0005 | - |
| 0.6924 | 15900 | 0.0018 | - |
| 0.6946 | 15950 | 0.0011 | - |
| 0.6968 | 16000 | 0.0006 | - |
| 0.6990 | 16050 | 0.0072 | - |
| 0.7011 | 16100 | 0.0224 | - |
| 0.7033 | 16150 | 0.0011 | - |
| 0.7055 | 16200 | 0.0005 | - |
| 0.7077 | 16250 | 0.0007 | - |
| 0.7098 | 16300 | 0.0005 | - |
| 0.7120 | 16350 | 0.0028 | - |
| 0.7142 | 16400 | 0.0017 | - |
| 0.7164 | 16450 | 0.2294 | - |
| 0.7185 | 16500 | 0.0253 | - |
| 0.7207 | 16550 | 0.0122 | - |
| 0.7229 | 16600 | 0.0001 | - |
| 0.7251 | 16650 | 0.0327 | - |
| 0.7273 | 16700 | 0.0042 | - |
| 0.7294 | 16750 | 0.0008 | - |
| 0.7316 | 16800 | 0.0004 | - |
| 0.7338 | 16850 | 0.0003 | - |
| 0.7360 | 16900 | 0.0005 | - |
| 0.7381 | 16950 | 0.0003 | - |
| 0.7403 | 17000 | 0.0021 | - |
| 0.7425 | 17050 | 0.2041 | - |
| 0.7447 | 17100 | 0.0002 | - |
| 0.7469 | 17150 | 0.0006 | - |
| 0.7490 | 17200 | 0.0002 | - |
| 0.7512 | 17250 | 0.0008 | - |
| 0.7534 | 17300 | 0.068 | - |
| 0.7556 | 17350 | 0.0016 | - |
| 0.7577 | 17400 | 0.0006 | - |
| 0.7599 | 17450 | 0.0005 | - |
| 0.7621 | 17500 | 0.0011 | - |
| 0.7643 | 17550 | 0.2192 | - |
| 0.7665 | 17600 | 0.0006 | - |
| 0.7686 | 17650 | 0.0003 | - |
| 0.7708 | 17700 | 0.0017 | - |
| 0.7730 | 17750 | 0.0033 | - |
| 0.7752 | 17800 | 0.0001 | - |
| 0.7773 | 17850 | 0.0011 | - |
| 0.7795 | 17900 | 0.0302 | - |
| 0.7817 | 17950 | 0.0004 | - |
| 0.7839 | 18000 | 0.2921 | - |
| 0.7860 | 18050 | 0.0001 | - |
| 0.7882 | 18100 | 0.006 | - |
| 0.7904 | 18150 | 0.0164 | - |
| 0.7926 | 18200 | 0.0003 | - |
| 0.7948 | 18250 | 0.0021 | - |
| 0.7969 | 18300 | 0.0094 | - |
| 0.7991 | 18350 | 0.002 | - |
| 0.8013 | 18400 | 0.0405 | - |
| 0.8035 | 18450 | 0.001 | - |
| 0.8056 | 18500 | 0.2594 | - |
| 0.8078 | 18550 | 0.0075 | - |
| 0.8100 | 18600 | 0.0003 | - |
| 0.8122 | 18650 | 0.0009 | - |
| 0.8144 | 18700 | 0.0018 | - |
| 0.8165 | 18750 | 0.0007 | - |
| 0.8187 | 18800 | 0.0006 | - |
| 0.8209 | 18850 | 0.0009 | - |
| 0.8231 | 18900 | 0.0003 | - |
| 0.8252 | 18950 | 0.0006 | - |
| 0.8274 | 19000 | 0.0002 | - |
| 0.8296 | 19050 | 0.0004 | - |
| 0.8318 | 19100 | 0.0018 | - |
| 0.8340 | 19150 | 0.0007 | - |
| 0.8361 | 19200 | 0.0005 | - |
| 0.8383 | 19250 | 0.0206 | - |
| 0.8405 | 19300 | 0.0005 | - |
| 0.8427 | 19350 | 0.1918 | - |
| 0.8448 | 19400 | 0.0093 | - |
| 0.8470 | 19450 | 0.0032 | - |
| 0.8492 | 19500 | 0.0004 | - |
| 0.8514 | 19550 | 0.1727 | - |
| 0.8535 | 19600 | 0.2034 | - |
| 0.8557 | 19650 | 0.0007 | - |
| 0.8579 | 19700 | 0.0004 | - |
| 0.8601 | 19750 | 0.0001 | - |
| 0.8623 | 19800 | 0.0024 | - |
| 0.8644 | 19850 | 0.0122 | - |
| 0.8666 | 19900 | 0.0003 | - |
| 0.8688 | 19950 | 0.0093 | - |
| 0.8710 | 20000 | 0.0003 | - |
| 0.8731 | 20050 | 0.0007 | - |
| 0.8753 | 20100 | 0.0044 | - |
| 0.8775 | 20150 | 0.0006 | - |
| 0.8797 | 20200 | 0.0002 | - |
| 0.8819 | 20250 | 0.0003 | - |
| 0.8840 | 20300 | 0.0024 | - |
| 0.8862 | 20350 | 0.0051 | - |
| 0.8884 | 20400 | 0.0767 | - |
| 0.8906 | 20450 | 0.0004 | - |
| 0.8927 | 20500 | 0.0002 | - |
| 0.8949 | 20550 | 0.0007 | - |
| 0.8971 | 20600 | 0.0012 | - |
| 0.8993 | 20650 | 0.0004 | - |
| 0.9015 | 20700 | 0.0003 | - |
| 0.9036 | 20750 | 0.0002 | - |
| 0.9058 | 20800 | 0.0005 | - |
| 0.9080 | 20850 | 0.0007 | - |
| 0.9102 | 20900 | 0.0006 | - |
| 0.9123 | 20950 | 0.2469 | - |
| 0.9145 | 21000 | 0.0002 | - |
| 0.9167 | 21050 | 0.0009 | - |
| 0.9189 | 21100 | 0.002 | - |
| 0.9210 | 21150 | 0.0027 | - |
| 0.9232 | 21200 | 0.0007 | - |
| 0.9254 | 21250 | 0.0008 | - |
| 0.9276 | 21300 | 0.0265 | - |
| 0.9298 | 21350 | 0.0019 | - |
| 0.9319 | 21400 | 0.0003 | - |
| 0.9341 | 21450 | 0.0064 | - |
| 0.9363 | 21500 | 0.0003 | - |
| 0.9385 | 21550 | 0.0015 | - |
| 0.9406 | 21600 | 0.0002 | - |
| 0.9428 | 21650 | 0.0015 | - |
| 0.9450 | 21700 | 0.1497 | - |
| 0.9472 | 21750 | 0.1422 | - |
| 0.9494 | 21800 | 0.0001 | - |
| 0.9515 | 21850 | 0.0007 | - |
| 0.9537 | 21900 | 0.0053 | - |
| 0.9559 | 21950 | 0.0002 | - |
| 0.9581 | 22000 | 0.0003 | - |
| 0.9602 | 22050 | 0.1234 | - |
| 0.9624 | 22100 | 0.2087 | - |
| 0.9646 | 22150 | 0.0005 | - |
| 0.9668 | 22200 | 0.0001 | - |
| 0.9690 | 22250 | 0.0003 | - |
| 0.9711 | 22300 | 0.0004 | - |
| 0.9733 | 22350 | 0.0014 | - |
| 0.9755 | 22400 | 0.0021 | - |
| 0.9777 | 22450 | 0.0105 | - |
| 0.9798 | 22500 | 0.0009 | - |
| 0.9820 | 22550 | 0.0003 | - |
| 0.9842 | 22600 | 0.0006 | - |
| 0.9864 | 22650 | 0.0007 | - |
| 0.9885 | 22700 | 0.0021 | - |
| 0.9907 | 22750 | 0.003 | - |
| 0.9929 | 22800 | 0.0099 | - |
| 0.9951 | 22850 | 0.001 | - |
| 0.9973 | 22900 | 0.0521 | - |
| 0.9994 | 22950 | 0.0003 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.2
- PyTorch: 2.0.1
- Datasets: 2.16.1
- Tokenizers: 0.15.0
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
{"base_model": "sentence-transformers/all-mpnet-base-v2", "library_name": "setfit", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "widget": [{"text": "so the representative presentative that i talked to a nine o'clock this morning but it said that they were gonna call the customer service my service or whatever that they were gonna call to to see if they dispute it and i didn't get a call back bags i'm a mother of three kids i worked were two job i can't have a phone that that's not on and i don't have the money the money to pay a hundred and fifty six eighty six dollars you guys want me to pay right now it's restore my service"}, {"text": "i understand kelly yes let me send you a little bit about be a moment okay is is that the pin number that you have the four days four digits that you have with us is"}, {"text": "yeah 'cause that that's that's really that's ridiculous you know that's ridiculous for her to do that i like i have you know all the time in the world okay all right okay okay no i got my card no cathy she asked me for my last time and i did not have it i'm"}, {"text": "ma'am uh thanks for holding for holding by the way everything upon checking i'm checking here a record ma'am aah for your for your current a hot spot you said you said you still have twenty gigabytes left"}, {"text": "and then when i asked i didn't even get to speak to the supervisor provider i just got hung up on"}], "inference": true, "model-index": [{"name": "SetFit with sentence-transformers/all-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.5463414634146342, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,968 |
dangvantuan/CrossEncoder-camembert-large
|
dangvantuan
|
sentence-similarity
|
[
"transformers",
"pytorch",
"safetensors",
"camembert",
"text-classification",
"Text",
"Sentence Similarity",
"Sentence-Embedding",
"camembert-base",
"sentence-similarity",
"fr",
"dataset:stsb_multi_mt",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2022-03-28T13:19:00Z |
2025-04-02T15:49:57+00:00
| 712 | 16 |
---
datasets:
- stsb_multi_mt
language: fr
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- Text
- Sentence Similarity
- Sentence-Embedding
- camembert-base
model-index:
- name: sentence-camembert-base by Van Tuan DANG
results:
- task:
type: Text Similarity
name: Sentence-Embedding
dataset:
name: Text Similarity fr
type: stsb_multi_mt
args: fr
metrics:
- type: Pearson_correlation_coefficient
value: xx.xx
name: Test Pearson correlation coefficient
---
## Model
Cross-Encoder for sentence-similarity
This model was trained using [sentence-transformers](https://www.SBERT.net) Cross-Encoder class.
## Training Data
This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('dangvantuan/CrossEncoder-camembert-large', max_length=128)
scores = model.predict([('Un avion est en train de décoller.', "Un homme joue d'une grande flûte."), ("Un homme étale du fromage râpé sur une pizza.", "Une personne jette un chat au plafond") ])
```
## Evaluation
The model can be evaluated as follows on the French test data of stsb.
```python
from sentence_transformers.readers import InputExample
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
from datasets import load_dataset
def convert_dataset(dataset):
dataset_samples=[]
for df in dataset:
score = float(df['similarity_score'])/5.0 # Normalize score to range 0 ... 1
inp_example = InputExample(texts=[df['sentence1'],
df['sentence2']], label=score)
dataset_samples.append(inp_example)
return dataset_samples
# Loading the dataset for evaluation
df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
df_test = load_dataset("stsb_multi_mt", name="fr", split="test")
# Convert the dataset for evaluation
# For Dev set:
dev_samples = convert_dataset(df_dev)
val_evaluator = CECorrelationEvaluator.from_input_examples(dev_samples, name='sts-dev')
val_evaluator(model, output_path="./")
# For Test set
test_samples = convert_dataset(df_test)
test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test')
test_evaluator(models, output_path="./")
```
**Test Result**:
The performance is measured using Pearson and Spearman correlation:
- On dev
| Model | Pearson correlation | Spearman correlation | #params |
| ------------- | ------------- | ------------- |------------- |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M |
- On test
| Model | Pearson correlation | Spearman correlation |
| ------------- | ------------- | ------------- |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 88.16 | 87.57|
| null |
Non_BioNLP
|
## Model
Cross-Encoder for sentence-similarity
This model was trained using [sentence-transformers](https://www.SBERT.net) Cross-Encoder class.
## Training Data
This model was trained on the [STS benchmark dataset](https://huggingface.co/datasets/stsb_multi_mt/viewer/fr/train). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('dangvantuan/CrossEncoder-camembert-large', max_length=128)
scores = model.predict([('Un avion est en train de décoller.', "Un homme joue d'une grande flûte."), ("Un homme étale du fromage râpé sur une pizza.", "Une personne jette un chat au plafond") ])
```
## Evaluation
The model can be evaluated as follows on the French test data of stsb.
```python
from sentence_transformers.readers import InputExample
from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator
from datasets import load_dataset
def convert_dataset(dataset):
dataset_samples=[]
for df in dataset:
score = float(df['similarity_score'])/5.0 # Normalize score to range 0 ... 1
inp_example = InputExample(texts=[df['sentence1'],
df['sentence2']], label=score)
dataset_samples.append(inp_example)
return dataset_samples
# Loading the dataset for evaluation
df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
df_test = load_dataset("stsb_multi_mt", name="fr", split="test")
# Convert the dataset for evaluation
# For Dev set:
dev_samples = convert_dataset(df_dev)
val_evaluator = CECorrelationEvaluator.from_input_examples(dev_samples, name='sts-dev')
val_evaluator(model, output_path="./")
# For Test set
test_samples = convert_dataset(df_test)
test_evaluator = CECorrelationEvaluator.from_input_examples(test_samples, name='sts-test')
test_evaluator(models, output_path="./")
```
**Test Result**:
The performance is measured using Pearson and Spearman correlation:
- On dev
| Model | Pearson correlation | Spearman correlation | #params |
| ------------- | ------------- | ------------- |------------- |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 90.11 |90.01 | 336M |
- On test
| Model | Pearson correlation | Spearman correlation |
| ------------- | ------------- | ------------- |
| [dangvantuan/CrossEncoder-camembert-large](https://huggingface.co/dangvantuan/CrossEncoder-camembert-large)| 88.16 | 87.57|
|
{"datasets": ["stsb_multi_mt"], "language": "fr", "license": "apache-2.0", "pipeline_tag": "text-ranking", "tags": ["Text", "Sentence Similarity", "Sentence-Embedding", "camembert-base"], "model-index": [{"name": "sentence-camembert-base by Van Tuan DANG", "results": [{"task": {"type": "Text Similarity", "name": "Sentence-Embedding"}, "dataset": {"name": "Text Similarity fr", "type": "stsb_multi_mt", "args": "fr"}, "metrics": [{"type": "Pearson_correlation_coefficient", "value": "xx.xx", "name": "Test Pearson correlation coefficient"}]}]}]}
|
task
|
[
"SEMANTIC_SIMILARITY"
] | 41,970 |
Helsinki-NLP/opus-mt-en-nic
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"en",
"sn",
"rw",
"wo",
"ig",
"sg",
"ee",
"zu",
"lg",
"ts",
"ln",
"ny",
"yo",
"rn",
"xh",
"nic",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T11:30:38+00:00
| 19 | 0 |
---
language:
- en
- sn
- rw
- wo
- ig
- sg
- ee
- zu
- lg
- ts
- ln
- ny
- yo
- rn
- xh
- nic
license: apache-2.0
tags:
- translation
---
### eng-nic
* source group: English
* target group: Niger-Kordofanian languages
* OPUS readme: [eng-nic](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-nic/README.md)
* model: transformer
* source language(s): eng
* target language(s): bam_Latn ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng-bam.eng.bam | 6.2 | 0.029 |
| Tatoeba-test.eng-ewe.eng.ewe | 4.5 | 0.258 |
| Tatoeba-test.eng-ful.eng.ful | 0.5 | 0.073 |
| Tatoeba-test.eng-ibo.eng.ibo | 3.9 | 0.267 |
| Tatoeba-test.eng-kin.eng.kin | 6.4 | 0.475 |
| Tatoeba-test.eng-lin.eng.lin | 1.2 | 0.308 |
| Tatoeba-test.eng-lug.eng.lug | 3.9 | 0.405 |
| Tatoeba-test.eng.multi | 11.1 | 0.427 |
| Tatoeba-test.eng-nya.eng.nya | 14.0 | 0.622 |
| Tatoeba-test.eng-run.eng.run | 13.6 | 0.477 |
| Tatoeba-test.eng-sag.eng.sag | 5.5 | 0.199 |
| Tatoeba-test.eng-sna.eng.sna | 19.6 | 0.557 |
| Tatoeba-test.eng-swa.eng.swa | 1.8 | 0.163 |
| Tatoeba-test.eng-toi.eng.toi | 8.3 | 0.231 |
| Tatoeba-test.eng-tso.eng.tso | 50.0 | 0.789 |
| Tatoeba-test.eng-umb.eng.umb | 7.8 | 0.342 |
| Tatoeba-test.eng-wol.eng.wol | 6.7 | 0.143 |
| Tatoeba-test.eng-xho.eng.xho | 26.4 | 0.620 |
| Tatoeba-test.eng-yor.eng.yor | 15.5 | 0.342 |
| Tatoeba-test.eng-zul.eng.zul | 35.9 | 0.750 |
### System Info:
- hf_name: eng-nic
- source_languages: eng
- target_languages: nic
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-nic/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'nic']
- src_constituents: {'eng'}
- tgt_constituents: {'bam_Latn', 'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi_Latn', 'umb'}
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.test.txt
- src_alpha3: eng
- tgt_alpha3: nic
- short_pair: en-nic
- chrF2_score: 0.42700000000000005
- bleu: 11.1
- brevity_penalty: 1.0
- ref_len: 10625.0
- src_name: English
- tgt_name: Niger-Kordofanian languages
- train_date: 2020-07-27
- src_alpha2: en
- tgt_alpha2: nic
- prefer_old: False
- long_pair: eng-nic
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
| null |
Non_BioNLP
|
### eng-nic
* source group: English
* target group: Niger-Kordofanian languages
* OPUS readme: [eng-nic](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-nic/README.md)
* model: transformer
* source language(s): eng
* target language(s): bam_Latn ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng-bam.eng.bam | 6.2 | 0.029 |
| Tatoeba-test.eng-ewe.eng.ewe | 4.5 | 0.258 |
| Tatoeba-test.eng-ful.eng.ful | 0.5 | 0.073 |
| Tatoeba-test.eng-ibo.eng.ibo | 3.9 | 0.267 |
| Tatoeba-test.eng-kin.eng.kin | 6.4 | 0.475 |
| Tatoeba-test.eng-lin.eng.lin | 1.2 | 0.308 |
| Tatoeba-test.eng-lug.eng.lug | 3.9 | 0.405 |
| Tatoeba-test.eng.multi | 11.1 | 0.427 |
| Tatoeba-test.eng-nya.eng.nya | 14.0 | 0.622 |
| Tatoeba-test.eng-run.eng.run | 13.6 | 0.477 |
| Tatoeba-test.eng-sag.eng.sag | 5.5 | 0.199 |
| Tatoeba-test.eng-sna.eng.sna | 19.6 | 0.557 |
| Tatoeba-test.eng-swa.eng.swa | 1.8 | 0.163 |
| Tatoeba-test.eng-toi.eng.toi | 8.3 | 0.231 |
| Tatoeba-test.eng-tso.eng.tso | 50.0 | 0.789 |
| Tatoeba-test.eng-umb.eng.umb | 7.8 | 0.342 |
| Tatoeba-test.eng-wol.eng.wol | 6.7 | 0.143 |
| Tatoeba-test.eng-xho.eng.xho | 26.4 | 0.620 |
| Tatoeba-test.eng-yor.eng.yor | 15.5 | 0.342 |
| Tatoeba-test.eng-zul.eng.zul | 35.9 | 0.750 |
### System Info:
- hf_name: eng-nic
- source_languages: eng
- target_languages: nic
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-nic/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'nic']
- src_constituents: {'eng'}
- tgt_constituents: {'bam_Latn', 'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi_Latn', 'umb'}
- src_multilingual: False
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nic/opus-2020-07-27.test.txt
- src_alpha3: eng
- tgt_alpha3: nic
- short_pair: en-nic
- chrF2_score: 0.42700000000000005
- bleu: 11.1
- brevity_penalty: 1.0
- ref_len: 10625.0
- src_name: English
- tgt_name: Niger-Kordofanian languages
- train_date: 2020-07-27
- src_alpha2: en
- tgt_alpha2: nic
- prefer_old: False
- long_pair: eng-nic
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41
|
{"language": ["en", "sn", "rw", "wo", "ig", "sg", "ee", "zu", "lg", "ts", "ln", "ny", "yo", "rn", "xh", "nic"], "license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,971 |
Mahan-M47/gpt2-lora-review-classification
|
Mahan-M47
| null |
[
"transformers",
"safetensors",
"Text Classification",
"en",
"arxiv:1910.09700",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-03-14T12:21:51Z |
2024-03-14T12:33:25+00:00
| 0 | 0 |
---
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- Text Classification
---
# GPT2-lora-review-classification
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
Training Dataset: https://www.kaggle.com/datasets/harisyafie/baldurs-gate-3-steam-reviews
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| null |
Non_BioNLP
|
# GPT2-lora-review-classification
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
Training Dataset: https://www.kaggle.com/datasets/harisyafie/baldurs-gate-3-steam-reviews
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
[More Information Needed]
|
{"language": ["en"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["Text Classification"]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,972 |
gaudi/opus-mt-fr-lus-ctranslate2
|
gaudi
|
translation
|
[
"transformers",
"marian",
"ctranslate2",
"translation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-07-22T15:58:29Z |
2024-10-19T04:34:38+00:00
| 7 | 0 |
---
license: apache-2.0
tags:
- ctranslate2
- translation
---
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): [Model Link](https://huggingface.co/Helsinki-NLP/opus-mt-fr-lus)
- This respository was based on the work of [CTranslate2](https://github.com/OpenNMT/CTranslate2).
- This repository was based on the work of [michaelfeil](https://huggingface.co/michaelfeil).
# What is CTranslate2?
[CTranslate2](https://opennmt.net/CTranslate2/) is a C++ and Python library for efficient inference with Transformer models.
CTranslate2 implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.
CTranslate2 is one of the most performant ways of hosting translation models at scale. Current supported models include:
- Encoder-decoder models: Transformer base/big, M2M-100, NLLB, BART, mBART, Pegasus, T5, Whisper
- Decoder-only models: GPT-2, GPT-J, GPT-NeoX, OPT, BLOOM, MPT, Llama, Mistral, Gemma, CodeGen, GPTBigCode, Falcon
- Encoder-only models: BERT, DistilBERT, XLM-RoBERTa
The project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration.
# CTranslate2 Benchmarks
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings. Tested against `newstest2014` (En -> De) dataset.
The benchmark reports the number of target tokens generated per second (higher is better). The results are aggregated over multiple runs. See the benchmark scripts for more details and reproduce these numbers.
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings.
## CPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 147.3 | 2332MB | 27.90 |
| Marian 1.11.0 (int16) | 330.2 | 5901MB | 27.65 |
| Marian 1.11.0 (int8) | 355.8 | 4763MB | 27.27 |
| CTranslate2 3.6.0 (int16) | 596.1 | 660MB | 27.53 |
| CTranslate2 3.6.0 (int8) | 696.1 | 516MB | 27.65 |
## GPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max GPU Memory Usage | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 1022.9 | 4097MB | 2109MB | 27.90 |
| Marian 1.11.0 (float16) | 3962.4 | 3239MB | 1976MB | 27.94 |
| CTranslate2 3.6.0 (float16) | 9296.7 | 909MB | 814MB | 27.9 |
| CTranslate2 3.6.0 (int8 + float16) | 8362.7 | 813MB | 766MB | 27.9 |
`Executed with 4 threads on a c5.2xlarge Amazon EC2 instance equipped with an Intel(R) Xeon(R) Platinum 8275CL CPU.`
**Source to benchmark information can be found [here](https://github.com/OpenNMT/CTranslate2).**<br />
**Original model BLEU scores can be found [here](https://huggingface.co/Helsinki-NLP/opus-mt-fr-lus).**
## Internal Benchmarks
Internal testing on our end showed **inference times reduced by 6x-10x** on average compared the vanilla checkpoints using the *transformers* library. A **slight reduction on BLEU scores (~5%)** was also identified in comparison to the vanilla checkpoints with a few exceptions. This is likely due to several factors, one being the quantization applied. Further testing is needed from our end to better assess the reduction in translation quality. The command used to compile the vanilla checkpoint into a CTranslate2 model can be found below. Modifying this command can yield differing balances between inferencing performance and translation quality.
# CTranslate2 Installation
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
### ct2-transformers-converter Command Used:
```bash
ct2-transformers-converter --model Helsinki-NLP/opus-mt-fr-lus --output_dir ./ctranslate2/opus-mt-fr-lus-ctranslate2 --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
# CTranslate2 Converted Checkpoint Information:
**Compatible With:**
- [ctranslate2](https://github.com/OpenNMT/CTranslate2)
- [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
**Compute Type:**
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
# Sample Code - ctranslate2
#### Clone the repository to the working directory or wherever you wish to store the model artifacts. ####
```bash
git clone https://huggingface.co/gaudi/opus-mt-fr-lus-ctranslate2
```
#### Take the python code below and update the 'model_dir' variable to the location of the cloned repository. ####
```python
from ctranslate2 import Translator
import transformers
model_dir = "./opus-mt-fr-lus-ctranslate2" # Path to model directory.
translator = Translator(
model_path=model_dir,
device="cuda", # cpu, cuda, or auto.
inter_threads=1, # Maximum number of parallel translations.
intra_threads=4, # Number of OpenMP threads per translator.
compute_type="int8_float16", # int8 for cpu or int8_float16 for cuda.
)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_dir)
source = tokenizer.convert_ids_to_tokens(tokenizer.encode("XXXXXX, XXX XX XXXXXX."))
results = translator.translate_batch([source])
target = results[0].hypotheses[0]
print(tokenizer.decode(tokenizer.convert_tokens_to_ids(target)))
```
# Sample Code - hf-hub-ctranslate2
**Derived From [michaelfeil](https://huggingface.co/michaelfeil):**
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "gaudi/opus-mt-fr-lus-ctranslate2"
model = TranslatorCT2fromHfHub(
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained(model_name)
)
outputs = model.generate(
text=["XXX XX XXX XXXXXXX XXXX?", "XX XX XXXX XX XXX!"],
)
print(outputs)
```
# License and other remarks:
License conditions are intended to be idential to [original huggingface repository](https://huggingface.co/Helsinki-NLP/opus-mt-fr-lus) by Helsinki-NLP.
| null |
Non_BioNLP
|
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): [Model Link](https://huggingface.co/Helsinki-NLP/opus-mt-fr-lus)
- This respository was based on the work of [CTranslate2](https://github.com/OpenNMT/CTranslate2).
- This repository was based on the work of [michaelfeil](https://huggingface.co/michaelfeil).
# What is CTranslate2?
[CTranslate2](https://opennmt.net/CTranslate2/) is a C++ and Python library for efficient inference with Transformer models.
CTranslate2 implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.
CTranslate2 is one of the most performant ways of hosting translation models at scale. Current supported models include:
- Encoder-decoder models: Transformer base/big, M2M-100, NLLB, BART, mBART, Pegasus, T5, Whisper
- Decoder-only models: GPT-2, GPT-J, GPT-NeoX, OPT, BLOOM, MPT, Llama, Mistral, Gemma, CodeGen, GPTBigCode, Falcon
- Encoder-only models: BERT, DistilBERT, XLM-RoBERTa
The project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration.
# CTranslate2 Benchmarks
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings. Tested against `newstest2014` (En -> De) dataset.
The benchmark reports the number of target tokens generated per second (higher is better). The results are aggregated over multiple runs. See the benchmark scripts for more details and reproduce these numbers.
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings.
## CPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 147.3 | 2332MB | 27.90 |
| Marian 1.11.0 (int16) | 330.2 | 5901MB | 27.65 |
| Marian 1.11.0 (int8) | 355.8 | 4763MB | 27.27 |
| CTranslate2 3.6.0 (int16) | 596.1 | 660MB | 27.53 |
| CTranslate2 3.6.0 (int8) | 696.1 | 516MB | 27.65 |
## GPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max GPU Memory Usage | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 1022.9 | 4097MB | 2109MB | 27.90 |
| Marian 1.11.0 (float16) | 3962.4 | 3239MB | 1976MB | 27.94 |
| CTranslate2 3.6.0 (float16) | 9296.7 | 909MB | 814MB | 27.9 |
| CTranslate2 3.6.0 (int8 + float16) | 8362.7 | 813MB | 766MB | 27.9 |
`Executed with 4 threads on a c5.2xlarge Amazon EC2 instance equipped with an Intel(R) Xeon(R) Platinum 8275CL CPU.`
**Source to benchmark information can be found [here](https://github.com/OpenNMT/CTranslate2).**<br />
**Original model BLEU scores can be found [here](https://huggingface.co/Helsinki-NLP/opus-mt-fr-lus).**
## Internal Benchmarks
Internal testing on our end showed **inference times reduced by 6x-10x** on average compared the vanilla checkpoints using the *transformers* library. A **slight reduction on BLEU scores (~5%)** was also identified in comparison to the vanilla checkpoints with a few exceptions. This is likely due to several factors, one being the quantization applied. Further testing is needed from our end to better assess the reduction in translation quality. The command used to compile the vanilla checkpoint into a CTranslate2 model can be found below. Modifying this command can yield differing balances between inferencing performance and translation quality.
# CTranslate2 Installation
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
### ct2-transformers-converter Command Used:
```bash
ct2-transformers-converter --model Helsinki-NLP/opus-mt-fr-lus --output_dir ./ctranslate2/opus-mt-fr-lus-ctranslate2 --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
# CTranslate2 Converted Checkpoint Information:
**Compatible With:**
- [ctranslate2](https://github.com/OpenNMT/CTranslate2)
- [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
**Compute Type:**
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
# Sample Code - ctranslate2
#### Clone the repository to the working directory or wherever you wish to store the model artifacts. ####
```bash
git clone https://huggingface.co/gaudi/opus-mt-fr-lus-ctranslate2
```
#### Take the python code below and update the 'model_dir' variable to the location of the cloned repository. ####
```python
from ctranslate2 import Translator
import transformers
model_dir = "./opus-mt-fr-lus-ctranslate2" # Path to model directory.
translator = Translator(
model_path=model_dir,
device="cuda", # cpu, cuda, or auto.
inter_threads=1, # Maximum number of parallel translations.
intra_threads=4, # Number of OpenMP threads per translator.
compute_type="int8_float16", # int8 for cpu or int8_float16 for cuda.
)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_dir)
source = tokenizer.convert_ids_to_tokens(tokenizer.encode("XXXXXX, XXX XX XXXXXX."))
results = translator.translate_batch([source])
target = results[0].hypotheses[0]
print(tokenizer.decode(tokenizer.convert_tokens_to_ids(target)))
```
# Sample Code - hf-hub-ctranslate2
**Derived From [michaelfeil](https://huggingface.co/michaelfeil):**
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "gaudi/opus-mt-fr-lus-ctranslate2"
model = TranslatorCT2fromHfHub(
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained(model_name)
)
outputs = model.generate(
text=["XXX XX XXX XXXXXXX XXXX?", "XX XX XXXX XX XXX!"],
)
print(outputs)
```
# License and other remarks:
License conditions are intended to be idential to [original huggingface repository](https://huggingface.co/Helsinki-NLP/opus-mt-fr-lus) by Helsinki-NLP.
|
{"license": "apache-2.0", "tags": ["ctranslate2", "translation"]}
|
task
|
[
"TRANSLATION"
] | 41,973 |
morenolq/bart-it-WITS
|
morenolq
|
text2text-generation
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"it",
"dataset:Silvia/WITS",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-12-27T16:16:49Z |
2023-06-15T13:50:21+00:00
| 21 | 0 |
---
datasets:
- Silvia/WITS
language: it
license: mit
tags:
- bart
- pytorch
pipeline:
- summarization
---
# BART-IT - WITS
BART-IT is a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a [large corpus of Italian text](https://huggingface.co/datasets/gsarti/clean_mc4_it), and can be fine-tuned on a variety of tasks.
## Model description
The model is a `base-`sized BART model, with a vocabulary size of 52,000 tokens. It has 140M parameters and can be used for any task that requires a sequence-to-sequence model. It is trained from scratch on a large corpus of Italian text, and can be fine-tuned on a variety of tasks.
## Pre-training
The code used to pre-train BART-IT together with additional information on model parameters can be found [here](https://github.com/MorenoLaQuatra/bart-it).
## Fine-tuning
The model has been fine-tuned for the abstractive summarization task on 3 different Italian datasets:
- [FanPage](https://huggingface.co/datasets/ARTeLab/fanpage) - finetuned model [here](https://huggingface.co/MorenoLaQuatra/bart-it-fanpage)
- [IlPost](https://huggingface.co/datasets/ARTeLab/ilpost) - finetuned model [here](https://huggingface.co/morenolq/bart-it-ilpost)
- **This model** [WITS](https://huggingface.co/datasets/Silvia/WITS) - finetuned model [here](https://huggingface.co/morenolq/bart-it-WITS)
## Usage
In order to use the model, you can use the following code:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-it-WITS")
model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-it-WITS")
input_ids = tokenizer.encode("Il modello BART-IT è stato pre-addestrato su un corpus di testo italiano", return_tensors="pt")
outputs = model.generate(input_ids, max_length=40, num_beams=4, early_stopping=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
# Citation
If you find this model useful for your research, please cite the following paper:
```bibtex
@Article{BARTIT,
AUTHOR = {La Quatra, Moreno and Cagliero, Luca},
TITLE = {BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization},
JOURNAL = {Future Internet},
VOLUME = {15},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {15},
URL = {https://www.mdpi.com/1999-5903/15/1/15},
ISSN = {1999-5903},
DOI = {10.3390/fi15010015}
}
```
| null |
Non_BioNLP
|
# BART-IT - WITS
BART-IT is a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a [large corpus of Italian text](https://huggingface.co/datasets/gsarti/clean_mc4_it), and can be fine-tuned on a variety of tasks.
## Model description
The model is a `base-`sized BART model, with a vocabulary size of 52,000 tokens. It has 140M parameters and can be used for any task that requires a sequence-to-sequence model. It is trained from scratch on a large corpus of Italian text, and can be fine-tuned on a variety of tasks.
## Pre-training
The code used to pre-train BART-IT together with additional information on model parameters can be found [here](https://github.com/MorenoLaQuatra/bart-it).
## Fine-tuning
The model has been fine-tuned for the abstractive summarization task on 3 different Italian datasets:
- [FanPage](https://huggingface.co/datasets/ARTeLab/fanpage) - finetuned model [here](https://huggingface.co/MorenoLaQuatra/bart-it-fanpage)
- [IlPost](https://huggingface.co/datasets/ARTeLab/ilpost) - finetuned model [here](https://huggingface.co/morenolq/bart-it-ilpost)
- **This model** [WITS](https://huggingface.co/datasets/Silvia/WITS) - finetuned model [here](https://huggingface.co/morenolq/bart-it-WITS)
## Usage
In order to use the model, you can use the following code:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-it-WITS")
model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-it-WITS")
input_ids = tokenizer.encode("Il modello BART-IT è stato pre-addestrato su un corpus di testo italiano", return_tensors="pt")
outputs = model.generate(input_ids, max_length=40, num_beams=4, early_stopping=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
# Citation
If you find this model useful for your research, please cite the following paper:
```bibtex
@Article{BARTIT,
AUTHOR = {La Quatra, Moreno and Cagliero, Luca},
TITLE = {BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization},
JOURNAL = {Future Internet},
VOLUME = {15},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {15},
URL = {https://www.mdpi.com/1999-5903/15/1/15},
ISSN = {1999-5903},
DOI = {10.3390/fi15010015}
}
```
|
{"datasets": ["Silvia/WITS"], "language": "it", "license": "mit", "tags": ["bart", "pytorch"], "pipeline": ["summarization"]}
|
task
|
[
"SUMMARIZATION"
] | 41,974 |
sophiefy/Qwen-7B-kanbun
|
sophiefy
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-7B-Chat-Int4",
"base_model:adapter:Qwen/Qwen-7B-Chat-Int4",
"region:us"
] | 2024-05-22T09:25:41Z |
2024-05-22T09:33:48+00:00
| 7 | 3 |
---
base_model: Qwen/Qwen-7B-Chat-Int4
library_name: peft
---
# Introduction
Qwen-7B finetuned on a parallel corpus for translation between Kanbun (漢文) and its Kakikudashibun (書き下し文).
# Examples
```python
response, history = model.chat(tokenizer, "冀靈體之復形，御輕舟而上溯。", history=None)
print(response)
```
```
冀して靈体の復形、軽舟に御して上溯せんとし
```
```python
response, history = model.chat(tokenizer, "鳥欲高飛先振翅，人求上進則讀書。", history=None)
print(response)
```
```
鳥の高飛するを欲すれば先づ翼を振ふ、人の上の前に進むを求めて則ち書を読む。
```
```python
response, history = model.chat(tokenizer, "浮長川而忘返，思綿綿而增慕。", history=None)
print(response)
```
```
長川に浮かして返らざるを、締結の绵綿にして慕うを増す。
```
```python
response, history = model.chat(tokenizer, "夜耿耿而不寐，沾繁霜而至曙。", history=None)
print(response)
```
```
夜は耿耿として寐りず、繁霜に沾れて曙を至る。
```
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1
| null |
Non_BioNLP
|
# Introduction
Qwen-7B finetuned on a parallel corpus for translation between Kanbun (漢文) and its Kakikudashibun (書き下し文).
# Examples
```python
response, history = model.chat(tokenizer, "冀靈體之復形，御輕舟而上溯。", history=None)
print(response)
```
```
冀して靈体の復形、軽舟に御して上溯せんとし
```
```python
response, history = model.chat(tokenizer, "鳥欲高飛先振翅，人求上進則讀書。", history=None)
print(response)
```
```
鳥の高飛するを欲すれば先づ翼を振ふ、人の上の前に進むを求めて則ち書を読む。
```
```python
response, history = model.chat(tokenizer, "浮長川而忘返，思綿綿而增慕。", history=None)
print(response)
```
```
長川に浮かして返らざるを、締結の绵綿にして慕うを増す。
```
```python
response, history = model.chat(tokenizer, "夜耿耿而不寐，沾繁霜而至曙。", history=None)
print(response)
```
```
夜は耿耿として寐りず、繁霜に沾れて曙を至る。
```
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1
|
{"base_model": "Qwen/Qwen-7B-Chat-Int4", "library_name": "peft"}
|
task
|
[
"TRANSLATION"
] | 41,975 |
pinzhenchen/sft-lora-zh-bloom-1b1
|
pinzhenchen
| null |
[
"generation",
"question answering",
"instruction tuning",
"zh",
"arxiv:2309.08958",
"license:cc-by-nc-4.0",
"region:us"
] | 2024-03-05T23:46:31Z |
2024-03-05T23:46:34+00:00
| 0 | 0 |
---
language:
- zh
license: cc-by-nc-4.0
tags:
- generation
- question answering
- instruction tuning
---
### Model Description
This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable.
* [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main)
* [Paper](https://arxiv.org/abs/2309.08958)
#### Instruction tuning details
* Base model: [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1)
* Instruction tuning language: Chinese
* Training method: LoRA.
* LoRA details: rank=8, alpha=16, target modules={key, query, value}.
* Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs.
* Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data).
#### Usage
The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries.
Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions.
#### Citation
```
@inproceedings{chen-etal-2024-monolingual,
title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}",
author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield",
year="2024",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
}
```
| null |
Non_BioNLP
|
### Model Description
This HF repository contains base LLMs instruction tuned (SFT) with LoRA and then used to study whether monolingual or multilingual instruction tuning is more favourable.
* [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main)
* [Paper](https://arxiv.org/abs/2309.08958)
#### Instruction tuning details
* Base model: [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1)
* Instruction tuning language: Chinese
* Training method: LoRA.
* LoRA details: rank=8, alpha=16, target modules={key, query, value}.
* Best checkpoint: best cross-entropy on a validation set, trained for 5 epochs.
* Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data).
#### Usage
The model checkpoint should be loaded with the base model together using `transformers` and `peft` libraries.
Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/loraft) for inference and training instructions.
#### Citation
```
@inproceedings{chen-etal-2024-monolingual,
title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}",
author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield",
year="2024",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
}
```
|
{"language": ["zh"], "license": "cc-by-nc-4.0", "tags": ["generation", "question answering", "instruction tuning"]}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,976 |
Trelis/falcon-40B-chat-SFT-adapters
|
Trelis
| null |
[
"safetensors",
"falcon",
"commercial use",
"en",
"dataset:tiiuae/falcon-refinedweb",
"dataset:Trelis/openassistant-falcon",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"region:us"
] | 2023-11-10T17:02:44Z |
2023-11-10T17:53:34+00:00
| 0 | 0 |
---
datasets:
- tiiuae/falcon-refinedweb
- Trelis/openassistant-falcon
language:
- en
tags:
- falcon
- commercial use
inference: false
extra_gated_prompt: Access to this repo requires the purchase of a license (see link
on model card below)
extra_gated_fields:
Name: text
Affiliation: text
Email: text
I have purchased access (access will be granted once your payment clears): checkbox
I agree to the terms of the license described on the dataset card: checkbox
---
# ✨ Falcon-40B-chat-SFT
This is a chat fine-tuned version of [Falcon-7b](https://huggingface.co/Trelis/falcon-7B-chat-SFT) and [Falcon-40b](https://huggingface.co/Trelis/falcon-40B-chat-SFT) trained using OpenAssistant conversations.
- Purchase access to the 7b model [here](https://buy.stripe.com/5kA28U3lH3LB3ks9Be)
- Purchase access to the 40b model [here](https://buy.stripe.com/14k6pa09v81R7AI8x9)
Notably:
- The data used is Apache 2 licensed and not generated using AI, thereby allowing this chat model to be used commercially, which is particularly useful for data preparation and generation for training other models.
- The purchase of access to this model grants the user permission to use the model commercially for inference or fine-tuning and inference.
## Prompt format:
```
# Falcon style
B_INST, E_INST = "\nHuman:", "\nAssistant:"
prompt = f"{B_INST} {user_prompt.strip()}{E_INST}"
```
THE ORIGINAL MODEL CARD FOLLOWS BELOW.
# 🚀 Falcon-7B
**Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon* 😊.
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B?
* **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B.
# Model Card for Falcon-7B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- **License:** Apache 2.0.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl |
| Books | 7% | 110B | |
| Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews |
| Code | 3% | 45B | |
| RefinedWeb-French | 3% | 45B | massive web crawl |
| Technical | 2% | 30B | arXiv, PubMed, USPTO, etc. |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 2304 | 30B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early March 2023 and took about two weeks.
## Evaluation
*Paper coming soon*.
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B is made available under the Apache 2.0 license.
## Contact
[email protected]
| null |
Non_BioNLP
|
# ✨ Falcon-40B-chat-SFT
This is a chat fine-tuned version of [Falcon-7b](https://huggingface.co/Trelis/falcon-7B-chat-SFT) and [Falcon-40b](https://huggingface.co/Trelis/falcon-40B-chat-SFT) trained using OpenAssistant conversations.
- Purchase access to the 7b model [here](https://buy.stripe.com/5kA28U3lH3LB3ks9Be)
- Purchase access to the 40b model [here](https://buy.stripe.com/14k6pa09v81R7AI8x9)
Notably:
- The data used is Apache 2 licensed and not generated using AI, thereby allowing this chat model to be used commercially, which is particularly useful for data preparation and generation for training other models.
- The purchase of access to this model grants the user permission to use the model commercially for inference or fine-tuning and inference.
## Prompt format:
```
# Falcon style
B_INST, E_INST = "\nHuman:", "\nAssistant:"
prompt = f"{B_INST} {user_prompt.strip()}{E_INST}"
```
THE ORIGINAL MODEL CARD FOLLOWS BELOW.
# 🚀 Falcon-7B
**Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon* 😊.
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B?
* **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B.
# Model Card for Falcon-7B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- **License:** Apache 2.0.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl |
| Books | 7% | 110B | |
| Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews |
| Code | 3% | 45B | |
| RefinedWeb-French | 3% | 45B | massive web crawl |
| Technical | 2% | 30B | arXiv, PubMed, USPTO, etc. |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 2304 | 30B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early March 2023 and took about two weeks.
## Evaluation
*Paper coming soon*.
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B is made available under the Apache 2.0 license.
## Contact
[email protected]
|
{"datasets": ["tiiuae/falcon-refinedweb", "Trelis/openassistant-falcon"], "language": ["en"], "tags": ["falcon", "commercial use"], "inference": false, "extra_gated_prompt": "Access to this repo requires the purchase of a license (see link on model card below)", "extra_gated_fields": {"Name": "text", "Affiliation": "text", "Email": "text", "I have purchased access (access will be granted once your payment clears)": "checkbox", "I agree to the terms of the license described on the dataset card": "checkbox"}}
|
task
|
[
"SUMMARIZATION"
] | 41,977 |
Tritkoman/EnglishtoAncientGreekV6
|
Tritkoman
|
translation
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"translation",
"unk",
"dataset:Tritkoman/autotrain-data-abagaga",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-17T15:35:24Z |
2023-02-17T15:43:06+00:00
| 10 | 0 |
---
datasets:
- Tritkoman/autotrain-data-abagaga
language:
- unk
- unk
tags:
- autotrain
- translation
co2_eq_emissions:
emissions: 10.516087938863189
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 3551595787
- CO2 Emissions (in grams): 10.5161
## Validation Metrics
- Loss: 1.732
- SacreBLEU: 12.665
- Gen len: 19.955
| null |
Non_BioNLP
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 3551595787
- CO2 Emissions (in grams): 10.5161
## Validation Metrics
- Loss: 1.732
- SacreBLEU: 12.665
- Gen len: 19.955
|
{"datasets": ["Tritkoman/autotrain-data-abagaga"], "language": ["unk", "unk"], "tags": ["autotrain", "translation"], "co2_eq_emissions": {"emissions": 10.516087938863189}}
|
task
|
[
"TRANSLATION"
] | 41,978 |
google/t5-efficient-small-el16
|
google
|
text2text-generation
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"deep-narrow",
"en",
"dataset:c4",
"arxiv:2109.10686",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:05Z |
2023-01-24T16:48:51+00:00
| 151 | 2 |
---
datasets:
- c4
language:
- en
license: apache-2.0
tags:
- deep-narrow
inference: false
---
# T5-Efficient-SMALL-EL16 (Deep-Narrow version)
T5-Efficient-SMALL-EL16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-small-el16** - is of model type **Small** with the following variations:
- **el** is **16**
It has **92.0** million parameters and thus requires *ca.* **367.99 MB** of memory in full precision (*fp32*)
or **183.99 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.
| null |
Non_BioNLP
|
# T5-Efficient-SMALL-EL16 (Deep-Narrow version)
T5-Efficient-SMALL-EL16 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5).
It is a *pretrained-only* checkpoint and was released with the
paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)**
by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*.
In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures
of similar parameter count.
To quote the paper:
> We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased
> before considering any other forms of uniform scaling across other dimensions. This is largely due to
> how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a
> tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise,
> a tall base model might also generally more efficient compared to a large model. We generally find
> that, regardless of size, even if absolute performance might increase as we continue to stack layers,
> the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36
> layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e.,
> params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params,
> FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to
> consider.
To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially.
A sequence of word embeddings is therefore processed sequentially by each transformer block.
## Details model architecture
This model checkpoint - **t5-efficient-small-el16** - is of model type **Small** with the following variations:
- **el** is **16**
It has **92.0** million parameters and thus requires *ca.* **367.99 MB** of memory in full precision (*fp32*)
or **183.99 MB** of memory in half precision (*fp16* or *bf16*).
A summary of the *original* T5 model architectures can be seen here:
| Model | nl (el/dl) | ff | dm | kv | nh | #Params|
| ----| ---- | ---- | ---- | ---- | ---- | ----|
| Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M|
| Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M|
| Small | 6/6 | 2048 | 512 | 32 | 8 | 60M|
| Base | 12/12 | 3072 | 768 | 64 | 12 | 220M|
| Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M|
| Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B|
| XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B|
whereas the following abbreviations are used:
| Abbreviation | Definition |
| ----| ---- |
| nl | Number of transformer blocks (depth) |
| dm | Dimension of embedding vector (output vector of transformers block) |
| kv | Dimension of key/value projection matrix |
| nh | Number of attention heads |
| ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
| el | Number of transformer blocks in the encoder (encoder depth) |
| dl | Number of transformer blocks in the decoder (decoder depth) |
| sh | Signifies that attention heads are shared |
| skv | Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*.
## Pre-Training
The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using
the span-based masked language modeling (MLM) objective.
## Fine-Tuning
**Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage.
The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow on of the following examples on how to fine-tune the model:
*PyTorch*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization)
- [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*Tensorflow*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
*JAX/Flax*:
- [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization)
- [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model.
## Downstream Performance
TODO: Add table if available
## Computational Complexity
TODO: Add table if available
## More information
We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint.
As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv*
model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.
|
{"datasets": ["c4"], "language": ["en"], "license": "apache-2.0", "tags": ["deep-narrow"], "inference": false}
|
task
|
[
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | 41,979 |
grammarly/medit-xxl
|
grammarly
|
text-generation
|
[
"transformers",
"text-generation",
"en",
"de",
"es",
"ar",
"ja",
"ko",
"zh",
"dataset:wi_locness",
"dataset:matejklemen/falko_merlin",
"dataset:paws",
"dataset:paws-x",
"dataset:facebook/asset",
"arxiv:2402.16472",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | 2023-12-19T16:52:57Z |
2024-06-28T21:40:20+00:00
| 0 | 10 |
---
datasets:
- wi_locness
- matejklemen/falko_merlin
- paws
- paws-x
- facebook/asset
language:
- en
- de
- es
- ar
- ja
- ko
- zh
library_name: transformers
license: cc-by-nc-sa-4.0
metrics:
- bleu
- rouge
- sari
- accuracy
pipeline_tag: text-generation
---
# Model Card for mEdIT-xxl
The `medit-xxl` model was obtained by fine-tuning the `MBZUAI/bactrian-x-llama-13b-lora` model on the mEdIT dataset.
**Paper:** mEdIT: Multilingual Text Editing via Instruction Tuning
**Authors:** Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar
## Model Details
### Model Description
- **Language(s) (NLP)**: Arabic, Chinese, English, German, Japanese, Korean, Spanish
- **Finetuned from model:** `MBZUAI/bactrian-x-llama-13b-lora`
### Model Sources
- **Repository:** https://github.com/vipulraheja/medit
- **Paper:** https://arxiv.org/abs/2402.16472v1
## How to use
Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
Specifically, our models support both multi-lingual and cross-lingual text revision. Note that the input and output texts are always in the same language. The monolingual
vs. cross-lingual setting is determined by comparing the language of the edit instruction in relation to the language of the input text.
### Instruction format
Adherence to the following instruction format is essential; failure to do so may result in the model producing less-than-ideal results.
```
instruction_tokens = [
"Instruction",
"Anweisung",
...
]
input_tokens = [
"Input",
"Aporte",
...
]
output_tokens = [
"Output",
"Produzione",
...
]
task_descriptions = [
"Fix grammatical errors in this sentence", # <-- GEC task
"Umschreiben Sie den Satz", # <-- Paraphrasing
...
]
```
**The entire list of possible instructions, input/output tokens, and task descriptions can be found in the Appendix of our paper.**
```
prompt_template = """### <instruction_token>:\n<task_description>\n### <input_token>:\n<input>\n### <output_token>:\n\n"""
```
Note that the tokens and the task description need not be in the language of the input (in the case of cross-lingual revision).
### Run the model
**Make sure you have the following libraries installed:**
```
- peft
- protobuf
- sentencepiece
- tokenizers
- torch
- transformers
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "grammarly/medit-xxl"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# English GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nI has small cat ,\n### 出力:\n\n'
inputs = tokenizer(prompt, return_tensors='pt')
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# --> I have a small cat ,
# German GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nIch haben eines kleines Katze ,\n### 出力:\n\n'
# ...
# --> Ich habe eine kleine Katze ,
```
#### Software
https://github.com/vipulraheja/medit
## Citation
**BibTeX:**
```
@article{raheja2023medit,
title={mEdIT: mEdIT: Multilingual Text Editing via Instruction Tuning},
author={Vipul Raheja and Dimitris Alikaniotis and Vivek Kulkarni and Bashar Alhafni and Dhruv Kumar},
year={2024},
eprint={2402.16472v1},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**APA:**
Raheja, V., Alikaniotis, D., Kulkarni, V., Alhafni, B., & Kumar, D. (2024). MEdIT: Multilingual Text Editing via Instruction Tuning. ArXiv. /abs/2402.16472
| null |
Non_BioNLP
|
# Model Card for mEdIT-xxl
The `medit-xxl` model was obtained by fine-tuning the `MBZUAI/bactrian-x-llama-13b-lora` model on the mEdIT dataset.
**Paper:** mEdIT: Multilingual Text Editing via Instruction Tuning
**Authors:** Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar
## Model Details
### Model Description
- **Language(s) (NLP)**: Arabic, Chinese, English, German, Japanese, Korean, Spanish
- **Finetuned from model:** `MBZUAI/bactrian-x-llama-13b-lora`
### Model Sources
- **Repository:** https://github.com/vipulraheja/medit
- **Paper:** https://arxiv.org/abs/2402.16472v1
## How to use
Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
Specifically, our models support both multi-lingual and cross-lingual text revision. Note that the input and output texts are always in the same language. The monolingual
vs. cross-lingual setting is determined by comparing the language of the edit instruction in relation to the language of the input text.
### Instruction format
Adherence to the following instruction format is essential; failure to do so may result in the model producing less-than-ideal results.
```
instruction_tokens = [
"Instruction",
"Anweisung",
...
]
input_tokens = [
"Input",
"Aporte",
...
]
output_tokens = [
"Output",
"Produzione",
...
]
task_descriptions = [
"Fix grammatical errors in this sentence", # <-- GEC task
"Umschreiben Sie den Satz", # <-- Paraphrasing
...
]
```
**The entire list of possible instructions, input/output tokens, and task descriptions can be found in the Appendix of our paper.**
```
prompt_template = """### <instruction_token>:\n<task_description>\n### <input_token>:\n<input>\n### <output_token>:\n\n"""
```
Note that the tokens and the task description need not be in the language of the input (in the case of cross-lingual revision).
### Run the model
**Make sure you have the following libraries installed:**
```
- peft
- protobuf
- sentencepiece
- tokenizers
- torch
- transformers
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "grammarly/medit-xxl"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# English GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nI has small cat ,\n### 出力:\n\n'
inputs = tokenizer(prompt, return_tensors='pt')
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# --> I have a small cat ,
# German GEC using Japanese instructions
prompt = '### 命令:\n文章を文法的にする\n### 入力:\nIch haben eines kleines Katze ,\n### 出力:\n\n'
# ...
# --> Ich habe eine kleine Katze ,
```
#### Software
https://github.com/vipulraheja/medit
## Citation
**BibTeX:**
```
@article{raheja2023medit,
title={mEdIT: mEdIT: Multilingual Text Editing via Instruction Tuning},
author={Vipul Raheja and Dimitris Alikaniotis and Vivek Kulkarni and Bashar Alhafni and Dhruv Kumar},
year={2024},
eprint={2402.16472v1},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**APA:**
Raheja, V., Alikaniotis, D., Kulkarni, V., Alhafni, B., & Kumar, D. (2024). MEdIT: Multilingual Text Editing via Instruction Tuning. ArXiv. /abs/2402.16472
|
{"datasets": ["wi_locness", "matejklemen/falko_merlin", "paws", "paws-x", "facebook/asset"], "language": ["en", "de", "es", "ar", "ja", "ko", "zh"], "library_name": "transformers", "license": "cc-by-nc-sa-4.0", "metrics": ["bleu", "rouge", "sari", "accuracy"], "pipeline_tag": "text-generation"}
|
task
|
[
"PARAPHRASING"
] | 41,980 |
gaudi/opus-mt-sal-en-ctranslate2
|
gaudi
|
translation
|
[
"transformers",
"marian",
"ctranslate2",
"translation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-07-17T00:15:33Z |
2024-10-18T22:41:23+00:00
| 4 | 0 |
---
license: apache-2.0
tags:
- ctranslate2
- translation
---
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): [Model Link](https://huggingface.co/Helsinki-NLP/opus-mt-sal-en)
- This respository was based on the work of [CTranslate2](https://github.com/OpenNMT/CTranslate2).
- This repository was based on the work of [michaelfeil](https://huggingface.co/michaelfeil).
# What is CTranslate2?
[CTranslate2](https://opennmt.net/CTranslate2/) is a C++ and Python library for efficient inference with Transformer models.
CTranslate2 implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.
CTranslate2 is one of the most performant ways of hosting translation models at scale. Current supported models include:
- Encoder-decoder models: Transformer base/big, M2M-100, NLLB, BART, mBART, Pegasus, T5, Whisper
- Decoder-only models: GPT-2, GPT-J, GPT-NeoX, OPT, BLOOM, MPT, Llama, Mistral, Gemma, CodeGen, GPTBigCode, Falcon
- Encoder-only models: BERT, DistilBERT, XLM-RoBERTa
The project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration.
# CTranslate2 Benchmarks
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings. Tested against `newstest2014` (En -> De) dataset.
The benchmark reports the number of target tokens generated per second (higher is better). The results are aggregated over multiple runs. See the benchmark scripts for more details and reproduce these numbers.
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings.
## CPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 147.3 | 2332MB | 27.90 |
| Marian 1.11.0 (int16) | 330.2 | 5901MB | 27.65 |
| Marian 1.11.0 (int8) | 355.8 | 4763MB | 27.27 |
| CTranslate2 3.6.0 (int16) | 596.1 | 660MB | 27.53 |
| CTranslate2 3.6.0 (int8) | 696.1 | 516MB | 27.65 |
## GPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max GPU Memory Usage | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 1022.9 | 4097MB | 2109MB | 27.90 |
| Marian 1.11.0 (float16) | 3962.4 | 3239MB | 1976MB | 27.94 |
| CTranslate2 3.6.0 (float16) | 9296.7 | 909MB | 814MB | 27.9 |
| CTranslate2 3.6.0 (int8 + float16) | 8362.7 | 813MB | 766MB | 27.9 |
`Executed with 4 threads on a c5.2xlarge Amazon EC2 instance equipped with an Intel(R) Xeon(R) Platinum 8275CL CPU.`
**Source to benchmark information can be found [here](https://github.com/OpenNMT/CTranslate2).**<br />
**Original model BLEU scores can be found [here](https://huggingface.co/Helsinki-NLP/opus-mt-sal-en).**
## Internal Benchmarks
Internal testing on our end showed **inference times reduced by 6x-10x** on average compared the vanilla checkpoints using the *transformers* library. A **slight reduction on BLEU scores (~5%)** was also identified in comparison to the vanilla checkpoints with a few exceptions. This is likely due to several factors, one being the quantization applied. Further testing is needed from our end to better assess the reduction in translation quality. The command used to compile the vanilla checkpoint into a CTranslate2 model can be found below. Modifying this command can yield differing balances between inferencing performance and translation quality.
# CTranslate2 Installation
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
### ct2-transformers-converter Command Used:
```bash
ct2-transformers-converter --model Helsinki-NLP/opus-mt-sal-en --output_dir ./ctranslate2/opus-mt-sal-en-ctranslate2 --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
# CTranslate2 Converted Checkpoint Information:
**Compatible With:**
- [ctranslate2](https://github.com/OpenNMT/CTranslate2)
- [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
**Compute Type:**
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
# Sample Code - ctranslate2
#### Clone the repository to the working directory or wherever you wish to store the model artifacts. ####
```bash
git clone https://huggingface.co/gaudi/opus-mt-sal-en-ctranslate2
```
#### Take the python code below and update the 'model_dir' variable to the location of the cloned repository. ####
```python
from ctranslate2 import Translator
import transformers
model_dir = "./opus-mt-sal-en-ctranslate2" # Path to model directory.
translator = Translator(
model_path=model_dir,
device="cuda", # cpu, cuda, or auto.
inter_threads=1, # Maximum number of parallel translations.
intra_threads=4, # Number of OpenMP threads per translator.
compute_type="int8_float16", # int8 for cpu or int8_float16 for cuda.
)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_dir)
source = tokenizer.convert_ids_to_tokens(tokenizer.encode("XXXXXX, XXX XX XXXXXX."))
results = translator.translate_batch([source])
target = results[0].hypotheses[0]
print(tokenizer.decode(tokenizer.convert_tokens_to_ids(target)))
```
# Sample Code - hf-hub-ctranslate2
**Derived From [michaelfeil](https://huggingface.co/michaelfeil):**
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "gaudi/opus-mt-sal-en-ctranslate2"
model = TranslatorCT2fromHfHub(
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained(model_name)
)
outputs = model.generate(
text=["XXX XX XXX XXXXXXX XXXX?", "XX XX XXXX XX XXX!"],
)
print(outputs)
```
# License and other remarks:
License conditions are intended to be idential to [original huggingface repository](https://huggingface.co/Helsinki-NLP/opus-mt-sal-en) by Helsinki-NLP.
| null |
Non_BioNLP
|
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): [Model Link](https://huggingface.co/Helsinki-NLP/opus-mt-sal-en)
- This respository was based on the work of [CTranslate2](https://github.com/OpenNMT/CTranslate2).
- This repository was based on the work of [michaelfeil](https://huggingface.co/michaelfeil).
# What is CTranslate2?
[CTranslate2](https://opennmt.net/CTranslate2/) is a C++ and Python library for efficient inference with Transformer models.
CTranslate2 implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.
CTranslate2 is one of the most performant ways of hosting translation models at scale. Current supported models include:
- Encoder-decoder models: Transformer base/big, M2M-100, NLLB, BART, mBART, Pegasus, T5, Whisper
- Decoder-only models: GPT-2, GPT-J, GPT-NeoX, OPT, BLOOM, MPT, Llama, Mistral, Gemma, CodeGen, GPTBigCode, Falcon
- Encoder-only models: BERT, DistilBERT, XLM-RoBERTa
The project is production-oriented and comes with backward compatibility guarantees, but it also includes experimental features related to model compression and inference acceleration.
# CTranslate2 Benchmarks
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings. Tested against `newstest2014` (En -> De) dataset.
The benchmark reports the number of target tokens generated per second (higher is better). The results are aggregated over multiple runs. See the benchmark scripts for more details and reproduce these numbers.
Please note that the results presented below are only valid for the configuration used during this benchmark: absolute and relative performance may change with different settings.
## CPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 147.3 | 2332MB | 27.90 |
| Marian 1.11.0 (int16) | 330.2 | 5901MB | 27.65 |
| Marian 1.11.0 (int8) | 355.8 | 4763MB | 27.27 |
| CTranslate2 3.6.0 (int16) | 596.1 | 660MB | 27.53 |
| CTranslate2 3.6.0 (int8) | 696.1 | 516MB | 27.65 |
## GPU Benchmarks for Generic Opus-MT Models
| Library | Tokens per Second | Max GPU Memory Usage | Max Memory Usage | BLEU |
| :----: | :----: | :----: | :----: | :----: |
| Transformers 4.26.1 (with PyTorch 1.13.1) | 1022.9 | 4097MB | 2109MB | 27.90 |
| Marian 1.11.0 (float16) | 3962.4 | 3239MB | 1976MB | 27.94 |
| CTranslate2 3.6.0 (float16) | 9296.7 | 909MB | 814MB | 27.9 |
| CTranslate2 3.6.0 (int8 + float16) | 8362.7 | 813MB | 766MB | 27.9 |
`Executed with 4 threads on a c5.2xlarge Amazon EC2 instance equipped with an Intel(R) Xeon(R) Platinum 8275CL CPU.`
**Source to benchmark information can be found [here](https://github.com/OpenNMT/CTranslate2).**<br />
**Original model BLEU scores can be found [here](https://huggingface.co/Helsinki-NLP/opus-mt-sal-en).**
## Internal Benchmarks
Internal testing on our end showed **inference times reduced by 6x-10x** on average compared the vanilla checkpoints using the *transformers* library. A **slight reduction on BLEU scores (~5%)** was also identified in comparison to the vanilla checkpoints with a few exceptions. This is likely due to several factors, one being the quantization applied. Further testing is needed from our end to better assess the reduction in translation quality. The command used to compile the vanilla checkpoint into a CTranslate2 model can be found below. Modifying this command can yield differing balances between inferencing performance and translation quality.
# CTranslate2 Installation
```bash
pip install hf-hub-ctranslate2>=1.0.0 ctranslate2>=3.13.0
```
### ct2-transformers-converter Command Used:
```bash
ct2-transformers-converter --model Helsinki-NLP/opus-mt-sal-en --output_dir ./ctranslate2/opus-mt-sal-en-ctranslate2 --force --copy_files README.md generation_config.json tokenizer_config.json vocab.json source.spm .gitattributes target.spm --quantization float16
```
# CTranslate2 Converted Checkpoint Information:
**Compatible With:**
- [ctranslate2](https://github.com/OpenNMT/CTranslate2)
- [hf-hub-ctranslate2](https://github.com/michaelfeil/hf-hub-ctranslate2)
**Compute Type:**
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
# Sample Code - ctranslate2
#### Clone the repository to the working directory or wherever you wish to store the model artifacts. ####
```bash
git clone https://huggingface.co/gaudi/opus-mt-sal-en-ctranslate2
```
#### Take the python code below and update the 'model_dir' variable to the location of the cloned repository. ####
```python
from ctranslate2 import Translator
import transformers
model_dir = "./opus-mt-sal-en-ctranslate2" # Path to model directory.
translator = Translator(
model_path=model_dir,
device="cuda", # cpu, cuda, or auto.
inter_threads=1, # Maximum number of parallel translations.
intra_threads=4, # Number of OpenMP threads per translator.
compute_type="int8_float16", # int8 for cpu or int8_float16 for cuda.
)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_dir)
source = tokenizer.convert_ids_to_tokens(tokenizer.encode("XXXXXX, XXX XX XXXXXX."))
results = translator.translate_batch([source])
target = results[0].hypotheses[0]
print(tokenizer.decode(tokenizer.convert_tokens_to_ids(target)))
```
# Sample Code - hf-hub-ctranslate2
**Derived From [michaelfeil](https://huggingface.co/michaelfeil):**
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "gaudi/opus-mt-sal-en-ctranslate2"
model = TranslatorCT2fromHfHub(
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
tokenizer=AutoTokenizer.from_pretrained(model_name)
)
outputs = model.generate(
text=["XXX XX XXX XXXXXXX XXXX?", "XX XX XXXX XX XXX!"],
)
print(outputs)
```
# License and other remarks:
License conditions are intended to be idential to [original huggingface repository](https://huggingface.co/Helsinki-NLP/opus-mt-sal-en) by Helsinki-NLP.
|
{"license": "apache-2.0", "tags": ["ctranslate2", "translation"]}
|
task
|
[
"TRANSLATION"
] | 41,981 |
dev-ashvi/sarvam_hinglish_translation
|
dev-ashvi
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:sarvamai/sarvam-1",
"base_model:adapter:sarvamai/sarvam-1",
"region:us"
] | 2025-03-05T11:32:46Z |
2025-03-05T13:54:31+00:00
| 11 | 0 |
---
base_model: sarvamai/sarvam-1
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: sarvam_hinglish_translation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sarvam_hinglish_translation
This model is a fine-tuned version of [sarvamai/sarvam-1](https://huggingface.co/sarvamai/sarvam-1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9108
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.0273 | 1.0 | 2742 | 6.0680 |
| 5.9235 | 2.0 | 5484 | 5.9861 |
| 5.8664 | 3.0 | 8226 | 5.9383 |
| 5.8076 | 4.0 | 10968 | 5.9181 |
| 5.8116 | 5.0 | 13710 | 5.9108 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sarvam_hinglish_translation
This model is a fine-tuned version of [sarvamai/sarvam-1](https://huggingface.co/sarvamai/sarvam-1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9108
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.0273 | 1.0 | 2742 | 6.0680 |
| 5.9235 | 2.0 | 5484 | 5.9861 |
| 5.8664 | 3.0 | 8226 | 5.9383 |
| 5.8076 | 4.0 | 10968 | 5.9181 |
| 5.8116 | 5.0 | 13710 | 5.9108 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0
|
{"base_model": "sarvamai/sarvam-1", "library_name": "peft", "tags": ["generated_from_trainer"], "model-index": [{"name": "sarvam_hinglish_translation", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 41,982 |
NorahAlshahrani/BERThard
|
NorahAlshahrani
|
text-classification
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"ar",
"dataset:hard",
"arxiv:2402.03477",
"license:mit",
"autotrain_compatible",
"region:us"
] | 2023-05-01T17:04:54Z |
2024-04-02T21:29:38+00:00
| 23 | 0 |
---
datasets:
- hard
language:
- ar
license: mit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- generated_from_trainer
inference: false
model-index:
- name: BERThard
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERThard
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the Hotel Arabic Reviews Dataset (HARD) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4141
- Accuracy: 0.8311
## BibTeX Citations:
```bash
@inproceedings{alshahrani-etal-2024-arabic,
title = "{{A}rabic Synonym {BERT}-based Adversarial Examples for Text Classification}",
author = "Alshahrani, Norah and
Alshahrani, Saied and
Wali, Esma and
Matthews, Jeanna",
editor = "Falk, Neele and
Papi, Sara and
Zhang, Mike",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-srw.10",
pages = "137--147",
abstract = "Text classification systems have been proven vulnerable to adversarial text examples, modified versions of the original text examples that are often unnoticed by human eyes, yet can force text classification models to alter their classification. Often, research works quantifying the impact of adversarial text attacks have been applied only to models trained in English. In this paper, we introduce the first word-level study of adversarial attacks in Arabic. Specifically, we use a synonym (word-level) attack using a Masked Language Modeling (MLM) task with a BERT model in a black-box setting to assess the robustness of the state-of-the-art text classification models to adversarial attacks in Arabic. To evaluate the grammatical and semantic similarities of the newly produced adversarial examples using our synonym BERT-based attack, we invite four human evaluators to assess and compare the produced adversarial examples with their original examples. We also study the transferability of these newly produced Arabic adversarial examples to various models and investigate the effectiveness of defense mechanisms against these adversarial examples on the BERT models. We find that fine-tuned BERT models were more susceptible to our synonym attacks than the other Deep Neural Networks (DNN) models like WordCNN and WordLSTM we trained. We also find that fine-tuned BERT models were more susceptible to transferred attacks. We, lastly, find that fine-tuned BERT models successfully regain at least 2{\%} in accuracy after applying adversarial training as an initial defense mechanism.",
}
```
```bash
@misc{alshahrani2024arabic,
title={{Arabic Synonym BERT-based Adversarial Examples for Text Classification}},
author={Norah Alshahrani and Saied Alshahrani and Esma Wali and Jeanna Matthews},
year={2024},
eprint={2402.03477},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Training procedure
We have trained this model using the PaperSpace GPU-Cloud service. We used a machine with 8 CPUs, 45GB RAM, and A6000 GPU with 48GB RAM.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4488 | 1.0 | 5946 | 0.4104 | 0.8232 |
| 0.3866 | 2.0 | 11892 | 0.4047 | 0.8288 |
| 0.3462 | 3.0 | 17838 | 0.4141 | 0.8311 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERThard
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the Hotel Arabic Reviews Dataset (HARD) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4141
- Accuracy: 0.8311
## BibTeX Citations:
```bash
@inproceedings{alshahrani-etal-2024-arabic,
title = "{{A}rabic Synonym {BERT}-based Adversarial Examples for Text Classification}",
author = "Alshahrani, Norah and
Alshahrani, Saied and
Wali, Esma and
Matthews, Jeanna",
editor = "Falk, Neele and
Papi, Sara and
Zhang, Mike",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-srw.10",
pages = "137--147",
abstract = "Text classification systems have been proven vulnerable to adversarial text examples, modified versions of the original text examples that are often unnoticed by human eyes, yet can force text classification models to alter their classification. Often, research works quantifying the impact of adversarial text attacks have been applied only to models trained in English. In this paper, we introduce the first word-level study of adversarial attacks in Arabic. Specifically, we use a synonym (word-level) attack using a Masked Language Modeling (MLM) task with a BERT model in a black-box setting to assess the robustness of the state-of-the-art text classification models to adversarial attacks in Arabic. To evaluate the grammatical and semantic similarities of the newly produced adversarial examples using our synonym BERT-based attack, we invite four human evaluators to assess and compare the produced adversarial examples with their original examples. We also study the transferability of these newly produced Arabic adversarial examples to various models and investigate the effectiveness of defense mechanisms against these adversarial examples on the BERT models. We find that fine-tuned BERT models were more susceptible to our synonym attacks than the other Deep Neural Networks (DNN) models like WordCNN and WordLSTM we trained. We also find that fine-tuned BERT models were more susceptible to transferred attacks. We, lastly, find that fine-tuned BERT models successfully regain at least 2{\%} in accuracy after applying adversarial training as an initial defense mechanism.",
}
```
```bash
@misc{alshahrani2024arabic,
title={{Arabic Synonym BERT-based Adversarial Examples for Text Classification}},
author={Norah Alshahrani and Saied Alshahrani and Esma Wali and Jeanna Matthews},
year={2024},
eprint={2402.03477},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Training procedure
We have trained this model using the PaperSpace GPU-Cloud service. We used a machine with 8 CPUs, 45GB RAM, and A6000 GPU with 48GB RAM.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4488 | 1.0 | 5946 | 0.4104 | 0.8232 |
| 0.3866 | 2.0 | 11892 | 0.4047 | 0.8288 |
| 0.3462 | 3.0 | 17838 | 0.4141 | 0.8311 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
|
{"datasets": ["hard"], "language": ["ar"], "license": "mit", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "tags": ["generated_from_trainer"], "inference": false, "model-index": [{"name": "BERThard", "results": []}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,983 |
luohoa97/PersonalBot
|
luohoa97
|
text-classification
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-07-17T01:48:09Z |
2024-07-17T02:19:20+00:00
| 178 | 0 |
---
language:
- en
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** luohoa97
- **Model type:** Question Answering
- **Language(s) (NLP):** [More Information Needed]
- **License:**
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| null |
Non_BioNLP
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** luohoa97
- **Model type:** Question Answering
- **Language(s) (NLP):** [More Information Needed]
- **License:**
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"language": ["en"], "library_name": "transformers", "license": "apache-2.0"}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,984 |
NeuML/pubmedbert-base-embeddings-100K
|
NeuML
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"model2vec",
"feature-extraction",
"sentence-similarity",
"transformers",
"embeddings",
"static-embeddings",
"en",
"arxiv:1909.11687",
"base_model:NeuML/pubmedbert-base-embeddings",
"base_model:finetune:NeuML/pubmedbert-base-embeddings",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-01-03T20:58:10Z |
2025-01-19T12:20:00+00:00
| 12 | 2 |
---
base_model:
- NeuML/pubmedbert-base-embeddings
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- embeddings
- static-embeddings
---
# PubMedBERT Embeddings 100K
This is a pruned version of [PubMedBERT Embeddings 2M](https://huggingface.co/NeuML/pubmedbert-base-embeddings-2M). It prunes the vocabulary to take the top 5% most frequently used tokens.
See [Extremely Small BERT Models from Mixed-Vocabulary Training](https://arxiv.org/abs/1909.11687) for background on pruning vocabularies to build smaller models.
## Usage (txtai)
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
```python
import txtai
# Create embeddings
embeddings = txtai.Embeddings(
path="neuml/pubmedbert-base-embeddings-100K",
content=True,
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
## Usage (Sentence-Transformers)
Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding
# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-100K")
model = SentenceTransformer(modules=[static])
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (Model2Vec)
The model can also be used directly with Model2Vec.
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-100K")
# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
The following compares performance of this model against the models previously compared with [PubMedBERT Embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings#evaluation-results). The following datasets were used to evaluate model performance.
- [PubMed QA](https://huggingface.co/datasets/pubmed_qa)
- Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
- [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k)
- Split: test, Pair: (title, text)
- _Note: The previously used [PubMed Subset](https://huggingface.co/datasets/zxvix/pubmed_subset_new) dataset is no longer available but a similar dataset is used here_
- [PubMed Summary](https://huggingface.co/datasets/scientific_papers)
- Subset: pubmed, Split: validation, Pair: (article, abstract)
The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.
| Model | PubMed QA | PubMed Subset | PubMed Summary | Average |
| -------------------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- |
| pubmedbert-base-embeddings-8M-M2V (No training) | 69.84 | 70.77 | 71.30 | 70.64 |
| [**pubmedbert-base-embeddings-100K**](https://hf.co/neuml/pubmedbert-base-embeddings-100K) | **74.56** | **84.65** | **81.84** | **80.35** |
| [pubmedbert-base-embeddings-500K](https://hf.co/neuml/pubmedbert-base-embeddings-500K) | 86.03 | 91.71 | 91.25 | 89.66 |
| [pubmedbert-base-embeddings-1M](https://hf.co/neuml/pubmedbert-base-embeddings-1M) | 87.87 | 92.80 | 92.87 | 91.18 |
| [pubmedbert-base-embeddings-2M](https://hf.co/neuml/pubmedbert-base-embeddings-2M) | 88.62 | 93.08 | 93.24 | 91.65 |
It's quite a steep dropoff in accuracy compared the original unpruned model. Although this model still scores higher than the naive distilled version without training
## Runtime performance
As another test, let's see how long each model takes to index 120K article abstracts using the following code. All indexing is done with a RTX 3090 GPU.
```python
from datasets import load_dataset
from tqdm import tqdm
from txtai import Embeddings
ds = load_dataset("ccdv/pubmed-summarization", split="train")
embeddings = Embeddings(path="path to model", content=True, backend="numpy")
embeddings.index(tqdm(ds["abstract"]))
```
| Model | Model Size (MB) | Index time (s) |
| -------------------------------------------------------------------------------------- | ---------- | -------------- |
| [**pubmedbert-base-embeddings-100K**](https://hf.co/neuml/pubmedbert-base-embeddings-100K) | **0.2** | **19** |
| [pubmedbert-base-embeddings-500K](https://hf.co/neuml/pubmedbert-base-embeddings-500K) | 1.0 | 17 |
| [pubmedbert-base-embeddings-1M](https://hf.co/neuml/pubmedbert-base-embeddings-1M) | 2.0 | 17 |
| [pubmedbert-base-embeddings-2M](https://hf.co/neuml/pubmedbert-base-embeddings-2M) | 7.5 | 17 |
Vocabulary pruning leads to a slighly higher runtime. This is attributed to the fact that more tokens are needed to represent text. But the model is much smaller. Vectors are stored at `int16` precision. This can be beneficial to smaller/lower powered embedded devices and could lead to faster vectorization times.
## Training
This model was vocabulary pruned using the following script.
```python
import json
import os
from collections import Counter
from pathlib import Path
import numpy as np
from model2vec import StaticModel
from more_itertools import batched
from sklearn.decomposition import PCA
from tokenlearn.train import collect_means_and_texts
from tokenizers import Tokenizer
from tqdm import tqdm
from txtai.scoring import ScoringFactory
def tokenize(tokenizer):
# Tokenize into dataset
dataset = []
for t in tqdm(batched(texts, 1024)):
encodings = tokenizer.encode_batch_fast(t, add_special_tokens=False)
for e in encodings:
dataset.append((None, e.ids, None))
return dataset
def tokenweights(tokenizer):
dataset = tokenize(tokenizer)
# Build scoring index
scoring = ScoringFactory.create({"method": "bm25", "terms": True})
scoring.index(dataset)
# Calculate mean value of weights array per token
tokens = np.zeros(tokenizer.get_vocab_size())
for x in scoring.idf:
tokens[x] = np.mean(scoring.terms.weights(x)[1])
return tokens
# See PubMedBERT Embeddings 2M model for details on this data
features = "features"
paths = sorted(Path(features).glob("*.json"))
texts, _ = collect_means_and_texts(paths)
# Output model parameters
output = "output path"
params, dims = 100000, 64
path = "pubmedbert-base-embeddings-2M_unweighted"
model = StaticModel.from_pretrained(path)
os.makedirs(output, exist_ok=True)
with open(f"{path}/tokenizer.json", "r", encoding="utf-8") as f:
config = json.load(f)
# Calculate number of tokens to keep
tokencount = params // model.dim
# Calculate term frequency
freqs = Counter()
for _, ids, _ in tokenize(model.tokenizer):
freqs.update(ids)
# Select top N most common tokens
uids = set(x for x, _ in freqs.most_common(tokencount))
uids = [uid for token, uid in config["model"]["vocab"].items() if uid in uids or token.startswith("[")]
# Get embeddings for uids
model.embedding = model.embedding[uids]
# Select pruned tokens
pairs, index = [], 0
for token, uid in config["model"]["vocab"].items():
if uid in uids:
pairs.append((token, index))
index += 1
config["model"]["vocab"] = dict(pairs)
# Write new tokenizer
with open(f"{output}/tokenizer.json", "w", encoding="utf-8") as f:
json.dump(config, f, indent=2)
model.tokenizer = Tokenizer.from_file(f"{output}/tokenizer.json")
# Re-weight tokens
weights = tokenweights(model.tokenizer)
# Remove NaNs from embedding, if any
embedding = np.nan_to_num(model.embedding)
# Apply PCA
embedding = PCA(n_components=dims).fit_transform(embedding)
# Apply weights
embedding *= weights[:, None]
# Update model embedding and normalize
model.embedding, model.normalize = embedding.astype(np.int16), True
model.save_pretrained(output)
```
## Acknowledgement
This model is built on the great work from the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
Read more at the following links.
- [Model2Vec](https://github.com/MinishLab/model2vec)
- [Tokenlearn](https://github.com/MinishLab/tokenlearn)
- [Minish Lab Blog](https://minishlab.github.io/)
| null |
BioNLP
|
# PubMedBERT Embeddings 100K
This is a pruned version of [PubMedBERT Embeddings 2M](https://huggingface.co/NeuML/pubmedbert-base-embeddings-2M). It prunes the vocabulary to take the top 5% most frequently used tokens.
See [Extremely Small BERT Models from Mixed-Vocabulary Training](https://arxiv.org/abs/1909.11687) for background on pruning vocabularies to build smaller models.
## Usage (txtai)
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
```python
import txtai
# Create embeddings
embeddings = txtai.Embeddings(
path="neuml/pubmedbert-base-embeddings-100K",
content=True,
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
## Usage (Sentence-Transformers)
Alternatively, the model can be loaded with [sentence-transformers](https://www.SBERT.net).
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import StaticEmbedding
# Initialize a StaticEmbedding module
static = StaticEmbedding.from_model2vec("neuml/pubmedbert-base-embeddings-100K")
model = SentenceTransformer(modules=[static])
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (Model2Vec)
The model can also be used directly with Model2Vec.
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("neuml/pubmedbert-base-embeddings-100K")
# Compute text embeddings
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
The following compares performance of this model against the models previously compared with [PubMedBERT Embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings#evaluation-results). The following datasets were used to evaluate model performance.
- [PubMed QA](https://huggingface.co/datasets/pubmed_qa)
- Subset: pqa_labeled, Split: train, Pair: (question, long_answer)
- [PubMed Subset](https://huggingface.co/datasets/awinml/pubmed_abstract_3_1k)
- Split: test, Pair: (title, text)
- _Note: The previously used [PubMed Subset](https://huggingface.co/datasets/zxvix/pubmed_subset_new) dataset is no longer available but a similar dataset is used here_
- [PubMed Summary](https://huggingface.co/datasets/scientific_papers)
- Subset: pubmed, Split: validation, Pair: (article, abstract)
The [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is used as the evaluation metric.
| Model | PubMed QA | PubMed Subset | PubMed Summary | Average |
| -------------------------------------------------------------------------------------- | --------- | ------------- | -------------- | --------- |
| pubmedbert-base-embeddings-8M-M2V (No training) | 69.84 | 70.77 | 71.30 | 70.64 |
| [**pubmedbert-base-embeddings-100K**](https://hf.co/neuml/pubmedbert-base-embeddings-100K) | **74.56** | **84.65** | **81.84** | **80.35** |
| [pubmedbert-base-embeddings-500K](https://hf.co/neuml/pubmedbert-base-embeddings-500K) | 86.03 | 91.71 | 91.25 | 89.66 |
| [pubmedbert-base-embeddings-1M](https://hf.co/neuml/pubmedbert-base-embeddings-1M) | 87.87 | 92.80 | 92.87 | 91.18 |
| [pubmedbert-base-embeddings-2M](https://hf.co/neuml/pubmedbert-base-embeddings-2M) | 88.62 | 93.08 | 93.24 | 91.65 |
It's quite a steep dropoff in accuracy compared the original unpruned model. Although this model still scores higher than the naive distilled version without training
## Runtime performance
As another test, let's see how long each model takes to index 120K article abstracts using the following code. All indexing is done with a RTX 3090 GPU.
```python
from datasets import load_dataset
from tqdm import tqdm
from txtai import Embeddings
ds = load_dataset("ccdv/pubmed-summarization", split="train")
embeddings = Embeddings(path="path to model", content=True, backend="numpy")
embeddings.index(tqdm(ds["abstract"]))
```
| Model | Model Size (MB) | Index time (s) |
| -------------------------------------------------------------------------------------- | ---------- | -------------- |
| [**pubmedbert-base-embeddings-100K**](https://hf.co/neuml/pubmedbert-base-embeddings-100K) | **0.2** | **19** |
| [pubmedbert-base-embeddings-500K](https://hf.co/neuml/pubmedbert-base-embeddings-500K) | 1.0 | 17 |
| [pubmedbert-base-embeddings-1M](https://hf.co/neuml/pubmedbert-base-embeddings-1M) | 2.0 | 17 |
| [pubmedbert-base-embeddings-2M](https://hf.co/neuml/pubmedbert-base-embeddings-2M) | 7.5 | 17 |
Vocabulary pruning leads to a slighly higher runtime. This is attributed to the fact that more tokens are needed to represent text. But the model is much smaller. Vectors are stored at `int16` precision. This can be beneficial to smaller/lower powered embedded devices and could lead to faster vectorization times.
## Training
This model was vocabulary pruned using the following script.
```python
import json
import os
from collections import Counter
from pathlib import Path
import numpy as np
from model2vec import StaticModel
from more_itertools import batched
from sklearn.decomposition import PCA
from tokenlearn.train import collect_means_and_texts
from tokenizers import Tokenizer
from tqdm import tqdm
from txtai.scoring import ScoringFactory
def tokenize(tokenizer):
# Tokenize into dataset
dataset = []
for t in tqdm(batched(texts, 1024)):
encodings = tokenizer.encode_batch_fast(t, add_special_tokens=False)
for e in encodings:
dataset.append((None, e.ids, None))
return dataset
def tokenweights(tokenizer):
dataset = tokenize(tokenizer)
# Build scoring index
scoring = ScoringFactory.create({"method": "bm25", "terms": True})
scoring.index(dataset)
# Calculate mean value of weights array per token
tokens = np.zeros(tokenizer.get_vocab_size())
for x in scoring.idf:
tokens[x] = np.mean(scoring.terms.weights(x)[1])
return tokens
# See PubMedBERT Embeddings 2M model for details on this data
features = "features"
paths = sorted(Path(features).glob("*.json"))
texts, _ = collect_means_and_texts(paths)
# Output model parameters
output = "output path"
params, dims = 100000, 64
path = "pubmedbert-base-embeddings-2M_unweighted"
model = StaticModel.from_pretrained(path)
os.makedirs(output, exist_ok=True)
with open(f"{path}/tokenizer.json", "r", encoding="utf-8") as f:
config = json.load(f)
# Calculate number of tokens to keep
tokencount = params // model.dim
# Calculate term frequency
freqs = Counter()
for _, ids, _ in tokenize(model.tokenizer):
freqs.update(ids)
# Select top N most common tokens
uids = set(x for x, _ in freqs.most_common(tokencount))
uids = [uid for token, uid in config["model"]["vocab"].items() if uid in uids or token.startswith("[")]
# Get embeddings for uids
model.embedding = model.embedding[uids]
# Select pruned tokens
pairs, index = [], 0
for token, uid in config["model"]["vocab"].items():
if uid in uids:
pairs.append((token, index))
index += 1
config["model"]["vocab"] = dict(pairs)
# Write new tokenizer
with open(f"{output}/tokenizer.json", "w", encoding="utf-8") as f:
json.dump(config, f, indent=2)
model.tokenizer = Tokenizer.from_file(f"{output}/tokenizer.json")
# Re-weight tokens
weights = tokenweights(model.tokenizer)
# Remove NaNs from embedding, if any
embedding = np.nan_to_num(model.embedding)
# Apply PCA
embedding = PCA(n_components=dims).fit_transform(embedding)
# Apply weights
embedding *= weights[:, None]
# Update model embedding and normalize
model.embedding, model.normalize = embedding.astype(np.int16), True
model.save_pretrained(output)
```
## Acknowledgement
This model is built on the great work from the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
Read more at the following links.
- [Model2Vec](https://github.com/MinishLab/model2vec)
- [Tokenlearn](https://github.com/MinishLab/tokenlearn)
- [Minish Lab Blog](https://minishlab.github.io/)
|
{"base_model": ["NeuML/pubmedbert-base-embeddings"], "language": "en", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "embeddings", "static-embeddings"]}
|
task
|
[
"SUMMARIZATION"
] | 41,985 |
RichardErkhov/Unbabel_-_TowerInstruct-7B-v0.1-8bits
|
RichardErkhov
|
text-generation
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2402.17733",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | 2024-05-11T07:30:11Z |
2024-05-11T08:42:54+00:00
| 5 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TowerInstruct-7B-v0.1 - bnb 8bits
- Model creator: https://huggingface.co/Unbabel/
- Original model: https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1/
Original model description:
---
license: cc-by-nc-4.0
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
metrics:
- comet
pipeline_tag: translation
---
# Model Card for TowerInstruct-7B-v0.1
## Model Details
### Model Description
TowerInstruct-7B is a language model that results from fine-tuning TowerBase on the TowerBlocks supervised fine-tuning dataset. TowerInstruct-7B-v0.1 is the first model in the series.
The model is trained to handle several translation-related tasks, such as general machine translation (e.g., sentence- and paragraph-level translation, terminology-aware translation, context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.
We will release more details in the upcoming technical report.
- **Developed by:** Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay
- **Model type:** A 7B parameter model fine-tuned on a mix of publicly available, synthetic datasets on translation-related tasks, as well as conversational datasets and code instructions.
- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
- **License:** CC-BY-NC-4.0, Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
- **Finetuned from model:** [TowerBase](https://huggingface.co/Unbabel/TowerBase-7B-v0.1)
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed supervised fine-tuning dataset ([TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1)), which contains a diverse range of data sources:
- Translation (sentence and paragraph-level)
- Automatic Post Edition
- Machine Translation Evaluation
- Context-aware Translation
- Terminology-aware Translation
- Multi-reference Translation
- Named-entity Recognition
- Paraphrase Generation
- Synthetic Chat data
- Code instructions
You can find the dataset and all data sources of [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1) here.
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="Unbabel/TowerInstruct-v0.1", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "user", "content": "Translate the following text from Portuguese into English.\nPortuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.\nEnglish:"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
print(outputs[0]["generated_text"])
# <|im_start|>user
# Translate the following text from Portuguese into English.
# Portuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.
# English:<|im_end|>
# <|im_start|>assistant
# A group of researchers has launched a new model for translation-related tasks.
```
### Out-of-Scope Use
The model is not guaranteed to perform for languages other than the 10 languages it supports. Even though we trained the model on conversational data and code instructions, it is not intended to be used as a conversational chatbot or code assistant.
We are currently working on improving quality and consistency on document-level translation. This model should is not intended to be use as a document-level translator.
## Bias, Risks, and Limitations
TowerInstruct-v0.1 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
## Prompt Format
TowerInstruct-v0.1 was trained using the ChatML prompt templates without any system prompts. An example follows below:
```
<|im_start|>user
{USER PROMPT}<|im_end|>
<|im_start|>assistant
{MODEL RESPONSE}<|im_end|>
<|im_start|>user
[...]
```
### Supervised tasks
The prompts for all supervised tasks can be found in [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1). We have used multiple prompt templates for each task. While different prompts may offer different outputs, the difference in downstream performance should be very minimal.
## Training Details
### Training Data
Link to [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1).
#### Training Hyperparameters
The following hyperparameters were used during training:
- total_train_batch_size: 256
- learning_rate: 7e-06
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 4
- max_seq_length: 2048
## Citation
```bibtex
@misc{tower_llm_2024,
title={Tower: An Open Multilingual Large Language Model for Translation-Related Tasks},
author={Duarte M. Alves and José Pombal and Nuno M. Guerreiro and Pedro H. Martins and João Alves and Amin Farajian and Ben Peters and Ricardo Rei and Patrick Fernandes and Sweta Agrawal and Pierre Colombo and José G. C. de Souza and André F. T. Martins},
year={2024},
eprint={2402.17733},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| null |
Non_BioNLP
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TowerInstruct-7B-v0.1 - bnb 8bits
- Model creator: https://huggingface.co/Unbabel/
- Original model: https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1/
Original model description:
---
license: cc-by-nc-4.0
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
metrics:
- comet
pipeline_tag: translation
---
# Model Card for TowerInstruct-7B-v0.1
## Model Details
### Model Description
TowerInstruct-7B is a language model that results from fine-tuning TowerBase on the TowerBlocks supervised fine-tuning dataset. TowerInstruct-7B-v0.1 is the first model in the series.
The model is trained to handle several translation-related tasks, such as general machine translation (e.g., sentence- and paragraph-level translation, terminology-aware translation, context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.
We will release more details in the upcoming technical report.
- **Developed by:** Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay
- **Model type:** A 7B parameter model fine-tuned on a mix of publicly available, synthetic datasets on translation-related tasks, as well as conversational datasets and code instructions.
- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
- **License:** CC-BY-NC-4.0, Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
- **Finetuned from model:** [TowerBase](https://huggingface.co/Unbabel/TowerBase-7B-v0.1)
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed supervised fine-tuning dataset ([TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1)), which contains a diverse range of data sources:
- Translation (sentence and paragraph-level)
- Automatic Post Edition
- Machine Translation Evaluation
- Context-aware Translation
- Terminology-aware Translation
- Multi-reference Translation
- Named-entity Recognition
- Paraphrase Generation
- Synthetic Chat data
- Code instructions
You can find the dataset and all data sources of [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1) here.
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="Unbabel/TowerInstruct-v0.1", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "user", "content": "Translate the following text from Portuguese into English.\nPortuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.\nEnglish:"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
print(outputs[0]["generated_text"])
# <|im_start|>user
# Translate the following text from Portuguese into English.
# Portuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.
# English:<|im_end|>
# <|im_start|>assistant
# A group of researchers has launched a new model for translation-related tasks.
```
### Out-of-Scope Use
The model is not guaranteed to perform for languages other than the 10 languages it supports. Even though we trained the model on conversational data and code instructions, it is not intended to be used as a conversational chatbot or code assistant.
We are currently working on improving quality and consistency on document-level translation. This model should is not intended to be use as a document-level translator.
## Bias, Risks, and Limitations
TowerInstruct-v0.1 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
## Prompt Format
TowerInstruct-v0.1 was trained using the ChatML prompt templates without any system prompts. An example follows below:
```
<|im_start|>user
{USER PROMPT}<|im_end|>
<|im_start|>assistant
{MODEL RESPONSE}<|im_end|>
<|im_start|>user
[...]
```
### Supervised tasks
The prompts for all supervised tasks can be found in [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1). We have used multiple prompt templates for each task. While different prompts may offer different outputs, the difference in downstream performance should be very minimal.
## Training Details
### Training Data
Link to [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1).
#### Training Hyperparameters
The following hyperparameters were used during training:
- total_train_batch_size: 256
- learning_rate: 7e-06
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 4
- max_seq_length: 2048
## Citation
```bibtex
@misc{tower_llm_2024,
title={Tower: An Open Multilingual Large Language Model for Translation-Related Tasks},
author={Duarte M. Alves and José Pombal and Nuno M. Guerreiro and Pedro H. Martins and João Alves and Amin Farajian and Ben Peters and Ricardo Rei and Patrick Fernandes and Sweta Agrawal and Pierre Colombo and José G. C. de Souza and André F. T. Martins},
year={2024},
eprint={2402.17733},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
|
{}
|
task
|
[
"TRANSLATION"
] | 41,987 |
vibhorg/rl4llm_uofm_nlpo_super_t5_arxiv
|
vibhorg
|
text2text-generation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text-generation-inference",
"rlhf",
"PPO",
"en",
"dataset:scientific_papers",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-03-13T21:39:55Z |
2024-03-20T16:55:20+00:00
| 10 | 0 |
---
datasets:
- scientific_papers
language:
- en
license: apache-2.0
metrics:
- bertscore
- rouge
tags:
- text-generation-inference
- rlhf
- PPO
---
This model is fintuned using PPO based NLPO RL algorithm, on ccdv/arxiv-summarization dataset. The base model is pretunerd version of flan-t5-base model.
| null |
Non_BioNLP
|
This model is fintuned using PPO based NLPO RL algorithm, on ccdv/arxiv-summarization dataset. The base model is pretunerd version of flan-t5-base model.
|
{"datasets": ["scientific_papers"], "language": ["en"], "license": "apache-2.0", "metrics": ["bertscore", "rouge"], "tags": ["text-generation-inference", "rlhf", "PPO"]}
|
task
|
[
"SUMMARIZATION"
] | 41,988 |
ibivibiv/aegolius-acadicus-34b-v3
|
ibivibiv
|
text-generation
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2009.03300",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-28T07:04:50Z |
2024-03-04T23:44:02+00:00
| 12 | 0 |
---
language:
- en
library_name: transformers
license: apache-2.0
tags:
- moe
model-index:
- name: aegolius-acadicus-34b-v3
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 67.66
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 62.13
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 63.33
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 54.21
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3
name: Open LLM Leaderboard
---
# Aegolius Acadicus 34b v3
MOE 5x7b model using the Mixtral branch of the mergekit. NOT A MERGE. It is tagged as an moe and is an moe. It is not a merge of models.
I like to call this model series "The little professor". I am funding this out of my pocket on rented hardware and runpod to create lora adapters and then assemble MOE models from them and others. Ultimately I hope to have them all be lora's that I have made. This is no different than Mixtral and I am literally using their tooling. It is simply a MOE of lora merged models across Llama2 and Mistral. I am using this as a test case to move to larger models and get my gate discrimination set correctly. This model is best suited for knowledge related use cases, I did not give it a specific workload target as I did with some of the other models in the "Owl Series".
In this particular run I am expanding data sets and model count to see if that helps/hurts. I am also moving to more of my own fine tuned mistrals
This model is an moe of the following models:
[Fine Tuned Mistral of Mine](https://huggingface.co/ibivibiv/temp_tuned_mistral2)
[Fine Tuned Mistral of Mine](https://huggingface.co/ibivibiv/temp_tuned_mistral3)
[WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
[flux-7b-v0.1](https://huggingface.co/chanwit/flux-7b-v0.1)
[senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
[WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)
The goal here is to create specialized models that can collaborate and run as one model.
# Prompting
## Prompt Template for alpaca style
```
### Instruction:
<prompt> (without the <>)
### Response:
```
## Sample Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("ibivibiv/aegolius-acadicus-34b-v3", torch_dtype="auto", device_config='auto')
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/aegolius-acadicus-34b-v3")
inputs = tokenizer("### Instruction: Who would when in an arm wrestling match between Abraham Lincoln and Chuck Norris?\n### Response:\n", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
# Model Details
* **Trained by**: [ibivibiv](https://huggingface.co/ibivibiv)
* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
* **Model type:** **aegolius-acadicus-24b-v2** is an auto-regressive language model moe from Llama 2 transformer architecture models and mistral models.
* **Language(s)**: English
* **Purpose**: This model is an attempt at an moe model to cover multiple disciplines using finetuned llama 2 and mistral models as base models.
# Benchmark Scores
coming soon
## Citations
```
@misc{open-llm-leaderboard,
author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
title = {Open LLM Leaderboard},
year = {2023},
publisher = {Hugging Face},
howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
}
```
```
@software{eval-harness,
author = {Gao, Leo and
Tow, Jonathan and
Biderman, Stella and
Black, Sid and
DiPofi, Anthony and
Foster, Charles and
Golding, Laurence and
Hsu, Jeffrey and
McDonell, Kyle and
Muennighoff, Niklas and
Phang, Jason and
Reynolds, Laria and
Tang, Eric and
Thite, Anish and
Wang, Ben and
Wang, Kevin and
Zou, Andy},
title = {A framework for few-shot language model evaluation},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.5371628},
url = {https://doi.org/10.5281/zenodo.5371628}
}
```
```
@misc{clark2018think,
title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
year={2018},
eprint={1803.05457},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```
@misc{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
year={2019},
eprint={1905.07830},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{hendrycks2021measuring,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
year={2021},
eprint={2009.03300},
archivePrefix={arXiv},
primaryClass={cs.CY}
}
```
```
@misc{lin2022truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2022},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{DBLP:journals/corr/abs-1907-10641,
title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale},
author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
year={2019},
eprint={1907.10641},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{DBLP:journals/corr/abs-2110-14168,
title={Training Verifiers to Solve Math Word Problems},
author={Karl Cobbe and
Vineet Kosaraju and
Mohammad Bavarian and
Mark Chen and
Heewoo Jun and
Lukasz Kaiser and
Matthias Plappert and
Jerry Tworek and
Jacob Hilton and
Reiichiro Nakano and
Christopher Hesse and
John Schulman},
year={2021},
eprint={2110.14168},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__aegolius-acadicus-34b-v3)
| Metric |Value|
|---------------------------------|----:|
|Avg. |68.59|
|AI2 Reasoning Challenge (25-Shot)|67.66|
|HellaSwag (10-Shot) |85.54|
|MMLU (5-Shot) |62.13|
|TruthfulQA (0-shot) |63.33|
|Winogrande (5-shot) |78.69|
|GSM8k (5-shot) |54.21|
| null |
Non_BioNLP
|
# Aegolius Acadicus 34b v3
MOE 5x7b model using the Mixtral branch of the mergekit. NOT A MERGE. It is tagged as an moe and is an moe. It is not a merge of models.
I like to call this model series "The little professor". I am funding this out of my pocket on rented hardware and runpod to create lora adapters and then assemble MOE models from them and others. Ultimately I hope to have them all be lora's that I have made. This is no different than Mixtral and I am literally using their tooling. It is simply a MOE of lora merged models across Llama2 and Mistral. I am using this as a test case to move to larger models and get my gate discrimination set correctly. This model is best suited for knowledge related use cases, I did not give it a specific workload target as I did with some of the other models in the "Owl Series".
In this particular run I am expanding data sets and model count to see if that helps/hurts. I am also moving to more of my own fine tuned mistrals
This model is an moe of the following models:
[Fine Tuned Mistral of Mine](https://huggingface.co/ibivibiv/temp_tuned_mistral2)
[Fine Tuned Mistral of Mine](https://huggingface.co/ibivibiv/temp_tuned_mistral3)
[WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
[flux-7b-v0.1](https://huggingface.co/chanwit/flux-7b-v0.1)
[senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
[WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)
The goal here is to create specialized models that can collaborate and run as one model.
# Prompting
## Prompt Template for alpaca style
```
### Instruction:
<prompt> (without the <>)
### Response:
```
## Sample Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("ibivibiv/aegolius-acadicus-34b-v3", torch_dtype="auto", device_config='auto')
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/aegolius-acadicus-34b-v3")
inputs = tokenizer("### Instruction: Who would when in an arm wrestling match between Abraham Lincoln and Chuck Norris?\n### Response:\n", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
# Model Details
* **Trained by**: [ibivibiv](https://huggingface.co/ibivibiv)
* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
* **Model type:** **aegolius-acadicus-24b-v2** is an auto-regressive language model moe from Llama 2 transformer architecture models and mistral models.
* **Language(s)**: English
* **Purpose**: This model is an attempt at an moe model to cover multiple disciplines using finetuned llama 2 and mistral models as base models.
# Benchmark Scores
coming soon
## Citations
```
@misc{open-llm-leaderboard,
author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
title = {Open LLM Leaderboard},
year = {2023},
publisher = {Hugging Face},
howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
}
```
```
@software{eval-harness,
author = {Gao, Leo and
Tow, Jonathan and
Biderman, Stella and
Black, Sid and
DiPofi, Anthony and
Foster, Charles and
Golding, Laurence and
Hsu, Jeffrey and
McDonell, Kyle and
Muennighoff, Niklas and
Phang, Jason and
Reynolds, Laria and
Tang, Eric and
Thite, Anish and
Wang, Ben and
Wang, Kevin and
Zou, Andy},
title = {A framework for few-shot language model evaluation},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.5371628},
url = {https://doi.org/10.5281/zenodo.5371628}
}
```
```
@misc{clark2018think,
title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
year={2018},
eprint={1803.05457},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
```
@misc{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
year={2019},
eprint={1905.07830},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{hendrycks2021measuring,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
year={2021},
eprint={2009.03300},
archivePrefix={arXiv},
primaryClass={cs.CY}
}
```
```
@misc{lin2022truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2022},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{DBLP:journals/corr/abs-1907-10641,
title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale},
author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
year={2019},
eprint={1907.10641},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{DBLP:journals/corr/abs-2110-14168,
title={Training Verifiers to Solve Math Word Problems},
author={Karl Cobbe and
Vineet Kosaraju and
Mohammad Bavarian and
Mark Chen and
Heewoo Jun and
Lukasz Kaiser and
Matthias Plappert and
Jerry Tworek and
Jacob Hilton and
Reiichiro Nakano and
Christopher Hesse and
John Schulman},
year={2021},
eprint={2110.14168},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__aegolius-acadicus-34b-v3)
| Metric |Value|
|---------------------------------|----:|
|Avg. |68.59|
|AI2 Reasoning Challenge (25-Shot)|67.66|
|HellaSwag (10-Shot) |85.54|
|MMLU (5-Shot) |62.13|
|TruthfulQA (0-shot) |63.33|
|Winogrande (5-shot) |78.69|
|GSM8k (5-shot) |54.21|
|
{"language": ["en"], "library_name": "transformers", "license": "apache-2.0", "tags": ["moe"], "model-index": [{"name": "aegolius-acadicus-34b-v3", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 67.66, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 85.54, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 62.13, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 63.33}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 78.69, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 54.21, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-34b-v3", "name": "Open LLM Leaderboard"}}]}]}
|
task
|
[
"QUESTION_ANSWERING"
] | 41,990 |
Jessica666/deberta_classifier
|
Jessica666
|
text-classification
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"multi-label text classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-07-27T15:47:20Z |
2024-07-28T20:50:16+00:00
| 11 | 0 |
---
base_model: microsoft/deberta-v3-base
license: mit
metrics:
- accuracy
- f1
- precision
- recall
tags:
- multi-label text classification
- generated_from_trainer
model-index:
- name: deberta_classifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Accuracy: 0.9955
- F1: 0.6062
- Precision: 0.8225
- Recall: 0.4799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6159 | 0.1169 | 100 | 0.5955 | 0.7621 | 0.0288 | 0.0148 | 0.4839 |
| 0.3536 | 0.2338 | 200 | 0.3085 | 0.9753 | 0.1645 | 0.1091 | 0.3341 |
| 0.1166 | 0.3507 | 300 | 0.0917 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0456 | 0.4676 | 400 | 0.0375 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0308 | 0.5845 | 500 | 0.0270 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0249 | 0.7013 | 600 | 0.0234 | 0.9942 | 0.4459 | 0.7407 | 0.3189 |
| 0.0231 | 0.8182 | 700 | 0.0211 | 0.9953 | 0.5983 | 0.7970 | 0.4789 |
| 0.0213 | 0.9351 | 800 | 0.0196 | 0.9953 | 0.5989 | 0.7998 | 0.4787 |
| 0.0197 | 1.0520 | 900 | 0.0187 | 0.9954 | 0.6029 | 0.8168 | 0.4778 |
| 0.0205 | 1.1689 | 1000 | 0.0183 | 0.9955 | 0.6062 | 0.8225 | 0.4799 |
| 0.017 | 1.2858 | 1100 | 0.0175 | 0.9959 | 0.6610 | 0.8426 | 0.5437 |
| 0.018 | 1.4027 | 1200 | 0.0170 | 0.9960 | 0.6653 | 0.8685 | 0.5392 |
| 0.0177 | 1.5196 | 1300 | 0.0165 | 0.9961 | 0.6722 | 0.8732 | 0.5464 |
| 0.0189 | 1.6365 | 1400 | 0.0162 | 0.9962 | 0.6752 | 0.8910 | 0.5435 |
| 0.0179 | 1.7534 | 1500 | 0.0159 | 0.9964 | 0.6898 | 0.9151 | 0.5535 |
| 0.0169 | 1.8703 | 1600 | 0.0158 | 0.9964 | 0.6928 | 0.9030 | 0.5620 |
| 0.0172 | 1.9871 | 1700 | 0.0156 | 0.9964 | 0.6909 | 0.9130 | 0.5557 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta_classifier
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0183
- Accuracy: 0.9955
- F1: 0.6062
- Precision: 0.8225
- Recall: 0.4799
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6159 | 0.1169 | 100 | 0.5955 | 0.7621 | 0.0288 | 0.0148 | 0.4839 |
| 0.3536 | 0.2338 | 200 | 0.3085 | 0.9753 | 0.1645 | 0.1091 | 0.3341 |
| 0.1166 | 0.3507 | 300 | 0.0917 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0456 | 0.4676 | 400 | 0.0375 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0308 | 0.5845 | 500 | 0.0270 | 0.9931 | 0.4124 | 0.5429 | 0.3325 |
| 0.0249 | 0.7013 | 600 | 0.0234 | 0.9942 | 0.4459 | 0.7407 | 0.3189 |
| 0.0231 | 0.8182 | 700 | 0.0211 | 0.9953 | 0.5983 | 0.7970 | 0.4789 |
| 0.0213 | 0.9351 | 800 | 0.0196 | 0.9953 | 0.5989 | 0.7998 | 0.4787 |
| 0.0197 | 1.0520 | 900 | 0.0187 | 0.9954 | 0.6029 | 0.8168 | 0.4778 |
| 0.0205 | 1.1689 | 1000 | 0.0183 | 0.9955 | 0.6062 | 0.8225 | 0.4799 |
| 0.017 | 1.2858 | 1100 | 0.0175 | 0.9959 | 0.6610 | 0.8426 | 0.5437 |
| 0.018 | 1.4027 | 1200 | 0.0170 | 0.9960 | 0.6653 | 0.8685 | 0.5392 |
| 0.0177 | 1.5196 | 1300 | 0.0165 | 0.9961 | 0.6722 | 0.8732 | 0.5464 |
| 0.0189 | 1.6365 | 1400 | 0.0162 | 0.9962 | 0.6752 | 0.8910 | 0.5435 |
| 0.0179 | 1.7534 | 1500 | 0.0159 | 0.9964 | 0.6898 | 0.9151 | 0.5535 |
| 0.0169 | 1.8703 | 1600 | 0.0158 | 0.9964 | 0.6928 | 0.9030 | 0.5620 |
| 0.0172 | 1.9871 | 1700 | 0.0156 | 0.9964 | 0.6909 | 0.9130 | 0.5557 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
{"base_model": "microsoft/deberta-v3-base", "license": "mit", "metrics": ["accuracy", "f1", "precision", "recall"], "tags": ["multi-label text classification", "generated_from_trainer"], "model-index": [{"name": "deberta_classifier", "results": []}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,992 |
RichardErkhov/four-two-labs_-_lynx-micro-awq
|
RichardErkhov
| null |
[
"safetensors",
"gemma",
"4-bit",
"awq",
"region:us"
] | 2025-01-06T10:31:57Z |
2025-01-06T10:34:10+00:00
| 4 | 0 |
---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
lynx-micro - AWQ
- Model creator: https://huggingface.co/four-two-labs/
- Original model: https://huggingface.co/four-two-labs/lynx-micro/
Original model description:
---
library_name: transformers
language:
- sv
- en
---
# Lynx 2B (micro)
## Model Details
### Model Description
This is the first release of a series of Swedish large language models we call "Lynx". Micro is a small model (2 billion params), but punches way above its weight!
Lynx micro is a fine-tune of Google DeepMind Gemma 2B, scores just below GPT-3.5 Turbo on [Scandeval](https://scandeval.com/swedish-nlg/). In fact, the only non OpenAI model (currently) topping the Swedish NLG board on scandeval is a fine-tune of Llama-3 by AI Sweden based on our data recipe.
We believe that this is a really capable model (for its size), but keep in mind that it is still a small model and hasn't memorized as much as larger models tend to do.
- **Funded, Developed and shared by:** [42 Labs](https://www.42labs.ai)
- **Model type:** Auto-regressive transformer
- **Language(s) (NLP):** Swedish and English
- **License:** Gemma terms of use
- **Finetuned from model:** [Gemma 2B, 1.1 instruct](https://huggingface.co/google/gemma-1.1-2b-it)
## How to Get Started with the Model
```python
import torch
from transformers import pipeline
from transformers import TextStreamer
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model_name = 'four-two-labs/lynx-micro'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map='cuda',
torch_dtype=torch.bfloat16,
use_flash_attention_2=True, # Remove if flash attention isn't available
)
pipe = pipeline(
'text-generation',
model=model,
tokenizer=tokenizer,
streamer=TextStreamer(tokenizer=tokenizer)
)
messages = [
#{'role': 'user', 'content': 'Lös ekvationen 2x^2-5 = 9'},
#{'role': 'user', 'content': 'Vad är fel med denna mening: "Hej! Jag idag bra mår."'},
#{'role': 'user', 'content': """Översätt till svenska: Hayashi, the Japanese government spokesperson, said Monday that Tokyo is answering the Chinese presence around the islands with vessels of its own.\n\n“We ensure a comprehensive security system for territorial waters by deploying Coast Guard patrol vessels that are consistently superior to other party’s capacity,” Hayashi said.\n\nAny Japanese-Chinese incident in the Senkakus raises the risk of a wider conflict, analysts note, due to Japan’s mutual defense treaty with the United States.\n\nWashington has made clear on numerous occasions that it considers the Senkakus to be covered by the mutual defense pact."""},
#{'role': 'user', 'content': """Vad handlar texten om?\n\nHayashi, the Japanese government spokesperson, said Monday that Tokyo is answering the Chinese presence around the islands with vessels of its own.\n\n“We ensure a comprehensive security system for territorial waters by deploying Coast Guard patrol vessels that are consistently superior to other party’s capacity,” Hayashi said.\n\nAny Japanese-Chinese incident in the Senkakus raises the risk of a wider conflict, analysts note, due to Japan’s mutual defense treaty with the United States.\n\nWashington has made clear on numerous occasions that it considers the Senkakus to be covered by the mutual defense pact."""},
#{'role': 'user', 'content': """Skriv en sci-fi novell som utspelar sig över millenium på en planet runt ett binärt stjärnsystem."""},
{'role': 'user', 'content': 'Hur många helikoptrar kan en människa äta på en gång?'},
]
r = pipe(
messages,
max_length=4096,
do_sample=False,
eos_token_id=[tokenizer.vocab['<end_of_turn>'], tokenizer.eos_token_id],
)
```
## Training Details
### Training Data
The model has been trained on a proprietary dataset of ~1.35M examples consisting of
* High quality swedish instruct data
* Single turn
* Multi-turn
* High quality swe <-> eng translations
### Training Procedure
For training we used hugginface Accelerate and TRL.
#### Preprocessing
For efficiency, we packed all the examples into 8K context windows, reducing the number examples to ~12% of their original count.
#### Training Hyperparameters
- **Training regime:**
[More Information Needed]
## Evaluation
The model has been evaluated on [Scandeval](https://scandeval.com/swedish-nlg/) swedish subset.
The result of the individual metrics compared to other top scoring models
The mean score of all metrics compared to other models in the Swedish NLG category.
## Environmental Impact
- **Hardware Type:** 8xH100
- **Hours used:** ~96 GPU hours
- **Cloud Provider:** runpod.io
- **Compute Region:** Canada
- **Carbon Emitted:** Minimal
| null |
Non_BioNLP
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
lynx-micro - AWQ
- Model creator: https://huggingface.co/four-two-labs/
- Original model: https://huggingface.co/four-two-labs/lynx-micro/
Original model description:
---
library_name: transformers
language:
- sv
- en
---
# Lynx 2B (micro)
## Model Details
### Model Description
This is the first release of a series of Swedish large language models we call "Lynx". Micro is a small model (2 billion params), but punches way above its weight!
Lynx micro is a fine-tune of Google DeepMind Gemma 2B, scores just below GPT-3.5 Turbo on [Scandeval](https://scandeval.com/swedish-nlg/). In fact, the only non OpenAI model (currently) topping the Swedish NLG board on scandeval is a fine-tune of Llama-3 by AI Sweden based on our data recipe.
We believe that this is a really capable model (for its size), but keep in mind that it is still a small model and hasn't memorized as much as larger models tend to do.
- **Funded, Developed and shared by:** [42 Labs](https://www.42labs.ai)
- **Model type:** Auto-regressive transformer
- **Language(s) (NLP):** Swedish and English
- **License:** Gemma terms of use
- **Finetuned from model:** [Gemma 2B, 1.1 instruct](https://huggingface.co/google/gemma-1.1-2b-it)
## How to Get Started with the Model
```python
import torch
from transformers import pipeline
from transformers import TextStreamer
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
model_name = 'four-two-labs/lynx-micro'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map='cuda',
torch_dtype=torch.bfloat16,
use_flash_attention_2=True, # Remove if flash attention isn't available
)
pipe = pipeline(
'text-generation',
model=model,
tokenizer=tokenizer,
streamer=TextStreamer(tokenizer=tokenizer)
)
messages = [
#{'role': 'user', 'content': 'Lös ekvationen 2x^2-5 = 9'},
#{'role': 'user', 'content': 'Vad är fel med denna mening: "Hej! Jag idag bra mår."'},
#{'role': 'user', 'content': """Översätt till svenska: Hayashi, the Japanese government spokesperson, said Monday that Tokyo is answering the Chinese presence around the islands with vessels of its own.\n\n“We ensure a comprehensive security system for territorial waters by deploying Coast Guard patrol vessels that are consistently superior to other party’s capacity,” Hayashi said.\n\nAny Japanese-Chinese incident in the Senkakus raises the risk of a wider conflict, analysts note, due to Japan’s mutual defense treaty with the United States.\n\nWashington has made clear on numerous occasions that it considers the Senkakus to be covered by the mutual defense pact."""},
#{'role': 'user', 'content': """Vad handlar texten om?\n\nHayashi, the Japanese government spokesperson, said Monday that Tokyo is answering the Chinese presence around the islands with vessels of its own.\n\n“We ensure a comprehensive security system for territorial waters by deploying Coast Guard patrol vessels that are consistently superior to other party’s capacity,” Hayashi said.\n\nAny Japanese-Chinese incident in the Senkakus raises the risk of a wider conflict, analysts note, due to Japan’s mutual defense treaty with the United States.\n\nWashington has made clear on numerous occasions that it considers the Senkakus to be covered by the mutual defense pact."""},
#{'role': 'user', 'content': """Skriv en sci-fi novell som utspelar sig över millenium på en planet runt ett binärt stjärnsystem."""},
{'role': 'user', 'content': 'Hur många helikoptrar kan en människa äta på en gång?'},
]
r = pipe(
messages,
max_length=4096,
do_sample=False,
eos_token_id=[tokenizer.vocab['<end_of_turn>'], tokenizer.eos_token_id],
)
```
## Training Details
### Training Data
The model has been trained on a proprietary dataset of ~1.35M examples consisting of
* High quality swedish instruct data
* Single turn
* Multi-turn
* High quality swe <-> eng translations
### Training Procedure
For training we used hugginface Accelerate and TRL.
#### Preprocessing
For efficiency, we packed all the examples into 8K context windows, reducing the number examples to ~12% of their original count.
#### Training Hyperparameters
- **Training regime:**
[More Information Needed]
## Evaluation
The model has been evaluated on [Scandeval](https://scandeval.com/swedish-nlg/) swedish subset.
The result of the individual metrics compared to other top scoring models
The mean score of all metrics compared to other models in the Swedish NLG category.
## Environmental Impact
- **Hardware Type:** 8xH100
- **Hours used:** ~96 GPU hours
- **Cloud Provider:** runpod.io
- **Compute Region:** Canada
- **Carbon Emitted:** Minimal
|
{}
|
task
|
[
"TRANSLATION"
] | 41,993 |
mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF
|
mradermacher
| null |
[
"transformers",
"gguf",
"ko",
"en",
"jp",
"cn",
"dataset:Saxo/ko_cn_translation_tech_social_science_linkbricks_single_dataset",
"dataset:Saxo/ko_jp_translation_tech_social_science_linkbricks_single_dataset",
"dataset:Saxo/en_ko_translation_tech_science_linkbricks_single_dataset_with_prompt_text_huggingface",
"dataset:Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface",
"dataset:Saxo/ko_aspect_sentiment_sns_mall_sentiment_linkbricks_single_dataset_with_prompt_text_huggingface",
"dataset:Saxo/ko_summarization_linkbricks_single_dataset_with_prompt_text_huggingface",
"dataset:Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface",
"dataset:Saxo/ko_government_qa_total_linkbricks_single_dataset_with_prompt_text_huggingface_sampled",
"dataset:Saxo/ko-news-corpus-1",
"dataset:Saxo/ko-news-corpus-2",
"dataset:Saxo/ko-news-corpus-3",
"dataset:Saxo/ko-news-corpus-4",
"dataset:Saxo/ko-news-corpus-5",
"dataset:Saxo/ko-news-corpus-6",
"dataset:Saxo/ko-news-corpus-7",
"dataset:Saxo/ko-news-corpus-8",
"dataset:Saxo/ko-news-corpus-9",
"dataset:maywell/ko_Ultrafeedback_binarized",
"dataset:youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo",
"dataset:lilacai/glaive-function-calling-v2-sharegpt",
"dataset:kuotient/gsm8k-ko",
"base_model:Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B",
"base_model:quantized:Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | 2025-01-03T08:00:37Z |
2025-01-03T11:07:25+00:00
| 79 | 0 |
---
base_model: Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B
datasets:
- Saxo/ko_cn_translation_tech_social_science_linkbricks_single_dataset
- Saxo/ko_jp_translation_tech_social_science_linkbricks_single_dataset
- Saxo/en_ko_translation_tech_science_linkbricks_single_dataset_with_prompt_text_huggingface
- Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface
- Saxo/ko_aspect_sentiment_sns_mall_sentiment_linkbricks_single_dataset_with_prompt_text_huggingface
- Saxo/ko_summarization_linkbricks_single_dataset_with_prompt_text_huggingface
- Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface
- Saxo/ko_government_qa_total_linkbricks_single_dataset_with_prompt_text_huggingface_sampled
- Saxo/ko-news-corpus-1
- Saxo/ko-news-corpus-2
- Saxo/ko-news-corpus-3
- Saxo/ko-news-corpus-4
- Saxo/ko-news-corpus-5
- Saxo/ko-news-corpus-6
- Saxo/ko-news-corpus-7
- Saxo/ko-news-corpus-8
- Saxo/ko-news-corpus-9
- maywell/ko_Ultrafeedback_binarized
- youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo
- lilacai/glaive-function-calling-v2-sharegpt
- kuotient/gsm8k-ko
language:
- ko
- en
- jp
- cn
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
| null |
Non_BioNLP
|
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Linkbricks-Horizon-AI-Japanese-Base-32B-GGUF/resolve/main/Linkbricks-Horizon-AI-Japanese-Base-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
{"base_model": "Saxo/Linkbricks-Horizon-AI-Japanese-Base-32B", "datasets": ["Saxo/ko_cn_translation_tech_social_science_linkbricks_single_dataset", "Saxo/ko_jp_translation_tech_social_science_linkbricks_single_dataset", "Saxo/en_ko_translation_tech_science_linkbricks_single_dataset_with_prompt_text_huggingface", "Saxo/en_ko_translation_social_science_linkbricks_single_dataset_with_prompt_text_huggingface", "Saxo/ko_aspect_sentiment_sns_mall_sentiment_linkbricks_single_dataset_with_prompt_text_huggingface", "Saxo/ko_summarization_linkbricks_single_dataset_with_prompt_text_huggingface", "Saxo/OpenOrca_cleaned_kor_linkbricks_single_dataset_with_prompt_text_huggingface", "Saxo/ko_government_qa_total_linkbricks_single_dataset_with_prompt_text_huggingface_sampled", "Saxo/ko-news-corpus-1", "Saxo/ko-news-corpus-2", "Saxo/ko-news-corpus-3", "Saxo/ko-news-corpus-4", "Saxo/ko-news-corpus-5", "Saxo/ko-news-corpus-6", "Saxo/ko-news-corpus-7", "Saxo/ko-news-corpus-8", "Saxo/ko-news-corpus-9", "maywell/ko_Ultrafeedback_binarized", "youjunhyeok/ko-orca-pair-and-ultrafeedback-dpo", "lilacai/glaive-function-calling-v2-sharegpt", "kuotient/gsm8k-ko"], "language": ["ko", "en", "jp", "cn"], "library_name": "transformers", "license": "apache-2.0", "quantized_by": "mradermacher"}
|
task
|
[
"TRANSLATION",
"SUMMARIZATION"
] | 41,994 |
Helsinki-NLP/opus-mt-sv-tum
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"sv",
"tum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T12:06:19+00:00
| 341 | 0 |
---
license: apache-2.0
tags:
- translation
---
### opus-mt-sv-tum
* source languages: sv
* target languages: tum
* OPUS readme: [sv-tum](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-tum/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.tum | 22.0 | 0.475 |
| null |
Non_BioNLP
|
### opus-mt-sv-tum
* source languages: sv
* target languages: tum
* OPUS readme: [sv-tum](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-tum/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-tum/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.tum | 22.0 | 0.475 |
|
{"license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,995 |
Helsinki-NLP/opus-mt-lue-en
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"lue",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T12:00:34+00:00
| 37 | 0 |
---
license: apache-2.0
tags:
- translation
---
### opus-mt-lue-en
* source languages: lue
* target languages: en
* OPUS readme: [lue-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.lue.en | 31.7 | 0.469 |
| null |
Non_BioNLP
|
### opus-mt-lue-en
* source languages: lue
* target languages: en
* OPUS readme: [lue-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-en/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.lue.en | 31.7 | 0.469 |
|
{"license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,996 |
Helsinki-NLP/opus-mt-ts-fr
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"ts",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T12:07:36+00:00
| 30 | 0 |
---
license: apache-2.0
tags:
- translation
---
### opus-mt-ts-fr
* source languages: ts
* target languages: fr
* OPUS readme: [ts-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ts-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ts-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ts.fr | 29.9 | 0.475 |
| null |
Non_BioNLP
|
### opus-mt-ts-fr
* source languages: ts
* target languages: fr
* OPUS readme: [ts-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ts-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ts-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ts-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ts.fr | 29.9 | 0.475 |
|
{"license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 41,997 |
anna052023/distilbert-base-uncased-finetuned-emotion
|
anna052023
|
text-classification
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-10T22:01:32Z |
2023-09-11T09:35:27+00:00
| 7 | 0 |
---
base_model: distilbert-base-uncased
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.9305
name: Accuracy
- type: f1
value: 0.9309807217925322
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1658
- Accuracy: 0.9305
- F1: 0.9310
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2094 | 1.0 | 250 | 0.1792 | 0.931 | 0.9318 |
| 0.1379 | 2.0 | 500 | 0.1658 | 0.9305 | 0.9310 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1658
- Accuracy: 0.9305
- F1: 0.9310
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2094 | 1.0 | 250 | 0.1792 | 0.931 | 0.9318 |
| 0.1379 | 2.0 | 500 | 0.1658 | 0.9305 | 0.9310 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
{"base_model": "distilbert-base-uncased", "datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9305, "name": "Accuracy"}, {"type": "f1", "value": 0.9309807217925322, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 41,999 |
Diaxil/Cardiotensive
|
Diaxil
| null |
[
"region:us"
] | 2024-12-12T10:48:30Z |
2024-12-12T10:49:29+00:00
| 0 | 0 |
---
{}
---
<p><a href="https://www.healthnutra.org/hu/cardiotensive/"><strong>Cardiotensive</strong></a> <span style="font-weight: 400;">A magas vérfeszültség vagy magas vérnyomás nemzetközileg kiterjedt fitnesz téma. Gyakran kimarad, mert nem ad azonnali jeleket, mégis továbbra is a szív- és érrendszeri megbetegedések fő indítéka marad, beleértve a szívkoszorúér-rohamokat, a stroke-ot és a veseelégtelenséget. A vérnyomás kezelése alapvető fontosságú az általános egészség megőrzéséhez, és különféle gyógyszerekre és étrend-kiegészítőkre van szükség annak megváltoztatására.</span></p>
<p><span style="font-weight: 400;"><br /><strong>彡 Elérhetőség és ár - </strong><a href="https://www.healthnutra.org/hu/cardiotensive/"><span style="font-weight: 400;">https://www.healthnutra.org/hu/cardiotensive/</span></a></span></p>
<div id="tw-target-text-container" class="tw-ta-container F0azHf tw-nfl" tabindex="0">
<pre id="tw-target-text" class="tw-data-text tw-text-large tw-ta" dir="ltr" data-placeholder="Translation" data-ved="2ahUKEwi34Lm-46GKAxVGFVkFHSj7AJwQ3ewLegQICRAU"><strong><span class="Y2IQFc" lang="hu">Olvasson tovább</span></strong></pre>
</div>
<div id="tw-target-rmn-container" class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"> </div>
<div id="tw-target-rmn-container" class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"> <a href="https://startupcentrum.com/tech-center/cardiotensive">https://startupcentrum.com/tech-center/cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://startupcentrum.com/tech-center/cardiotensive-1">https://startupcentrum.com/tech-center/cardiotensive-1</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://teeshopper.in/store/Cardiotensive">https://teeshopper.in/store/Cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://teeshopper.in/store/Cardiotensive-Hungary">https://teeshopper.in/store/Cardiotensive-Hungary</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://twibbo.nz/u/cardiotensivekapszulak">https://twibbo.nz/u/cardiotensivekapszulak</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://www.twibbonize.com/cardiotensive">https://www.twibbonize.com/cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://cardiotensive.quora.com/">https://cardiotensive.quora.com/</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://cardiotensivehungary.quora.com/">https://cardiotensivehungary.quora.com/</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://cardiotensive.company.site/">https://cardiotensive.company.site/</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://lookerstudio.google.com/s/n4PlED5TU0A">https://lookerstudio.google.com/s/n4PlED5TU0A</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://lookerstudio.google.com/s/hYXEmRVu86g">https://lookerstudio.google.com/s/hYXEmRVu86g</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://view.genially.com/675a8c835a67056c19ccbc04/video-presentation-cardiotensive">https://view.genially.com/675a8c835a67056c19ccbc04/video-presentation-cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"> </div>
| null |
Non_BioNLP
|
<p><a href="https://www.healthnutra.org/hu/cardiotensive/"><strong>Cardiotensive</strong></a> <span style="font-weight: 400;">A magas vérfeszültség vagy magas vérnyomás nemzetközileg kiterjedt fitnesz téma. Gyakran kimarad, mert nem ad azonnali jeleket, mégis továbbra is a szív- és érrendszeri megbetegedések fő indítéka marad, beleértve a szívkoszorúér-rohamokat, a stroke-ot és a veseelégtelenséget. A vérnyomás kezelése alapvető fontosságú az általános egészség megőrzéséhez, és különféle gyógyszerekre és étrend-kiegészítőkre van szükség annak megváltoztatására.</span></p>
<p><span style="font-weight: 400;"><br /><strong>彡 Elérhetőség és ár - </strong><a href="https://www.healthnutra.org/hu/cardiotensive/"><span style="font-weight: 400;">https://www.healthnutra.org/hu/cardiotensive/</span></a></span></p>
<div id="tw-target-text-container" class="tw-ta-container F0azHf tw-nfl" tabindex="0">
<pre id="tw-target-text" class="tw-data-text tw-text-large tw-ta" dir="ltr" data-placeholder="Translation" data-ved="2ahUKEwi34Lm-46GKAxVGFVkFHSj7AJwQ3ewLegQICRAU"><strong><span class="Y2IQFc" lang="hu">Olvasson tovább</span></strong></pre>
</div>
<div id="tw-target-rmn-container" class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"> </div>
<div id="tw-target-rmn-container" class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"> <a href="https://startupcentrum.com/tech-center/cardiotensive">https://startupcentrum.com/tech-center/cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://startupcentrum.com/tech-center/cardiotensive-1">https://startupcentrum.com/tech-center/cardiotensive-1</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://teeshopper.in/store/Cardiotensive">https://teeshopper.in/store/Cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://teeshopper.in/store/Cardiotensive-Hungary">https://teeshopper.in/store/Cardiotensive-Hungary</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://twibbo.nz/u/cardiotensivekapszulak">https://twibbo.nz/u/cardiotensivekapszulak</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://www.twibbonize.com/cardiotensive">https://www.twibbonize.com/cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://cardiotensive.quora.com/">https://cardiotensive.quora.com/</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://cardiotensivehungary.quora.com/">https://cardiotensivehungary.quora.com/</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://cardiotensive.company.site/">https://cardiotensive.company.site/</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://lookerstudio.google.com/s/n4PlED5TU0A">https://lookerstudio.google.com/s/n4PlED5TU0A</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://lookerstudio.google.com/s/hYXEmRVu86g">https://lookerstudio.google.com/s/hYXEmRVu86g</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"><br /><a href="https://view.genially.com/675a8c835a67056c19ccbc04/video-presentation-cardiotensive">https://view.genially.com/675a8c835a67056c19ccbc04/video-presentation-cardiotensive</a></div>
<div class="tw-target-rmn tw-ta-container F0azHf tw-nfl" tabindex="0"> </div>
|
{}
|
task
|
[
"TRANSLATION"
] | 42,000 |
Draxgabe/text-summarization-model
|
Draxgabe
|
text2text-generation
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2025-02-19T18:02:08Z |
2025-02-20T01:15:48+00:00
| 36 | 0 |
---
base_model: t5-small
library_name: transformers
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: text-summarization-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text-summarization-model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.9764 | 1.0 | 71779 | 0.9822 |
| 0.9702 | 2.0 | 143558 | 0.9729 |
| 0.9512 | 3.0 | 215337 | 0.9696 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text-summarization-model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.9764 | 1.0 | 71779 | 0.9822 |
| 0.9702 | 2.0 | 143558 | 0.9729 |
| 0.9512 | 3.0 | 215337 | 0.9696 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
|
{"base_model": "t5-small", "library_name": "transformers", "license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "text-summarization-model", "results": []}]}
|
task
|
[
"SUMMARIZATION"
] | 42,001 |
DMFZ/marian-finetuned-kde4-en-to-fr
|
DMFZ
|
translation
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-07-28T08:29:52Z |
2024-07-28T16:09:58+00:00
| 9 | 0 |
---
base_model: Helsinki-NLP/opus-mt-en-fr
datasets:
- kde4
license: apache-2.0
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- type: bleu
value: 52.91210143343284
name: Bleu
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8554
- Bleu: 52.9121
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8554
- Bleu: 52.9121
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
{"base_model": "Helsinki-NLP/opus-mt-en-fr", "datasets": ["kde4"], "license": "apache-2.0", "metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "config": "en-fr", "split": "train", "args": "en-fr"}, "metrics": [{"type": "bleu", "value": 52.91210143343284, "name": "Bleu"}]}]}]}
|
task
|
[
"TRANSLATION"
] | 42,003 |
prashanth/IndicBART-ibart-en-to-hi
|
prashanth
|
text2text-generation
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:hindi_english_machine_translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-06-07T09:41:24Z |
2022-06-07T09:45:31+00:00
| 13 | 0 |
---
datasets:
- hindi_english_machine_translation
tags:
- generated_from_trainer
model-index:
- name: IndicBART-ibart-en-to-hi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IndicBART-ibart-en-to-hi
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the hindi_english_machine_translation dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 157 | 4.7112 | 0.8663 | 20.0 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IndicBART-ibart-en-to-hi
This model is a fine-tuned version of [ai4bharat/IndicBART](https://huggingface.co/ai4bharat/IndicBART) on the hindi_english_machine_translation dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 157 | 4.7112 | 0.8663 | 20.0 |
### Framework versions
- Transformers 4.19.1
- Pytorch 1.11.0+cu102
- Datasets 1.18.0
- Tokenizers 0.12.1
|
{"datasets": ["hindi_english_machine_translation"], "tags": ["generated_from_trainer"], "model-index": [{"name": "IndicBART-ibart-en-to-hi", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 42,004 |
pridaj/distilbert-base-uncased-finetuned-clinc
|
pridaj
|
text-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-03-09T15:10:51Z |
2023-03-09T15:16:47+00:00
| 13 | 0 |
---
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- type: accuracy
value: 0.9183870967741935
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7432 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 |
| 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2890 | 0.7432 |
| 3.7868 | 2.0 | 636 | 1.8756 | 0.8377 |
| 3.7868 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.6929 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.9058 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
{"datasets": ["clinc_oos"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "config": "plus", "split": "validation", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9183870967741935, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,005 |
almanach/camemberta-base-generator
|
almanach
|
fill-mask
|
[
"transformers",
"tf",
"tensorboard",
"deberta-v2",
"fill-mask",
"deberta",
"deberta-v3",
"fr",
"dataset:ccnet",
"license:mit",
"autotrain_compatible",
"region:us"
] | 2023-05-03T09:52:42Z |
2023-06-12T15:29:24+00:00
| 8 | 2 |
---
datasets:
- ccnet
language: fr
license: mit
tags:
- deberta
- deberta-v3
inference: false
---
# CamemBERTa: A French language model based on DeBERTa V3
CamemBERTa, a French language model based on DeBERTa V3, which is a DeBerta V2 with ELECTRA style pretraining using the Replaced Token Detection (RTD) objective.
RTD uses a generator model, trained using the MLM objective, to replace masked tokens with plausible candidates, and a discriminator model trained to detect which tokens were replaced by the generator.
Usually the generator and discriminator share the same embedding matrix, but the authors of DeBERTa V3 propose a new technique to disentagle the gradients of the shared embedding between the generator and discriminator called gradient-disentangled embedding sharing (GDES)
*This the first publicly available implementation of DeBERTa V3, and the first publicly DeBERTaV3 model outside of the original Microsoft release.*
Preprint Paper: https://inria.hal.science/hal-03963729/
Pre-training Code: https://gitlab.inria.fr/almanach/CamemBERTa
## How to use CamemBERTa
Our pretrained weights are available on the HuggingFace model hub, you can load them using the following code:
```python
from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM
CamemBERTa = AutoModel.from_pretrained("almanach/camemberta-base")
tokenizer = AutoTokenizer.from_pretrained("almanach/camemberta-base")
CamemBERTa_gen = AutoModelForMaskedLM.from_pretrained("almanach/camemberta-base-generator")
tokenizer_gen = AutoTokenizer.from_pretrained("almanach/camemberta-base-generator")
```
We also include the TF2 weights including the weights for the model's RTD head for the discriminator, and the MLM head for the generator.
CamemBERTa is compatible with most finetuning scripts from the transformers library.
## Pretraining Setup
The model was trained on the French subset of the CCNet corpus (the same subset used in CamemBERT and PaGNOL) and is available on the HuggingFace model hub: CamemBERTa and CamemBERTa Generator.
To speed up the pre-training experiments, the pre-training was split into two phases;
in phase 1, the model is trained with a maximum sequence length of 128 tokens for 10,000 steps with 2,000 warm-up steps and a very large batch size of 67,584.
In phase 2, maximum sequence length is increased to the full model capacity of 512 tokens for 3,300 steps with 200 warm-up steps and a batch size of 27,648.
The model would have seen 133B tokens compared to 419B tokens for CamemBERT-CCNet which was trained for 100K steps, this represents roughly 30% of CamemBERT’s full training.
To have a fair comparison, we trained a RoBERTa model, CamemBERT30%, using the same exact pretraining setup but with the MLM objective.
## Pretraining Loss Curves
check the tensorboard logs and plots
## Fine-tuning results
Datasets: POS tagging and Dependency Parsing (GSD, Rhapsodie, Sequoia, FSMB), NER (FTB), the FLUE benchmark (XNLI, CLS, PAWS-X), and the French Question Answering Dataset (FQuAD)
| Model | UPOS | LAS | NER | CLS | PAWS-X | XNLI | F1 (FQuAD) | EM (FQuAD) |
|-------------------|-----------|-----------|-----------|-----------|-----------|-----------|------------|------------|
| CamemBERT (CCNet) | **97.59** | **88.69** | 89.97 | 94.62 | 91.36 | 81.95 | 80.98 | **62.51** |
| CamemBERT (30%) | 97.53 | 87.98 | **91.04** | 93.28 | 88.94 | 79.89 | 75.14 | 56.19 |
| CamemBERTa | 97.57 | 88.55 | 90.33 | **94.92** | **91.67** | **82.00** | **81.15** | 62.01 |
The following table compares CamemBERTa's performance on XNLI against other models under different training setups, which demonstrates the data efficiency of CamemBERTa.
| Model | XNLI (Acc.) | Training Steps | Tokens seen in pre-training | Dataset Size in Tokens |
|-------------------|-------------|----------------|-----------------------------|------------------------|
| mDeBERTa | 84.4 | 500k | 2T | 2.5T |
| CamemBERTa | 82.0 | 33k | 0.139T | 0.319T |
| XLM-R | 81.4 | 1.5M | 6T | 2.5T |
| CamemBERT - CCNet | 81.95 | 100k | 0.419T | 0.319T |
*Note: The CamemBERTa training steps was adjusted for a batch size of 8192.*
## License
The public model weights are licensed under MIT License.
This code is licensed under the Apache License 2.0.
## Citation
Paper accepted to Findings of ACL 2023.
You can use the preprint citation for now
```
@article{antoun2023camemberta
TITLE = {{Data-Efficient French Language Modeling with CamemBERTa}},
AUTHOR = {Antoun, Wissam and Sagot, Beno{\^i}t and Seddah, Djam{\'e}},
URL = {https://inria.hal.science/hal-03963729},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Jan,
PDF = {https://inria.hal.science/hal-03963729/file/French_DeBERTa___ACL_2023%20to%20be%20uploaded.pdf},
HAL_ID = {hal-03963729},
HAL_VERSION = {v1},
}
```
## Contact
Wissam Antoun: `wissam (dot) antoun (at) inria (dot) fr`
Benoit Sagot: `benoit (dot) sagot (at) inria (dot) fr`
Djame Seddah: `djame (dot) seddah (at) inria (dot) fr`
| null |
Non_BioNLP
|
# CamemBERTa: A French language model based on DeBERTa V3
CamemBERTa, a French language model based on DeBERTa V3, which is a DeBerta V2 with ELECTRA style pretraining using the Replaced Token Detection (RTD) objective.
RTD uses a generator model, trained using the MLM objective, to replace masked tokens with plausible candidates, and a discriminator model trained to detect which tokens were replaced by the generator.
Usually the generator and discriminator share the same embedding matrix, but the authors of DeBERTa V3 propose a new technique to disentagle the gradients of the shared embedding between the generator and discriminator called gradient-disentangled embedding sharing (GDES)
*This the first publicly available implementation of DeBERTa V3, and the first publicly DeBERTaV3 model outside of the original Microsoft release.*
Preprint Paper: https://inria.hal.science/hal-03963729/
Pre-training Code: https://gitlab.inria.fr/almanach/CamemBERTa
## How to use CamemBERTa
Our pretrained weights are available on the HuggingFace model hub, you can load them using the following code:
```python
from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM
CamemBERTa = AutoModel.from_pretrained("almanach/camemberta-base")
tokenizer = AutoTokenizer.from_pretrained("almanach/camemberta-base")
CamemBERTa_gen = AutoModelForMaskedLM.from_pretrained("almanach/camemberta-base-generator")
tokenizer_gen = AutoTokenizer.from_pretrained("almanach/camemberta-base-generator")
```
We also include the TF2 weights including the weights for the model's RTD head for the discriminator, and the MLM head for the generator.
CamemBERTa is compatible with most finetuning scripts from the transformers library.
## Pretraining Setup
The model was trained on the French subset of the CCNet corpus (the same subset used in CamemBERT and PaGNOL) and is available on the HuggingFace model hub: CamemBERTa and CamemBERTa Generator.
To speed up the pre-training experiments, the pre-training was split into two phases;
in phase 1, the model is trained with a maximum sequence length of 128 tokens for 10,000 steps with 2,000 warm-up steps and a very large batch size of 67,584.
In phase 2, maximum sequence length is increased to the full model capacity of 512 tokens for 3,300 steps with 200 warm-up steps and a batch size of 27,648.
The model would have seen 133B tokens compared to 419B tokens for CamemBERT-CCNet which was trained for 100K steps, this represents roughly 30% of CamemBERT’s full training.
To have a fair comparison, we trained a RoBERTa model, CamemBERT30%, using the same exact pretraining setup but with the MLM objective.
## Pretraining Loss Curves
check the tensorboard logs and plots
## Fine-tuning results
Datasets: POS tagging and Dependency Parsing (GSD, Rhapsodie, Sequoia, FSMB), NER (FTB), the FLUE benchmark (XNLI, CLS, PAWS-X), and the French Question Answering Dataset (FQuAD)
| Model | UPOS | LAS | NER | CLS | PAWS-X | XNLI | F1 (FQuAD) | EM (FQuAD) |
|-------------------|-----------|-----------|-----------|-----------|-----------|-----------|------------|------------|
| CamemBERT (CCNet) | **97.59** | **88.69** | 89.97 | 94.62 | 91.36 | 81.95 | 80.98 | **62.51** |
| CamemBERT (30%) | 97.53 | 87.98 | **91.04** | 93.28 | 88.94 | 79.89 | 75.14 | 56.19 |
| CamemBERTa | 97.57 | 88.55 | 90.33 | **94.92** | **91.67** | **82.00** | **81.15** | 62.01 |
The following table compares CamemBERTa's performance on XNLI against other models under different training setups, which demonstrates the data efficiency of CamemBERTa.
| Model | XNLI (Acc.) | Training Steps | Tokens seen in pre-training | Dataset Size in Tokens |
|-------------------|-------------|----------------|-----------------------------|------------------------|
| mDeBERTa | 84.4 | 500k | 2T | 2.5T |
| CamemBERTa | 82.0 | 33k | 0.139T | 0.319T |
| XLM-R | 81.4 | 1.5M | 6T | 2.5T |
| CamemBERT - CCNet | 81.95 | 100k | 0.419T | 0.319T |
*Note: The CamemBERTa training steps was adjusted for a batch size of 8192.*
## License
The public model weights are licensed under MIT License.
This code is licensed under the Apache License 2.0.
## Citation
Paper accepted to Findings of ACL 2023.
You can use the preprint citation for now
```
@article{antoun2023camemberta
TITLE = {{Data-Efficient French Language Modeling with CamemBERTa}},
AUTHOR = {Antoun, Wissam and Sagot, Beno{\^i}t and Seddah, Djam{\'e}},
URL = {https://inria.hal.science/hal-03963729},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Jan,
PDF = {https://inria.hal.science/hal-03963729/file/French_DeBERTa___ACL_2023%20to%20be%20uploaded.pdf},
HAL_ID = {hal-03963729},
HAL_VERSION = {v1},
}
```
## Contact
Wissam Antoun: `wissam (dot) antoun (at) inria (dot) fr`
Benoit Sagot: `benoit (dot) sagot (at) inria (dot) fr`
Djame Seddah: `djame (dot) seddah (at) inria (dot) fr`
|
{"datasets": ["ccnet"], "language": "fr", "license": "mit", "tags": ["deberta", "deberta-v3"], "inference": false}
|
task
|
[
"QUESTION_ANSWERING"
] | 42,006 |
DrishtiSharma/llama-pro-8b-english-to-hinglish-translation
|
DrishtiSharma
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TencentARC/LLaMA-Pro-8B",
"base_model:adapter:TencentARC/LLaMA-Pro-8B",
"license:llama2",
"region:us"
] | 2024-02-08T12:06:06Z |
2024-02-08T12:06:29+00:00
| 0 | 0 |
---
base_model: TencentARC/LLaMA-Pro-8B
library_name: peft
license: llama2
tags:
- generated_from_trainer
model-index:
- name: llama-pro-8b-english-to-hinglish-translation
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-pro-8b-english-to-hinglish-translation
This model is a fine-tuned version of [TencentARC/LLaMA-Pro-8B](https://huggingface.co/TencentARC/LLaMA-Pro-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7581
- Rouge Scores: {'rouge1': 0.920395098520514, 'rouge2': 0.8260982220795346, 'rougeL': 0.8629699886603178, 'rougeLsum': 0.9203938314651259}
- Bleu Scores: [0.0799874668395633, 0.07835910254936027, 0.07659241243282754, 0.0747653244473694]
- Gen Len: 2048.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge Scores | Bleu Scores | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------:|:-------:|
| 0.8367 | 1.0 | 500 | 0.7705 | {'rouge1': 0.9217534575496169, 'rouge2': 0.826045962480547, 'rougeL': 0.861202510859852, 'rougeLsum': 0.9217216485625217} | [0.07995984189186581, 0.07829715249738517, 0.07650957414005091, 0.07466840050021681] | 2048.0 |
| 0.581 | 2.0 | 1000 | 0.7581 | {'rouge1': 0.920395098520514, 'rouge2': 0.8260982220795346, 'rougeL': 0.8629699886603178, 'rougeLsum': 0.9203938314651259} | [0.0799874668395633, 0.07835910254936027, 0.07659241243282754, 0.0747653244473694] | 2048.0 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-pro-8b-english-to-hinglish-translation
This model is a fine-tuned version of [TencentARC/LLaMA-Pro-8B](https://huggingface.co/TencentARC/LLaMA-Pro-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7581
- Rouge Scores: {'rouge1': 0.920395098520514, 'rouge2': 0.8260982220795346, 'rougeL': 0.8629699886603178, 'rougeLsum': 0.9203938314651259}
- Bleu Scores: [0.0799874668395633, 0.07835910254936027, 0.07659241243282754, 0.0747653244473694]
- Gen Len: 2048.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge Scores | Bleu Scores | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------:|:-------:|
| 0.8367 | 1.0 | 500 | 0.7705 | {'rouge1': 0.9217534575496169, 'rouge2': 0.826045962480547, 'rougeL': 0.861202510859852, 'rougeLsum': 0.9217216485625217} | [0.07995984189186581, 0.07829715249738517, 0.07650957414005091, 0.07466840050021681] | 2048.0 |
| 0.581 | 2.0 | 1000 | 0.7581 | {'rouge1': 0.920395098520514, 'rouge2': 0.8260982220795346, 'rougeL': 0.8629699886603178, 'rougeLsum': 0.9203938314651259} | [0.0799874668395633, 0.07835910254936027, 0.07659241243282754, 0.0747653244473694] | 2048.0 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1
|
{"base_model": "TencentARC/LLaMA-Pro-8B", "library_name": "peft", "license": "llama2", "tags": ["generated_from_trainer"], "model-index": [{"name": "llama-pro-8b-english-to-hinglish-translation", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 42,007 |
danielrsn/distilbert-base-uncased-finetuned-clinc
|
danielrsn
| null |
[
"pytorch",
"tensorboard",
"distilbert",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"region:us"
] | 2024-09-29T10:54:49Z |
2024-10-01T08:57:22+00:00
| 7 | 0 |
---
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- type: accuracy
value: 0.9180645161290323
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2895 | 1.0 | 318 | 3.2884 | 0.7419 |
| 2.6277 | 2.0 | 636 | 1.8751 | 0.8368 |
| 1.5479 | 3.0 | 954 | 1.1569 | 0.8961 |
| 1.0148 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.7952 | 5.0 | 1590 | 0.7721 | 0.9181 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.4.1+cu121
- Datasets 1.16.1
- Tokenizers 0.19.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
- Accuracy: 0.9181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2895 | 1.0 | 318 | 3.2884 | 0.7419 |
| 2.6277 | 2.0 | 636 | 1.8751 | 0.8368 |
| 1.5479 | 3.0 | 954 | 1.1569 | 0.8961 |
| 1.0148 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.7952 | 5.0 | 1590 | 0.7721 | 0.9181 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.4.1+cu121
- Datasets 1.16.1
- Tokenizers 0.19.1
|
{"datasets": ["clinc_oos"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos", "args": "plus"}, "metrics": [{"type": "accuracy", "value": 0.9180645161290323, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,008 |
asaakyan/ruRoPEBert-e5-base-512-allRU-authorship
|
asaakyan
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2465773",
"loss:ContrastiveLoss",
"custom_code",
"arxiv:1908.10084",
"base_model:Tochka-AI/ruRoPEBert-e5-base-512",
"base_model:finetune:Tochka-AI/ruRoPEBert-e5-base-512",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-05T20:54:28Z |
2024-12-05T20:54:44+00:00
| 9 | 0 |
---
base_model: Tochka-AI/ruRoPEBert-e5-base-512
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2465773
- loss:ContrastiveLoss
widget:
- source_sentence: 'Вообще-то, цветущая весна здесь началась ещё в начале февраля,
но мне не хотелось дразнить людей теплом слишком задолго до окончания зимы. А
вот сейчас - самое время!
Все фотографии сделаны мной в городском парке или недалеко от города. Проводить
здесь зимние месяцы - одно удовольствие! Очень комфортная погода, много зелени,
а сейчас и цветов - любуйтесь сами :3
<PERSON>. Ну ведь красавица!
Я уже почти месяц жду, когда зацветёт всё дерево, но создаётся впечатление, будто
снизу раскрывшиеся бутоны кто-то обрывает - ещё вчера был, а сегодня нет. Возможно,
они просто опадают, сегодня я как раз подобрала пару штучек.
А этот кустарник цвёл всю зиму. Таких жёлтых кустарников тут несколько видов,
есть и те, которые вкусно пахнут.
А тут я специально ради вас нашла название \- Красный Хеномелес! Или просто японская
айва. Я как-то привыкла к белым и розовым цветочкам на деревьях, а такой насыщенный
красный приятно удивил.
А это цветёт <PERSON>, мне в комментариях подсказали =)
Интересно, что до сих пор осталось немножко осенних листьев (а в декабре я успела
собрать очень красочный гербарий). Такой вот осенне-весенний контраст.
Это жасмин. Кажется. Ну, я краем уха слышала, как тётенька-экскурсовод рассказывала.
А вот это точно знаю - цикломены лесные. Одни из самых ранних цветочков, застилают
лесные поляны - очень мило, с начала февраля цветут.
Подснежники цвели вместе с цикломенами в феврале.
Скоро у нас распустится <PERSON>. Жду-жду *_*
Ну и, конечно же, <PERSON>! Если встать в правильном месте и поймать нужный ветер,
можно услышать прекрасный аромат =)
Если у вас возникает мысль "А не махнуть ли мне в Сочи" - решайтесь! Здесь не
только пустынные пляжи созерцательно море, но и очень приятно гулять среди зелени
и цветов.'
sentences:
- 'Урал радует разнообразием. В лёгкой пешей прогулке между деревеньками и дачами
можно увидеть...
Выработанный мраморный карьер.
Как после хорошего дождя тропинки становятся белыми.
Карьер, как это часто бывает, заполнен водой и служит местом отдыха
С определённых ракурсов вода приобретает очень красивый оттенок.
На такой почве очень хорошо растёт облепиха.
А дорога под ногами блестит и переливается бликами от солнца, но этого я вам не
покажу.
В нескольких дестяках метров от карьера есть небольшой водопад.
Маленький, но симпатичный.
И даже ущелье! Или каньон?
Неподалёку располагается ни много ни мало памятник природы - базальтовые скалы.
Похоже на поджаренную щепку, правда? Только гораааздо больше. И скала.
Вы уже заметили по теням, какой тут стройный хвойный лес?
А ещё тут растут люпины, прямо как в Зеландиях и Исландиях. [Помимо них ещё красиво
цветёт Иван-чай в окрестностях.]
Ну и завершим прогулку красивым закатом, хотя они в любом месте красивы - за это
люблю свою планету.
Конечно, ещё есть река, поля, грибы, лесные озёра и что-то, до чего мы ещё не
прогулялись. Доехать можно хоть на электричке - 47 или 49 км. В окрестных деревнях
тоже есть на что посмотреть.
Красота ближе, чем вы думаете ;)'
- Может люди работают до поздна? Приходят в 22, что им теперь дела домашние не делать?
Явно косяк со слышимостью. Вам мешают - сразу пара долбоебов. У нас раньше такая
херня была - внизу соседи слышали как мы ходим и даже как компьютерное кресло
двигается. Тоже истерики устраивали, мой к ним содил поговорить, позвонил мне
попросил походить. Да, говорит слышно сильно. А знаете в чем прикол? В том что
я не топаю. Я хожу тихо. Тут именно вибрация. Поменяли кресло - специально когда
покупали катали чтобы вообще не было слышно, постелили ковёр. Вроде перестали
на нас жаловаться. С той квартиры мы съехали, сейчас там живет мама молодого человека
моего. Так на нее они гонят за то что стиралку ночью включает! На кухне. Стиралка
ее из тех что идёт на взлёт. При нас не жаловались. Но видимо доебаться надо.
Может и вы такой? Может вам надо доебаться?
- "Позиция нажатого элемента ListView и её получение в запущенном активити\nВсем\
\ добрый день.\nПосле нажатия на элемент списка listview, нужно передать его позицию\
\ (номер элемента списка) в другое активити. Использую position, которая передается\
\ в onListItemClick. Но в другое активити почему то передается null.\nПомогите\
\ разобраться))\n[code]\n public class MainActivity extends ListActivity {\n\
\ @Override\n protected void onListItemClick(ListView l, View v, int position,\
\ long id) {\n super.onListItemClick(l, v, position, id);\n Intent\
\ intent = new Intent(getApplicationContext(), AfterClickActivity.class);\n \
\ intent.putExtra(\"position\",position);\n startActivity(intent);\n\
\ }\n @Override\n protected void onCreate(Bundle savedInstanceState)\
\ {\n Integer[] array = new Integer[100];\n for (int i = 0; i <100\
\ ; i++) {\n array[i] = i;\n }\n super.onCreate(savedInstanceState);\n\
\ ArrayAdapter adapter = new ArrayAdapter(getApplicationContext(),R.layout.list_item,R.id.tvText,array);\n\
\ setListAdapter(adapter);\n[/code]\n}\n[code]\n public class AfterClickActivity\
\ extends ActionBarActivity {\n @Override\n protected void onCreate(Bundle\
\ savedInstanceState) {\n super.onCreate(savedInstanceState);\n \
\ setContentView(R.layout.activity_after_click);\n String position = getIntent().getStringExtra(\"\
position\");\n TextView view = (TextView)findViewById(R.id.tvView1);\n\
\ view.setText(String.valueOf(position));\n }\n[/code]"
- source_sentence: Здравствуйте,попал в не приятную ситуацию. В октябре на МКАД произошло
мелкое ДТП я на грузовой машине легонько притер <PERSON> сам виноват,не соблюдал
дистанцию. За рулём девушка,поговорили оформили евро протокол и разъехались. Протокол
в страховую компанию надо было предоставить в течение пяти дней. Грузовая машина
оформлена на предприятие где я работаю. Через день с механиком поехали в страховую, а
там висит объявление что прием заявлений и протоколов временно приостановлен.
Сколько это "временно "продлилось. Не известно. Сегодня пришло письмо из Росгосстраха
,о том что я вовремя не предоставил соответствующие документы и выплата по ремонту
18800к возлагается на меня. В страховую больше не приезжал. Как быть?
sentences:
- 'Кстати ole72ole я писал уже выше насчёт книги "Пластичность мозга", там в самой
первой главе говорится о женщине которая всё время падала и о методах которыми
её смогли восстановить. Хорошо бы показать эту книгу его родственникам, чтобы
в свою очередь они прочитали и рассказали о методах лечащему врачу. Вот начало
этой главы, мне кажется или у вашего сопалатника похожая ситуация только чуть
всё иначе? Могу скинуть если напишешь свою почту эту книгу, т.к. на пикабу я не
нашёл Личных сообщений, а в свободном доступе (бесплатно) книгу очень тяжело найти.
<PERSON> постоянно кажется, что она падает. И из-за этого непреходящего ощущения
она
действительно не может устоять на ногах.
Когда <PERSON>, не имея точки опоры, поднимается с места, в первые мгновения она
выглядит так,
словно стоит на краю пропасти, в которую вот-вот упадет. Сначала у нее начинает
дрожать и
склоняться на одну сторону голова, и <PERSON> вытягивает руки, пытаясь зафиксировать
свое положение.
После этого ее тело принимается хаотично двигаться вперед и назад, и тогда <PERSON>
напоминает
человека, идущего по туго натянутому канату в тот ужасный момент, когда канатоходец
начинает
терять равновесие. При этом вы видите, что <PERSON>, на самом деле, стоит на твердой,
устойчивой
поверхности (на полу, на земле), широко расставив ноги. Создается впечатление,
что она боится не столько падения, сколько того, что ее толкнут.'
- 'Семья из Екатеринбурга более 10 лет пытается добиться восстановления вентиляции
в квартире
<PERSON>:
Место действия: мск, дом 24 этажа. Я на 9-м. Стал замечать, что нет тяги в вентиляции.
Пришел в диспетчерскую УК (благо два шага от подъезда), написал заявление на проверку/прочистку.
Фабула:
Возвращаюсь часов в 8 вечера, в холле подъезда стихийный митинг из человек 7,
что мол УК совсем охерели и портят имущество. Вслушиваясь в монолог истерящей
дамочки проясняю обстоятельства: разбит холодильник.
Далее подробности от УК.
УК приняв заявку, отправляет работника на крышу проверить вентканал, (короб в
квартире ппц толстый: где то 120*50 см по внешним габаритам, и расположен там,
где многие ввиду нестандартной планировки, хотели бы установить холодильник) решили
прочистить канал привязав обычную 16кг гирю советского производства к тросу начали
проход шахты. На высоте примерно 12-11 этажа нашли "засор" и начали прочистку,
постепенно увеличивая высоту подъёма грузика... Уж не знаю на какую высоту подняли
этот грузик, но он прошел "засор" в виде холодильника до середины морозилки.
Как итог за самовольную врезку в вент канал "пострадавшей" был влуплен штраф и
предписание устранить изменения.
Единственное что частично оправдывает "пострадавшую" - она сделала обвод вокруг
врезанного холодильника из венттрубы 110мм*50мм квадратного сечения, что было
явно недостаточно для тяги в таком стояке.'
- 'Космическое производство кристаллов для лазеров
Ключевой вопрос космического производства стоит так. Что в космосе можно делать
такого, чтобы оно 1) отбивало стоимость вывода на орбиту и возвращения на Землю
груза и 2) оно было массовым, чтобы рынок был достаточно большим?
Когда начинают говорить о 3D-печати живой ткани - то, по моим впечатлениям, говорят
об этом от отчаяния. Да, с научной точки зрения это очень интересно. Но до того,
чтобы что-то такое напечатанное пересаживать живому человеку - мягко говоря ну
вот совсем очень далеко.
Да, можно печатать ткани нескольких органов на одной площадке, связывать их друг
с другом, организовывать обмен веществ, и затем смотреть как какое-нибудь новое
лекарство влияет на "экосистему" тканей (а не на каждый вид ткани в отдельности).
Но это все штучный товар, бизнес на этом не построить.
Что-то более близкое к реальной экономике сделала летом корпорация Redwire.
Redwire вырастила на орбите на борту МКС двухграммовый кристалл дигидрофосфата
калия (их используют для создания мощных лазеров) и даже продала его - Университету
штата Огайо. Компания заявляет, что это первая в истории сделка с товарами, произведенными
в космосе. Вот этот кристалл:
Кристалл, выращенный Redwire на МКС
Первые попытки выращивания кристаллов на орбите были предприняты в 1970-х. На
фото слева - "земные" кристаллы, а справа - кристаллы, выращенные в космосе в
условиях микрогравитации (см. фото ниже).
Профессор <PERSON> приобрел образец, выращенный Redwire за 4000 долларов, что
эквивалентно 2 миллионам долларов за килограмм.
Сейчас сотрудники университетского центра электронной микроскопии и анализа изучают
образец - режут его на тонкие сегменты и изучат его свойства.
Redwire ожидает, что анализ покажет, что выращивание кристалла в условиях микрогравитации
привело к меньшему количеству дефектов и получению более качественного продукта.
https://t.me/IngeniumNotes/622'
- source_sentence: 'Феодосия, 25 декабря. Крыминформ. В Феодосии 67-летний <PERSON>
объявил голодовку и грозится покончить жизнь самоубийством в связи с тем, что
суд не удовлетворил иск о подтверждении факта его постоянного проживания на территории
Крыма, что необходимо для приобретения российского гражданства. Он объявил об
этом сегодня, выйдя к зданию городского суда с табличкой "Голодовка".
"Суд 22 декабря вынес решение, которым не подтверждает факт моего постоянного
проживания в Феодосии. Я прописан в Украине, но живу в городе по одному и тому
же адресу с 2004 года, работал на заводе, принимаю активное участие в жизни города.
Пенсию мне перестали выплачивать, жить не на что, деньги занимаю у соседей", -
сообщил <PERSON>. По его словам, на суде давали показания его соседи, а сам он
представил подтверждающие документы.
Сегодня, как сообщил мужчина, он получил ответ председателя суда <PERSON>, в котором
ему предлагают обратиться в апелляционный суд. Однако, по словам <PERSON>, у него
нет денег даже на оплату госпошлины. "Мне рекомендовали обратиться в апелляционный
суд, но денег на это у меня нет вообще", - сказал он.'
sentences:
- '17-18 января исполнилось два года эпохальному событию: кухонная утварь (кастрюли,
дуршлаги и прочее) напрочь вошла в украинскую политику, а понятие «кастрюлеголовые»
плотно закрепилось за майданствующими особями незалежной.
Напомним, 16 января 2014 года Верховная Рада Украины приняла (а 17 января президент
<PERSON> подписал) пакет законов, которые должны были помочь в борьбе с массовыми
беспорядками. Как-то: запрещалось участие в демонстрациях с использованием средств,
затрудняющих идентификацию личности, присутствие на митинге с открытым огнем,
пиротехникой, оружием, газовыми баллончиками, взрывчаткой и т. п. средствами,
а также в камуфляже, масках и касках. Отметим, что практически все принятые тогда
законы имели прямые аналоги в законодательстве стран ЕС и США. Но Совет Европы
дал им предварительную собственную оценку, по которой многие предлагаемые нормы
почему-то не соответствовали Европейской конвенции по правам человека, рекомендовав
парламенту либо отменить, либо приостановить их действие в течение значительного
периода времени.
Адепты «революции гыдности» сразу же окрестили принятые акты «Законами о диктатуре»
и массово вышли на Майдан с кастрюлями на головах. К слову, 9 из 12-ти законов
таки были отменены Верховной Радой 28 января 2014 года. Но, увы, кухонная утварь
плотно сдавила черепа украинских патриотов: многие до сих пор кастрюли так и не
сняли.
«Нешуточная дата – день кастрюлеголовых. Есть и те, кто, спустя два года, ничего
так и не понял. Поздравляют друг дружку, вспоминают, кто ведро нацепил, кто –
дуршлаг, кто – кастрюлю с цветочками. Они и по сей день не сняли с головы кастрюли.
Им только кажется, что они их сняли. А на самом деле кастрюли приросли и пустили
корни», - пишет блогер <PERSON>.
Как сложилась судьба этих кастрюлеголовых идиотов – известно всем. Скачущие холопы
стали «незалежнее всех незалежных» вместе взятых: отныне их судьба им не принадлежит.
А как еще объяснить тот факт, что внутренние дела развалившейся Украины решают
<PERSON> и <PERSON>, при этом даже не информируя «козаков гаврылюков» о результатах
переговоров? И вправду, кастрюлеголовая Украина теперь самая независимая страна
в мире: от нее в мире ничего не зависит!
«Эй, мыздобулы! А ни у кого из вас не чешется ганьба через то, что ваши дела решают
<PERSON> (не президент, но, по вашим словам, командовавший снайперами на майдауне)
и <PERSON> (тоже не президент, но кормившая вас печеньками на майдауне)? Причем,
ваших олигофренов-хунтозавров даже не пригласили послушать, про что они там тёрли!
Ну, что шнобаками хлюпаете?
Хрюкните что-либо по этому поводу! СУГС, например», - обращается к майданным деятелям
блогер и журналист <PERSON>.
Но в ответ почему-то никто не хрюкает. Возможно, в сдавленных кастрюлями черепах
шевелятся остатки разума? И этот разум говорит следующее (снова устами <PERSON>):
1.Убили 50 тысяч человек.
2\. Изгнали из домов 3 млн. человек.
3\. Разрушили Донбасс.
4\. Прос*али Крым и Донбасс.
5\. Поссорили народ Украины.
6\. Сделали половину населения внутренними эмигрантами, а другую - подлыми стукачами
и тиранами.
7\. Уронили гривну в три раза.
8\. Уничтожили остатки промышленности.
9\. Оставили людей без работы и заработка.
10\. Посчитали гов*нные коммунальные услуги в гов*нных хрущёбах как в «пяти звездах».
11\. Сделали Украину колонией анонимных кредиторов.
12\. Сделали армию карательной, а солдат и офицеров - карателями.
13\. Сделали украинский паспорт тревожным сигналом для погранцов всех планет солнечной
системы.
14\. Установили тиранию потомков фашистских недобитков.
15\. Превратили прессу в яму со змеями.
16\. Превратили общество в стадо тупых, но агрессивных захребетников.
17\. <PERSON> предали и искусили предать других свою историю, своих предков, свою
Родину.
18\. Сделали презренного буржуя своим президентом.
Подробнее: http://antifashist.com/item/den-kastryulegolovyh-oni-zdobuly...'
- 'Всех приветствую.
Первый сшитый мной лонгер.
Ну или длинный кошелек.
Никогда их не делал, решил попробовать.
Оказалось довольно таки удобная штука.
Хвалюсь тем что получилось.
Семь отделений для карт, три больших кармана под купюры и документы.
Небольшой карман для монет или других мелких предметов закрывающийся на качественную
молнию.
Кожа: "растишка" итальянская 1,5 - 1,6 мм.
Подклад: овечка тонкая.
Пробойник: ромб 4 мм.
Нитка: Галасес 0,5 мм.
Торцы обработаны при помощи краски <PERSON> и наждачки.
Молния тоже какая то иностранная (не Китай, без вирусов)).
Как Вам?'
- нет, не в России, это в риге. не получится пока, дом давно сдали в эксплуатацию,
ворота наверняка закрыты, круг далеко от ворот. фоткать особо нечего там, просто
как большой круглый люк с двумя металлическими кольцами на стыке. на всякий <PERSON>.
у меня есть топоплан чтоб иметь представление. круг 4,5м (пунктир на плане), двор
от бордюра до стены 7х8м. всё включая круг уложено брусчаткой. возможно что в
кругу, это имитация брусчатки. механизма не видел. все уже было смонтировано когда
я мерял.
- source_sentence: '<PERSON> не проходить, а пропускать других людей - предоставлять
проходы.
По правилам у каждого человека должен быть свой индивидуальный способ прохода
- проездной, банковская карта, социальная карта и т.д. То есть нельзя купить проездной
на две поездки и двум людям использовать его для одновременного прохода. По факту
часто закрывают глаза, если по одному лимитному проездному проходят пара-тройка
человек (друзья, семья), но не стоит это воспринимать как должное и причитать,
если нарвётесь на штраф.'
sentences:
- 'Господа юристы, пожалуйста, прокомментируйте. Просто очень много противоречивой
информации. Хотя бы те моменты которые я чаще всего слышу.
Сейчас очень популярны настроения: вводите ЧП или ЧС.
И тогда заживем:
1\. Всем предприятиям по закону оплатят все зарплаты сотрудников которые в "самоизоляции".
2\. Всем простят кредиты. Ну ок, не простят, но можно не платить.
3\. Обязательств по договорам например с управляющей компанией нет. За ЖКХ можно
не платить.
4\. Всем кто не работает должны денег по закону. Я серьезно, и такое часто встречал.
Не будет ли:
1\. Любой работодатель сможет увольнять работников по трудовому договору. Ссылаются
на раздел "форс-мажор"
2\. Не получится ли так. У меня договор с со страховой компанией, наступил страховой
случай, компания не заплатила на законных основаниях ссылаясь на ЧП/ЧС?
<PERSON> от греха тег <PERSON>.'
- 'Веб в нынешнем его состоянии - это как раз клоака, где всем пофиг как, главное
чтоб работало и стоило $0.01, а лучше еще дешевле. Как оно там работает, будет
ли способно к развитию и поддержке - всем насрать, ибо через год все стопиццот
фреймворков по десять раз сменят мажорные версии и API, половина из них и вовсе
помрет, зато родится еще столько же новых, вчерашние джуниоры уйдут в менеджмент
и прочие "побегушки" а на их место вновь придут люди с нулевыми знаниями, старый
код даже пытаться разбирать никто не станет - его просто выкинут и напишут заново.
Это как с китайскими товарами: мммаксимум дешево, мммаксимум дешево и мммаксимум
дешево, при этом полностью неремотнопригодно.'
- 'Когда в меде учился,то на 5-м курсе была психиатрия -цикл.
В общем на одном занятии повели нас по палатам и "раздали " больных. Типа опросите,опишите
жизнь,анамнез и поставьте предварительный диагноз .
Итак, мне попалась девушка. На вид лет 17. Начинает рассказывать про жизнь,что
у нее она тяжёлая была. Всю жизнь в спорте с детства,что родила в 15 лет по залету.
Какой вид спорта? Она мне начинает рассказывать подробно про лёгкую атлетику ,затем
,что занималась спортивной гимнастикой . И говорила НАСТОЛЬКО убедительно ,что
я начал верить. Но затем ее понесло и начала говорить,что на Олимпиаде 2008 года
выступала ,заняла бронзу по бегу . Я на этом моменте прихерел и начал подозревать
подвох ( 17 лет, олимпиада, ребенок в 15....WTF?)
Для убедительности она херанулась с разбега в шпагат.
В итоге оказалось,что ей 21,ребенка никакого нет,никаким спортом она не занималась.
Была простой студенткой колледжа поваров и свихнулась. Шизофрения.
С тех пор я уверен,что много среди нас не совсем адекватных. Просто мы их не замечаем.'
- source_sentence: 'Решил своей девушке сделать подарок ко дню рождения. А именно
- котенка (давно хотела завести). Нашел девушку, которая отдает маленького милого
котенка в добрые руки. Созвонились, договорились о встрече, хозяйка сказала, что
как только вернется домой с магазина - можно приехать и забрать пушистого зверька.
Через час поступил звонок, своей девушке сказал, что надо по делам рабочим сгонять.
В итоге девушка оказалась непорядочной, когда приехал, она сообщила, что котенка
уже забрали 10 минут назад, мол, приехали раньше меня. Но это полбеды.
Вечером следующего дня я понял, что записать в девушку в телефоне как "Катя(Котёнок)"
было очень глупой идеей...'
sentences:
- 'Дело было году в 2010-м. Время моего беззаботного студенчества и прочих ништяков
19-тилетнего возраста. Дело было на зимних каникулах, в период святок. Я вернулась
в полупустое общежитие от родителей, через пару дней приехала моя близкая подруга,
которая предложила мне в одну из ночей вызвать чей-нибудь дух - святки же. Надо
сказать, в ту пору мы подобным оккультизмом увлекались в силу юного возраста и
гуманитарных направлений в институте. Я как умела пробовала гадать на таро (сейчас
понимаю, как это по прошествии лет выглядит), подруга собирала всякий оккультный
славянский фольклор и неплохо в этом разбиралась. Но ближе к делу. Решено было
вызвать дух. Но чей? Выбор по ряду причин пал на легендарную для отечественной
рок-музыки личность - <PERSON>.
Сеанс решили проводить с иглой на нити, кругом с буквами и свечой, которая однажды
была зажжена от благодатного огня. Свечу нам предоставила преподаватель по психологии(!),
которая неоднократно вещала о ее магических свойствах. В назначенный день, начитавшись
всяких оккультных историй и насмотревшись соответствующих фильмов мы наготовили
сладких пироженок (в подобной литературе часто говорится, что сладкое после таких
сеансов помогает прийти в себя).
Не веря особо в успех, мы выключили свет, закрыли окна, зажгли свечу, подвесили
иглу и начали вещать "Призываем дух <PERSON>!"
До какого-то момента мы вещали все ту же фразу "Мы призываем..." и уже готовы
были прекратить, так как игла висела неподвижно, и каких-то других признаков духа
не было, как вдруг из ниоткуда появился мотылек, мотылек, КАРЛ! (январь-месяц
на дворе) и начал наворачивать круги вокруг нас.
Стало не просто страшно. Стало до кирпичей страшно!!! Но отступать было некуда,
подруга решила начать распрашивать дух. Дрожащим голосом, с явными признаками
паники, она начала спрашивать его про <PERSON> и ее смерть (кто не знает, она
известный рок-музыкант, погибшая при странных обстоятельствах и состоявшая с <PERSON> в
отношениях долгое время). И вот тут...я не знаю... то ли у меня удачно дрожали
руки, то ли сквозняк, но игла начала качаться, выдавая вполне себе осмысленные
ответы из букв в круге.
Так продолжалось какое-то время, наша паника нарастала, как вдруг мотылек сел
прямо на пламя свечи и моментально сгорел... Подруга посчитала это знаком ухода
"духа", и мы порешили, что все закончилось.
Совпадение или нет - сладкого и правда хотелось до безумия. Мы включили свет,
достали приготовленные сладости и решили послушать музыку. Естественно запустили
в плейлист всю папку с названием "ГрОб". Казалось бы, все закончилось, но последним
звоночком стало то, что в рандомном плейлисте первой песней была "Отряд не заметил
потери бойца", которая начинается строкой "Глупый мотылек догорал на свечке".
Надо ли говорить, что мы ночью спали в другой комнате? И духов больше никогда
вызвать не пытались.
Сейчас, вспоминая данный случай, единственную крипоту в моей жизни, наверное,
я искренне хочу верить, что это не более чем стечение обстоятельств. Если бы не
мотылек, это им бы и было с вероятностью 146%. Подобными вещами больше не увлекаюсь.
Повзрослела и из гуманитария превратилась в инженера.'
- 'Чтобы факт, признанный наукой назвать "дурацким стереотипом"? Ну да, как по мне,
требуется осведомлённость повыше обывательской. И ещё раз. С вами никто не спорит.
Бывает всяко, в том числе так, как вы написали. Вариантов столько же, сколько
живых людей. А паттерны, усвоенные в семье ребёнком, присущи большому количеству
взрослых.
И речь изначально шла о модели семьи и вариантах поведения, связанных с ней. То
есть "мальчик копирует отношение папы к маме". Это не значит, что его ждёт такая
же судьба, у него будет такой же характер, или он тоже разведётся в 27. Но по
умолчанию, пока не наживёт своего опыта (а некоторые и дальше), мальчик будет
опираться на то, что видел в семье. Ваш пример с эмпатией - верен. Только в этом
контексте ничего не доказывает.'
- 'на работе выключили свет. Диспетчер сидит в своей программе, делает табеля. бесперебойник
периодически пищит.
\- д не пищи ты!!
\- пиик пиик пиик
\- д знаю я что света нет, не ори!!
\- пиик пиик пиик
\- ну сейчас сейчас, доделаю и выключу
\- пиик пиик пиик
\- какой же ты глупый!!!
\- пиик пиик пиик
\- как же ты достал, ну сейчас, еще 10 человек осталось и выключаю
\- пиииииииииик...........
компьютер гаснет.....пауза......злая тётенька диспетчер осознает что произошло
\- он что вырубился????? так я же ничего не сохранила!!!! какого черта??? мне
же теперь все заново делать!!!!! что за компьютер глупый такой!!!
почти собралась сисадмину звонить )))) но не стала)))
За компьютер обидно, он же из последних сил терпел, кричал, а его глупым...)'
---
# SentenceTransformer based on Tochka-AI/ruRoPEBert-e5-base-512
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) <!-- at revision faaef0a859c7623bafa93254af2bedcffab6e361 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RoPEBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("asaakyan/ruRoPEBert-e5-base-512-allRU-authorship")
# Run inference
sentences = [
'Решил своей девушке сделать подарок ко дню рождения. А именно - котенка (давно хотела завести). Нашел девушку, которая отдает маленького милого котенка в добрые руки. Созвонились, договорились о встрече, хозяйка сказала, что как только вернется домой с магазина - можно приехать и забрать пушистого зверька. Через час поступил звонок, своей девушке сказал, что надо по делам рабочим сгонять. В итоге девушка оказалась непорядочной, когда приехал, она сообщила, что котенка уже забрали 10 минут назад, мол, приехали раньше меня. Но это полбеды.\nВечером следующего дня я понял, что записать в девушку в телефоне как "Катя(Котёнок)" было очень глупой идеей...',
'на работе выключили свет. Диспетчер сидит в своей программе, делает табеля. бесперебойник периодически пищит.\n\\- д не пищи ты!!\n\\- пиик пиик пиик\n\\- д знаю я что света нет, не ори!!\n\\- пиик пиик пиик\n\\- ну сейчас сейчас, доделаю и выключу\n\\- пиик пиик пиик\n\\- какой же ты глупый!!!\n\\- пиик пиик пиик\n\\- как же ты достал, ну сейчас, еще 10 человек осталось и выключаю\n\\- пиииииииииик...........\nкомпьютер гаснет.....пауза......злая тётенька диспетчер осознает что произошло\n\\- он что вырубился????? так я же ничего не сохранила!!!! какого черта??? мне же теперь все заново делать!!!!! что за компьютер глупый такой!!!\nпочти собралась сисадмину звонить )))) но не стала)))\nЗа компьютер обидно, он же из последних сил терпел, кричал, а его глупым...)',
'Чтобы факт, признанный наукой назвать "дурацким стереотипом"? Ну да, как по мне, требуется осведомлённость повыше обывательской. И ещё раз. С вами никто не спорит. Бывает всяко, в том числе так, как вы написали. Вариантов столько же, сколько живых людей. А паттерны, усвоенные в семье ребёнком, присущи большому количеству взрослых.\nИ речь изначально шла о модели семьи и вариантах поведения, связанных с ней. То есть "мальчик копирует отношение папы к маме". Это не значит, что его ждёт такая же судьба, у него будет такой же характер, или он тоже разведётся в 27. Но по умолчанию, пока не наживёт своего опыта (а некоторые и дальше), мальчик будет опираться на то, что видел в семье. Ваш пример с эмпатией - верен. Только в этом контексте ничего не доказывает.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 2,465,773 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 97 tokens</li><li>mean: 304.32 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 93 tokens</li><li>mean: 304.08 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~49.90%</li><li>1: ~50.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>С целью повышения безопасности я ограничил сумму перевода со своих счетов 30 тысячами, а тут нужно жене перекинуть поболее.<br>Для голосового подтверждения я раньше звонил на номер <PERSON>, переходил на пункт ввода кода обслуживания в тональном режиме и далее подтверждал сумму и получателя.<br>Позвонив этот раз в Сбербанк ответила мне робот, голосовой помощник и обещала мне помочь, только скажи. Говорю "подтвердить операцию" на что робот говорит что не понимает меня. И со второй попытки тоже. После этого, не найдя контакта она предлагает мне соединить с оператором, на что я говорю Да, и-и-и, пара секунд и короткие гудки.<br><PERSON> повторно и сразу говорю "соединить с оператором", попадаю на живого человека, который выслушав меня говорит: ноль проблем, переключаю на ввод кода. И кто меня ждёт с той стороны? Правильно, опять не доделанный ИИ, который опять меня не понимает.<br>Прошу соединить с живым человеком, соединяет. На что новый сотрудник говорит, что по коду нельзя теперь авторизоваться, т...</code> | <code>Очень часто вижу негативные посты про опсосов и банки. И вот что хочу сказать: бесспорно это просто бизнес, но в тоже время как не ругай банки, они многим нужны. Так вот на волне негатива хочу сказать немного хорошего.<br>У меня, как и у большинства россиян зелёный банк. За время моего сотрудничества с ним я переоценил свое отношение к нему. Да, очереди, проблемы и косяки, были, но или мне повезло или как, но косяки все решились мне не в убыток.<br>Итак: с момента как у меня появилась зарплатная карта:<br>\- я купил первую квартиру в ипотеку, студию, деньги дал банк. Ипотеку гасил как мог быстро. Результат: кредит погасил до того момента, когда проценты превысили рыночную стоимость на момент закрытия кредита. Причем даже с профитом. И это при ставке 12.5%.<br>\- я купил бу машину, взяв потребительский кредит, погасил, переплата 12 тыс, но взял машину на 50% дороже, чем взял бы на свои накопления.<br>\- банк предложил кредиту виза серебряная первый год бесплатно. Взял, научился льготному периоду. Закр...</code> | <code>1</code> |
| <code>Тоже был на Канарах, там проходило одно студенческое мероприятие. Как-то вечером иду с знакомым поляком по вечернему городу, заходим в случайный бар за пивом. Иду уверенным шагом к барной стойке и тут слышу сзади меня: "Ооо, курваа..." и дальше не переводимая игра слов на польском. Оказалось, что мой знакомый встретил своего друга поляка и при этом, оба друг другу не говорили о том, что собираются куда-либо поехать. Вот так тоже подумаешь, несколько тысяч километров и два знакомых встречаются случайно.<br>Еще одну историю услышал от знакомых. Дело было в Барселоне. На пляже услышали родную речь и разговорились. Оказалось, мало того, что с одного города, так еще и с одного дома, только разные подъезды.</code> | <code>Есть у меня друг. Ему за тридцать, живет в деревне с матерью и сестрой. Все его друзья перебрались в город. И излюбленным его нытьем стало то, что они зазнались, зазвездились, каблуки и т.д. Звонит как то, говорит что в городе и предлагает сходить на новоселье к одному из них, своих старых друзей. Я малость удивился, с человеком не знаком, зачем я то пойду. Уверяет меня, что друг человек компанейский, новым друзьям всегда рад, а квартира его новая вообще в моем доме расположена, в другом подъезде ток. Ну, думаю, раз так, зайду, в случае чего можно и уйти под каким нибудь предлогом. Приходим, компании никакой нет, но накрыт стол в гостиной, бегает пацаненок лет пяти, с кухни выходит сильно беременная супруга. Познакомились с новыми соседями, сели, выпили. Не буду затягивать с рассказом: друга моего деревенского понесло, бутылки на троих ему было мало, сгонял еще за одной, потом еще...Потом горячо уговаривал всех в караоке поехать, ну а что, душа просит праздника. Утащить его с этой кв...</code> | <code>0</code> |
| <code>> никто из русских в здравом уме не пытается озвучить мысль-"всех чеченов надо убить"<br>Я искал какую-нибудь отметку о сарказме, но не нашел. Эту мысль озвучивали и озвучивают и сейчас везде - от политических деятелей до каких-нибудь Петровичей на кухне. Особенно легко такие призывы найти на Пикабу, и я тебя уверяю, они будут заплюсованы. Или спорить с этим будешь? Или скажешь, что каждый, кто это говорит не подходит под твое "в здравом уме" и потому снова несчитово?)) Тогда могу только восхититься твоей предусмотрительностью.<br>> Тем более-осуществить. Не в нашем это обычае, хотя и сил и средств достаточно, кмк.<br>Так пытались же, я расписал достаточно подробно. Убить однажды половину, в следующий раз - треть, а в последний раз четверть - разве нельзя считать это попытками? Просто не получилось, не смогли. Вся надежда на ОМП теперь.<br>Ну и вообще, не в тему твой комментарий, неполный он какой-то.</code> | <code>Да потому что вы несете бред, не связанный с реальностью. Это как дискутировать о том, что небо зеленое, а трава фиолетвая и в пупырушек. В вашем розовом мире писюнов злое правительство не дает нищасным папкам детей? Вам обьяснили почему вы можете видеть такую картину, почему существует перекос и откуда растут ноги у процента принятия решений. Но вам, походу, нужно чтобы суды исходили не из интересов ребенка, а из интересов вечно обделенных. Я то не против, я вообще считаю, что закон о принудительном оставлении ребенка в любом случае с отцом не плохо бы укрепил желание некоторых сохранять семью и выбирать адекватных партнеров</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 35
- `per_device_eval_batch_size`: 35
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 35
- `per_device_eval_batch_size`: 35
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0006 | 10 | 0.1108 |
| 0.0011 | 20 | 0.1075 |
| 0.0017 | 30 | 0.1033 |
| 0.0023 | 40 | 0.0971 |
| 0.0028 | 50 | 0.0852 |
| 0.0034 | 60 | 0.0693 |
| 0.0040 | 70 | 0.0599 |
| 0.0045 | 80 | 0.0478 |
| 0.0051 | 90 | 0.035 |
| 0.0057 | 100 | 0.03 |
| 0.0062 | 110 | 0.0285 |
| 0.0068 | 120 | 0.0292 |
| 0.0074 | 130 | 0.0288 |
| 0.0079 | 140 | 0.0282 |
| 0.0085 | 150 | 0.028 |
| 0.0091 | 160 | 0.0271 |
| 0.0097 | 170 | 0.0278 |
| 0.0102 | 180 | 0.0272 |
| 0.0108 | 190 | 0.0273 |
| 0.0114 | 200 | 0.0273 |
| 0.0119 | 210 | 0.0266 |
| 0.0125 | 220 | 0.0267 |
| 0.0131 | 230 | 0.0271 |
| 0.0136 | 240 | 0.0269 |
| 0.0142 | 250 | 0.0271 |
| 0.0148 | 260 | 0.0267 |
| 0.0153 | 270 | 0.0272 |
| 0.0159 | 280 | 0.0268 |
| 0.0165 | 290 | 0.0263 |
| 0.0170 | 300 | 0.0257 |
| 0.0176 | 310 | 0.0257 |
| 0.0182 | 320 | 0.0259 |
| 0.0187 | 330 | 0.0256 |
| 0.0193 | 340 | 0.0249 |
| 0.0199 | 350 | 0.025 |
| 0.0204 | 360 | 0.0251 |
| 0.0210 | 370 | 0.0244 |
| 0.0216 | 380 | 0.0244 |
| 0.0221 | 390 | 0.0246 |
| 0.0227 | 400 | 0.0248 |
| 0.0233 | 410 | 0.0243 |
| 0.0238 | 420 | 0.0251 |
| 0.0244 | 430 | 0.026 |
| 0.0250 | 440 | 0.0245 |
| 0.0255 | 450 | 0.0249 |
| 0.0261 | 460 | 0.0241 |
| 0.0267 | 470 | 0.0244 |
| 0.0273 | 480 | 0.0234 |
| 0.0278 | 490 | 0.0234 |
| 0.0284 | 500 | 0.0237 |
| 0.0290 | 510 | 0.0235 |
| 0.0295 | 520 | 0.0236 |
| 0.0301 | 530 | 0.0246 |
| 0.0307 | 540 | 0.0242 |
| 0.0312 | 550 | 0.023 |
| 0.0318 | 560 | 0.0231 |
| 0.0324 | 570 | 0.0232 |
| 0.0329 | 580 | 0.0233 |
| 0.0335 | 590 | 0.023 |
| 0.0341 | 600 | 0.0235 |
| 0.0346 | 610 | 0.0241 |
| 0.0352 | 620 | 0.0225 |
| 0.0358 | 630 | 0.0224 |
| 0.0363 | 640 | 0.0232 |
| 0.0369 | 650 | 0.0229 |
| 0.0375 | 660 | 0.023 |
| 0.0380 | 670 | 0.0232 |
| 0.0386 | 680 | 0.0217 |
| 0.0392 | 690 | 0.0224 |
| 0.0397 | 700 | 0.0225 |
| 0.0403 | 710 | 0.023 |
| 0.0409 | 720 | 0.0234 |
| 0.0414 | 730 | 0.023 |
| 0.0420 | 740 | 0.0215 |
| 0.0426 | 750 | 0.0216 |
| 0.0431 | 760 | 0.0219 |
| 0.0437 | 770 | 0.0208 |
| 0.0443 | 780 | 0.0222 |
| 0.0449 | 790 | 0.0233 |
| 0.0454 | 800 | 0.0224 |
| 0.0460 | 810 | 0.0217 |
| 0.0466 | 820 | 0.0225 |
| 0.0471 | 830 | 0.0219 |
| 0.0477 | 840 | 0.0219 |
| 0.0483 | 850 | 0.0209 |
| 0.0488 | 860 | 0.0218 |
| 0.0494 | 870 | 0.022 |
| 0.0500 | 880 | 0.0215 |
| 0.0505 | 890 | 0.0229 |
| 0.0511 | 900 | 0.0212 |
| 0.0517 | 910 | 0.0221 |
| 0.0522 | 920 | 0.023 |
| 0.0528 | 930 | 0.022 |
| 0.0534 | 940 | 0.0217 |
| 0.0539 | 950 | 0.022 |
| 0.0545 | 960 | 0.0213 |
| 0.0551 | 970 | 0.023 |
| 0.0556 | 980 | 0.0215 |
| 0.0562 | 990 | 0.0208 |
| 0.0568 | 1000 | 0.0225 |
| 0.0573 | 1010 | 0.0217 |
| 0.0579 | 1020 | 0.0213 |
| 0.0585 | 1030 | 0.0212 |
| 0.0590 | 1040 | 0.0214 |
| 0.0596 | 1050 | 0.0224 |
| 0.0602 | 1060 | 0.021 |
| 0.0608 | 1070 | 0.0205 |
| 0.0613 | 1080 | 0.0227 |
| 0.0619 | 1090 | 0.0224 |
| 0.0625 | 1100 | 0.0213 |
| 0.0630 | 1110 | 0.0219 |
| 0.0636 | 1120 | 0.0213 |
| 0.0642 | 1130 | 0.0216 |
| 0.0647 | 1140 | 0.0205 |
| 0.0653 | 1150 | 0.021 |
| 0.0659 | 1160 | 0.0211 |
| 0.0664 | 1170 | 0.0212 |
| 0.0670 | 1180 | 0.0207 |
| 0.0676 | 1190 | 0.0213 |
| 0.0681 | 1200 | 0.0214 |
| 0.0687 | 1210 | 0.0216 |
| 0.0693 | 1220 | 0.0221 |
| 0.0698 | 1230 | 0.0223 |
| 0.0704 | 1240 | 0.0197 |
| 0.0710 | 1250 | 0.0215 |
| 0.0715 | 1260 | 0.022 |
| 0.0721 | 1270 | 0.0218 |
| 0.0727 | 1280 | 0.0208 |
| 0.0732 | 1290 | 0.0215 |
| 0.0738 | 1300 | 0.0216 |
| 0.0744 | 1310 | 0.0204 |
| 0.0749 | 1320 | 0.0217 |
| 0.0755 | 1330 | 0.0222 |
| 0.0761 | 1340 | 0.0207 |
| 0.0766 | 1350 | 0.022 |
| 0.0772 | 1360 | 0.0211 |
| 0.0778 | 1370 | 0.0203 |
| 0.0784 | 1380 | 0.0206 |
| 0.0789 | 1390 | 0.0209 |
| 0.0795 | 1400 | 0.0207 |
| 0.0801 | 1410 | 0.0219 |
| 0.0806 | 1420 | 0.0215 |
| 0.0812 | 1430 | 0.0215 |
| 0.0818 | 1440 | 0.0206 |
| 0.0823 | 1450 | 0.0212 |
| 0.0829 | 1460 | 0.0202 |
| 0.0835 | 1470 | 0.0212 |
| 0.0840 | 1480 | 0.0214 |
| 0.0846 | 1490 | 0.0213 |
| 0.0852 | 1500 | 0.0213 |
| 0.0857 | 1510 | 0.0186 |
| 0.0863 | 1520 | 0.0206 |
| 0.0869 | 1530 | 0.0214 |
| 0.0874 | 1540 | 0.0205 |
| 0.0880 | 1550 | 0.0211 |
| 0.0886 | 1560 | 0.0213 |
| 0.0891 | 1570 | 0.021 |
| 0.0897 | 1580 | 0.0207 |
| 0.0903 | 1590 | 0.0217 |
| 0.0908 | 1600 | 0.0206 |
| 0.0914 | 1610 | 0.0202 |
| 0.0920 | 1620 | 0.0219 |
| 0.0925 | 1630 | 0.0205 |
| 0.0931 | 1640 | 0.021 |
| 0.0937 | 1650 | 0.0197 |
| 0.0942 | 1660 | 0.0206 |
| 0.0948 | 1670 | 0.0209 |
| 0.0954 | 1680 | 0.021 |
| 0.0960 | 1690 | 0.0217 |
| 0.0965 | 1700 | 0.0224 |
| 0.0971 | 1710 | 0.0208 |
| 0.0977 | 1720 | 0.0212 |
| 0.0982 | 1730 | 0.0212 |
| 0.0988 | 1740 | 0.0216 |
| 0.0994 | 1750 | 0.0205 |
| 0.0999 | 1760 | 0.0204 |
| 0.1005 | 1770 | 0.0202 |
| 0.1011 | 1780 | 0.0203 |
| 0.1016 | 1790 | 0.0207 |
| 0.1022 | 1800 | 0.0209 |
| 0.1028 | 1810 | 0.02 |
| 0.1033 | 1820 | 0.0204 |
| 0.1039 | 1830 | 0.0204 |
| 0.1045 | 1840 | 0.02 |
| 0.1050 | 1850 | 0.0218 |
| 0.1056 | 1860 | 0.0212 |
| 0.1062 | 1870 | 0.021 |
| 0.1067 | 1880 | 0.021 |
| 0.1073 | 1890 | 0.0214 |
| 0.1079 | 1900 | 0.0199 |
| 0.1084 | 1910 | 0.0207 |
| 0.1090 | 1920 | 0.0205 |
| 0.1096 | 1930 | 0.0204 |
| 0.1101 | 1940 | 0.0217 |
| 0.1107 | 1950 | 0.0217 |
| 0.1113 | 1960 | 0.0212 |
| 0.1118 | 1970 | 0.0204 |
| 0.1124 | 1980 | 0.0214 |
| 0.1130 | 1990 | 0.0206 |
| 0.1136 | 2000 | 0.0205 |
| 0.1141 | 2010 | 0.0206 |
| 0.1147 | 2020 | 0.0208 |
| 0.1153 | 2030 | 0.0209 |
| 0.1158 | 2040 | 0.0206 |
| 0.1164 | 2050 | 0.0212 |
| 0.1170 | 2060 | 0.0209 |
| 0.1175 | 2070 | 0.0206 |
| 0.1181 | 2080 | 0.02 |
| 0.1187 | 2090 | 0.0209 |
| 0.1192 | 2100 | 0.0211 |
| 0.1198 | 2110 | 0.0207 |
| 0.1204 | 2120 | 0.0211 |
| 0.1209 | 2130 | 0.0206 |
| 0.1215 | 2140 | 0.0214 |
| 0.1221 | 2150 | 0.021 |
| 0.1226 | 2160 | 0.0194 |
| 0.1232 | 2170 | 0.0206 |
| 0.1238 | 2180 | 0.0204 |
| 0.1243 | 2190 | 0.0202 |
| 0.1249 | 2200 | 0.0208 |
| 0.1255 | 2210 | 0.0207 |
| 0.1260 | 2220 | 0.0207 |
| 0.1266 | 2230 | 0.0204 |
| 0.1272 | 2240 | 0.0205 |
| 0.1277 | 2250 | 0.0207 |
| 0.1283 | 2260 | 0.0212 |
| 0.1289 | 2270 | 0.0196 |
| 0.1294 | 2280 | 0.021 |
| 0.1300 | 2290 | 0.0207 |
| 0.1306 | 2300 | 0.0222 |
| 0.1312 | 2310 | 0.0203 |
| 0.1317 | 2320 | 0.0209 |
| 0.1323 | 2330 | 0.0209 |
| 0.1329 | 2340 | 0.0205 |
| 0.1334 | 2350 | 0.0199 |
| 0.1340 | 2360 | 0.0207 |
| 0.1346 | 2370 | 0.021 |
| 0.1351 | 2380 | 0.0202 |
| 0.1357 | 2390 | 0.0213 |
| 0.1363 | 2400 | 0.0208 |
| 0.1368 | 2410 | 0.0201 |
| 0.1374 | 2420 | 0.0208 |
| 0.1380 | 2430 | 0.0201 |
| 0.1385 | 2440 | 0.0201 |
| 0.1391 | 2450 | 0.0202 |
| 0.1397 | 2460 | 0.0203 |
| 0.1402 | 2470 | 0.0203 |
| 0.1408 | 2480 | 0.0204 |
| 0.1414 | 2490 | 0.02 |
| 0.1419 | 2500 | 0.0214 |
| 0.1425 | 2510 | 0.0202 |
| 0.1431 | 2520 | 0.0203 |
| 0.1436 | 2530 | 0.0212 |
| 0.1442 | 2540 | 0.0219 |
| 0.1448 | 2550 | 0.0207 |
| 0.1453 | 2560 | 0.02 |
| 0.1459 | 2570 | 0.0208 |
| 0.1465 | 2580 | 0.0213 |
| 0.1471 | 2590 | 0.0199 |
| 0.1476 | 2600 | 0.0208 |
| 0.1482 | 2610 | 0.0207 |
| 0.1488 | 2620 | 0.0198 |
| 0.1493 | 2630 | 0.0205 |
| 0.1499 | 2640 | 0.0212 |
| 0.1505 | 2650 | 0.0215 |
| 0.1510 | 2660 | 0.0209 |
| 0.1516 | 2670 | 0.0207 |
| 0.1522 | 2680 | 0.021 |
| 0.1527 | 2690 | 0.0212 |
| 0.1533 | 2700 | 0.0208 |
| 0.1539 | 2710 | 0.0205 |
| 0.1544 | 2720 | 0.0193 |
| 0.1550 | 2730 | 0.0209 |
| 0.1556 | 2740 | 0.0208 |
| 0.1561 | 2750 | 0.02 |
| 0.1567 | 2760 | 0.0203 |
| 0.1573 | 2770 | 0.0197 |
| 0.1578 | 2780 | 0.0205 |
| 0.1584 | 2790 | 0.0198 |
| 0.1590 | 2800 | 0.0199 |
| 0.1595 | 2810 | 0.0193 |
| 0.1601 | 2820 | 0.0209 |
| 0.1607 | 2830 | 0.0206 |
| 0.1612 | 2840 | 0.0206 |
| 0.1618 | 2850 | 0.0207 |
| 0.1624 | 2860 | 0.0202 |
| 0.1629 | 2870 | 0.0203 |
| 0.1635 | 2880 | 0.0204 |
| 0.1641 | 2890 | 0.0197 |
| 0.1647 | 2900 | 0.02 |
| 0.1652 | 2910 | 0.0204 |
| 0.1658 | 2920 | 0.0206 |
| 0.1664 | 2930 | 0.0204 |
| 0.1669 | 2940 | 0.0202 |
| 0.1675 | 2950 | 0.0205 |
| 0.1681 | 2960 | 0.0199 |
| 0.1686 | 2970 | 0.0197 |
| 0.1692 | 2980 | 0.0207 |
| 0.1698 | 2990 | 0.0201 |
| 0.1703 | 3000 | 0.0199 |
| 0.1709 | 3010 | 0.0205 |
| 0.1715 | 3020 | 0.0198 |
| 0.1720 | 3030 | 0.0206 |
| 0.1726 | 3040 | 0.0189 |
| 0.1732 | 3050 | 0.0209 |
| 0.1737 | 3060 | 0.02 |
| 0.1743 | 3070 | 0.0191 |
| 0.1749 | 3080 | 0.0206 |
| 0.1754 | 3090 | 0.0198 |
| 0.1760 | 3100 | 0.0219 |
| 0.1766 | 3110 | 0.02 |
| 0.1771 | 3120 | 0.0194 |
| 0.1777 | 3130 | 0.0221 |
| 0.1783 | 3140 | 0.0196 |
| 0.1788 | 3150 | 0.0197 |
| 0.1794 | 3160 | 0.0215 |
| 0.1800 | 3170 | 0.0195 |
| 0.1805 | 3180 | 0.0198 |
| 0.1811 | 3190 | 0.021 |
| 0.1817 | 3200 | 0.0204 |
| 0.1823 | 3210 | 0.0199 |
| 0.1828 | 3220 | 0.0207 |
| 0.1834 | 3230 | 0.0194 |
| 0.1840 | 3240 | 0.0212 |
| 0.1845 | 3250 | 0.0199 |
| 0.1851 | 3260 | 0.0198 |
| 0.1857 | 3270 | 0.0201 |
| 0.1862 | 3280 | 0.0204 |
| 0.1868 | 3290 | 0.0206 |
| 0.1874 | 3300 | 0.0206 |
| 0.1879 | 3310 | 0.0195 |
| 0.1885 | 3320 | 0.0205 |
| 0.1891 | 3330 | 0.0208 |
| 0.1896 | 3340 | 0.0205 |
| 0.1902 | 3350 | 0.0208 |
| 0.1908 | 3360 | 0.0194 |
| 0.1913 | 3370 | 0.0205 |
| 0.1919 | 3380 | 0.0201 |
| 0.1925 | 3390 | 0.0212 |
| 0.1930 | 3400 | 0.019 |
| 0.1936 | 3410 | 0.0198 |
| 0.1942 | 3420 | 0.0203 |
| 0.1947 | 3430 | 0.0205 |
| 0.1953 | 3440 | 0.0207 |
| 0.1959 | 3450 | 0.0207 |
| 0.1964 | 3460 | 0.0203 |
| 0.1970 | 3470 | 0.0194 |
| 0.1976 | 3480 | 0.0198 |
| 0.1981 | 3490 | 0.0195 |
| 0.1987 | 3500 | 0.0204 |
| 0.1993 | 3510 | 0.0209 |
| 0.1999 | 3520 | 0.021 |
| 0.2004 | 3530 | 0.0194 |
| 0.2010 | 3540 | 0.0199 |
| 0.2016 | 3550 | 0.0215 |
| 0.2021 | 3560 | 0.0195 |
| 0.2027 | 3570 | 0.0202 |
| 0.2033 | 3580 | 0.0203 |
| 0.2038 | 3590 | 0.0194 |
| 0.2044 | 3600 | 0.0198 |
| 0.2050 | 3610 | 0.02 |
| 0.2055 | 3620 | 0.0205 |
| 0.2061 | 3630 | 0.0195 |
| 0.2067 | 3640 | 0.0195 |
| 0.2072 | 3650 | 0.0194 |
| 0.2078 | 3660 | 0.02 |
| 0.2084 | 3670 | 0.0198 |
| 0.2089 | 3680 | 0.0197 |
| 0.2095 | 3690 | 0.0202 |
| 0.2101 | 3700 | 0.0198 |
| 0.2106 | 3710 | 0.0208 |
| 0.2112 | 3720 | 0.0207 |
| 0.2118 | 3730 | 0.0197 |
| 0.2123 | 3740 | 0.0194 |
| 0.2129 | 3750 | 0.0188 |
| 0.2135 | 3760 | 0.0201 |
| 0.2140 | 3770 | 0.02 |
| 0.2146 | 3780 | 0.0199 |
| 0.2152 | 3790 | 0.0202 |
| 0.2157 | 3800 | 0.0198 |
| 0.2163 | 3810 | 0.0195 |
| 0.2169 | 3820 | 0.0202 |
| 0.2175 | 3830 | 0.0197 |
| 0.2180 | 3840 | 0.0209 |
| 0.2186 | 3850 | 0.0203 |
| 0.2192 | 3860 | 0.02 |
| 0.2197 | 3870 | 0.0197 |
| 0.2203 | 3880 | 0.0204 |
| 0.2209 | 3890 | 0.0198 |
| 0.2214 | 3900 | 0.0198 |
| 0.2220 | 3910 | 0.0207 |
| 0.2226 | 3920 | 0.0193 |
| 0.2231 | 3930 | 0.0194 |
| 0.2237 | 3940 | 0.0202 |
| 0.2243 | 3950 | 0.0205 |
| 0.2248 | 3960 | 0.0202 |
| 0.2254 | 3970 | 0.0192 |
| 0.2260 | 3980 | 0.02 |
| 0.2265 | 3990 | 0.0185 |
| 0.2271 | 4000 | 0.0206 |
| 0.2277 | 4010 | 0.0193 |
| 0.2282 | 4020 | 0.0209 |
| 0.2288 | 4030 | 0.0205 |
| 0.2294 | 4040 | 0.0197 |
| 0.2299 | 4050 | 0.0201 |
| 0.2305 | 4060 | 0.0197 |
| 0.2311 | 4070 | 0.0202 |
| 0.2316 | 4080 | 0.0203 |
| 0.2322 | 4090 | 0.0215 |
| 0.2328 | 4100 | 0.0206 |
| 0.2334 | 4110 | 0.0197 |
| 0.2339 | 4120 | 0.0209 |
| 0.2345 | 4130 | 0.0205 |
| 0.2351 | 4140 | 0.0214 |
| 0.2356 | 4150 | 0.02 |
| 0.2362 | 4160 | 0.0208 |
| 0.2368 | 4170 | 0.02 |
| 0.2373 | 4180 | 0.0197 |
| 0.2379 | 4190 | 0.0203 |
| 0.2385 | 4200 | 0.0207 |
| 0.2390 | 4210 | 0.0199 |
| 0.2396 | 4220 | 0.0199 |
| 0.2402 | 4230 | 0.0187 |
| 0.2407 | 4240 | 0.0201 |
| 0.2413 | 4250 | 0.0213 |
| 0.2419 | 4260 | 0.0204 |
| 0.2424 | 4270 | 0.0205 |
| 0.2430 | 4280 | 0.02 |
| 0.2436 | 4290 | 0.0206 |
| 0.2441 | 4300 | 0.0199 |
| 0.2447 | 4310 | 0.0201 |
| 0.2453 | 4320 | 0.021 |
| 0.2458 | 4330 | 0.0206 |
| 0.2464 | 4340 | 0.0206 |
| 0.2470 | 4350 | 0.019 |
| 0.2475 | 4360 | 0.0206 |
| 0.2481 | 4370 | 0.0199 |
| 0.2487 | 4380 | 0.0206 |
| 0.2492 | 4390 | 0.0202 |
| 0.2498 | 4400 | 0.0199 |
| 0.2504 | 4410 | 0.0196 |
| 0.2510 | 4420 | 0.0196 |
| 0.2515 | 4430 | 0.0206 |
| 0.2521 | 4440 | 0.0202 |
| 0.2527 | 4450 | 0.0195 |
| 0.2532 | 4460 | 0.0196 |
| 0.2538 | 4470 | 0.0205 |
| 0.2544 | 4480 | 0.0203 |
| 0.2549 | 4490 | 0.0202 |
| 0.2555 | 4500 | 0.0198 |
| 0.2561 | 4510 | 0.0201 |
| 0.2566 | 4520 | 0.0195 |
| 0.2572 | 4530 | 0.0197 |
| 0.2578 | 4540 | 0.0189 |
| 0.2583 | 4550 | 0.021 |
| 0.2589 | 4560 | 0.0197 |
| 0.2595 | 4570 | 0.021 |
| 0.2600 | 4580 | 0.0201 |
| 0.2606 | 4590 | 0.0205 |
| 0.2612 | 4600 | 0.0201 |
| 0.2617 | 4610 | 0.0186 |
| 0.2623 | 4620 | 0.0203 |
| 0.2629 | 4630 | 0.0193 |
| 0.2634 | 4640 | 0.0197 |
| 0.2640 | 4650 | 0.0209 |
| 0.2646 | 4660 | 0.0201 |
| 0.2651 | 4670 | 0.0203 |
| 0.2657 | 4680 | 0.0202 |
| 0.2663 | 4690 | 0.0193 |
| 0.2668 | 4700 | 0.02 |
| 0.2674 | 4710 | 0.0198 |
| 0.2680 | 4720 | 0.0191 |
| 0.2686 | 4730 | 0.0194 |
| 0.2691 | 4740 | 0.0195 |
| 0.2697 | 4750 | 0.0191 |
| 0.2703 | 4760 | 0.0204 |
| 0.2708 | 4770 | 0.0195 |
| 0.2714 | 4780 | 0.0197 |
| 0.2720 | 4790 | 0.0206 |
| 0.2725 | 4800 | 0.0199 |
| 0.2731 | 4810 | 0.02 |
| 0.2737 | 4820 | 0.02 |
| 0.2742 | 4830 | 0.0196 |
| 0.2748 | 4840 | 0.0202 |
| 0.2754 | 4850 | 0.0195 |
| 0.2759 | 4860 | 0.0204 |
| 0.2765 | 4870 | 0.0194 |
| 0.2771 | 4880 | 0.0194 |
| 0.2776 | 4890 | 0.0205 |
| 0.2782 | 4900 | 0.0191 |
| 0.2788 | 4910 | 0.02 |
| 0.2793 | 4920 | 0.0196 |
| 0.2799 | 4930 | 0.0181 |
| 0.2805 | 4940 | 0.0198 |
| 0.2810 | 4950 | 0.0201 |
| 0.2816 | 4960 | 0.0199 |
| 0.2822 | 4970 | 0.0202 |
| 0.2827 | 4980 | 0.0199 |
| 0.2833 | 4990 | 0.0187 |
| 0.2839 | 5000 | 0.0206 |
| 0.2844 | 5010 | 0.0198 |
| 0.2850 | 5020 | 0.0209 |
| 0.2856 | 5030 | 0.0196 |
| 0.2862 | 5040 | 0.0201 |
| 0.2867 | 5050 | 0.0201 |
| 0.2873 | 5060 | 0.0193 |
| 0.2879 | 5070 | 0.0206 |
| 0.2884 | 5080 | 0.0201 |
| 0.2890 | 5090 | 0.0191 |
| 0.2896 | 5100 | 0.0207 |
| 0.2901 | 5110 | 0.0198 |
| 0.2907 | 5120 | 0.0201 |
| 0.2913 | 5130 | 0.0198 |
| 0.2918 | 5140 | 0.0195 |
| 0.2924 | 5150 | 0.0198 |
| 0.2930 | 5160 | 0.0202 |
| 0.2935 | 5170 | 0.0189 |
| 0.2941 | 5180 | 0.0194 |
| 0.2947 | 5190 | 0.0203 |
| 0.2952 | 5200 | 0.0194 |
| 0.2958 | 5210 | 0.0195 |
| 0.2964 | 5220 | 0.0197 |
| 0.2969 | 5230 | 0.0197 |
| 0.2975 | 5240 | 0.0188 |
| 0.2981 | 5250 | 0.02 |
| 0.2986 | 5260 | 0.02 |
| 0.2992 | 5270 | 0.0199 |
| 0.2998 | 5280 | 0.0205 |
| 0.3003 | 5290 | 0.0204 |
| 0.3009 | 5300 | 0.0192 |
| 0.3015 | 5310 | 0.0206 |
| 0.3020 | 5320 | 0.0212 |
| 0.3026 | 5330 | 0.0197 |
| 0.3032 | 5340 | 0.0192 |
| 0.3038 | 5350 | 0.0197 |
| 0.3043 | 5360 | 0.0189 |
| 0.3049 | 5370 | 0.0195 |
| 0.3055 | 5380 | 0.0202 |
| 0.3060 | 5390 | 0.0199 |
| 0.3066 | 5400 | 0.0202 |
| 0.3072 | 5410 | 0.0217 |
| 0.3077 | 5420 | 0.0208 |
| 0.3083 | 5430 | 0.0207 |
| 0.3089 | 5440 | 0.0207 |
| 0.3094 | 5450 | 0.0201 |
| 0.3100 | 5460 | 0.0197 |
| 0.3106 | 5470 | 0.0192 |
| 0.3111 | 5480 | 0.0192 |
| 0.3117 | 5490 | 0.0198 |
| 0.3123 | 5500 | 0.0202 |
| 0.3128 | 5510 | 0.0182 |
| 0.3134 | 5520 | 0.0198 |
| 0.3140 | 5530 | 0.0195 |
| 0.3145 | 5540 | 0.0192 |
| 0.3151 | 5550 | 0.0193 |
| 0.3157 | 5560 | 0.0189 |
| 0.3162 | 5570 | 0.0198 |
| 0.3168 | 5580 | 0.02 |
| 0.3174 | 5590 | 0.0196 |
| 0.3179 | 5600 | 0.0196 |
| 0.3185 | 5610 | 0.0197 |
| 0.3191 | 5620 | 0.0193 |
| 0.3197 | 5630 | 0.021 |
| 0.3202 | 5640 | 0.0193 |
| 0.3208 | 5650 | 0.0204 |
| 0.3214 | 5660 | 0.0187 |
| 0.3219 | 5670 | 0.0191 |
| 0.3225 | 5680 | 0.02 |
| 0.3231 | 5690 | 0.0197 |
| 0.3236 | 5700 | 0.0198 |
| 0.3242 | 5710 | 0.0198 |
| 0.3248 | 5720 | 0.0192 |
| 0.3253 | 5730 | 0.019 |
| 0.3259 | 5740 | 0.0195 |
| 0.3265 | 5750 | 0.0192 |
| 0.3270 | 5760 | 0.0193 |
| 0.3276 | 5770 | 0.0197 |
| 0.3282 | 5780 | 0.0204 |
| 0.3287 | 5790 | 0.0199 |
| 0.3293 | 5800 | 0.0192 |
| 0.3299 | 5810 | 0.0202 |
| 0.3304 | 5820 | 0.019 |
| 0.3310 | 5830 | 0.0195 |
| 0.3316 | 5840 | 0.02 |
| 0.3321 | 5850 | 0.0197 |
| 0.3327 | 5860 | 0.0188 |
| 0.3333 | 5870 | 0.0204 |
| 0.3338 | 5880 | 0.0196 |
| 0.3344 | 5890 | 0.0203 |
| 0.3350 | 5900 | 0.0189 |
| 0.3355 | 5910 | 0.0197 |
| 0.3361 | 5920 | 0.0196 |
| 0.3367 | 5930 | 0.0194 |
| 0.3373 | 5940 | 0.0191 |
| 0.3378 | 5950 | 0.0196 |
| 0.3384 | 5960 | 0.0181 |
| 0.3390 | 5970 | 0.0205 |
| 0.3395 | 5980 | 0.0194 |
| 0.3401 | 5990 | 0.02 |
| 0.3407 | 6000 | 0.0187 |
| 0.3412 | 6010 | 0.0196 |
| 0.3418 | 6020 | 0.0206 |
| 0.3424 | 6030 | 0.019 |
| 0.3429 | 6040 | 0.0189 |
| 0.3435 | 6050 | 0.0196 |
| 0.3441 | 6060 | 0.0194 |
| 0.3446 | 6070 | 0.0205 |
| 0.3452 | 6080 | 0.0193 |
| 0.3458 | 6090 | 0.019 |
| 0.3463 | 6100 | 0.0191 |
| 0.3469 | 6110 | 0.0199 |
| 0.3475 | 6120 | 0.019 |
| 0.3480 | 6130 | 0.0206 |
| 0.3486 | 6140 | 0.0191 |
| 0.3492 | 6150 | 0.0195 |
| 0.3497 | 6160 | 0.0193 |
| 0.3503 | 6170 | 0.0191 |
| 0.3509 | 6180 | 0.0192 |
| 0.3514 | 6190 | 0.0196 |
| 0.3520 | 6200 | 0.0193 |
| 0.3526 | 6210 | 0.0191 |
| 0.3531 | 6220 | 0.0201 |
| 0.3537 | 6230 | 0.0194 |
| 0.3543 | 6240 | 0.0182 |
| 0.3549 | 6250 | 0.019 |
| 0.3554 | 6260 | 0.0201 |
| 0.3560 | 6270 | 0.0188 |
| 0.3566 | 6280 | 0.0196 |
| 0.3571 | 6290 | 0.0195 |
| 0.3577 | 6300 | 0.0195 |
| 0.3583 | 6310 | 0.019 |
| 0.3588 | 6320 | 0.0184 |
| 0.3594 | 6330 | 0.0197 |
| 0.3600 | 6340 | 0.0194 |
| 0.3605 | 6350 | 0.0196 |
| 0.3611 | 6360 | 0.0201 |
| 0.3617 | 6370 | 0.0197 |
| 0.3622 | 6380 | 0.0193 |
| 0.3628 | 6390 | 0.0189 |
| 0.3634 | 6400 | 0.0201 |
| 0.3639 | 6410 | 0.0197 |
| 0.3645 | 6420 | 0.0204 |
| 0.3651 | 6430 | 0.0202 |
| 0.3656 | 6440 | 0.0195 |
| 0.3662 | 6450 | 0.0194 |
| 0.3668 | 6460 | 0.0199 |
| 0.3673 | 6470 | 0.019 |
| 0.3679 | 6480 | 0.0193 |
| 0.3685 | 6490 | 0.0196 |
| 0.3690 | 6500 | 0.0194 |
| 0.3696 | 6510 | 0.0189 |
| 0.3702 | 6520 | 0.0183 |
| 0.3707 | 6530 | 0.0197 |
| 0.3713 | 6540 | 0.0194 |
| 0.3719 | 6550 | 0.0197 |
| 0.3725 | 6560 | 0.0199 |
| 0.3730 | 6570 | 0.0187 |
| 0.3736 | 6580 | 0.0201 |
| 0.3742 | 6590 | 0.0197 |
| 0.3747 | 6600 | 0.0192 |
| 0.3753 | 6610 | 0.02 |
| 0.3759 | 6620 | 0.0192 |
| 0.3764 | 6630 | 0.0187 |
| 0.3770 | 6640 | 0.0202 |
| 0.3776 | 6650 | 0.0201 |
| 0.3781 | 6660 | 0.0194 |
| 0.3787 | 6670 | 0.0192 |
| 0.3793 | 6680 | 0.0191 |
| 0.3798 | 6690 | 0.0198 |
| 0.3804 | 6700 | 0.0196 |
| 0.3810 | 6710 | 0.0183 |
| 0.3815 | 6720 | 0.0196 |
| 0.3821 | 6730 | 0.0194 |
| 0.3827 | 6740 | 0.0193 |
| 0.3832 | 6750 | 0.0199 |
| 0.3838 | 6760 | 0.0198 |
| 0.3844 | 6770 | 0.0191 |
| 0.3849 | 6780 | 0.0197 |
| 0.3855 | 6790 | 0.0194 |
| 0.3861 | 6800 | 0.019 |
| 0.3866 | 6810 | 0.0198 |
| 0.3872 | 6820 | 0.0192 |
| 0.3878 | 6830 | 0.0198 |
| 0.3883 | 6840 | 0.0194 |
| 0.3889 | 6850 | 0.0207 |
| 0.3895 | 6860 | 0.02 |
| 0.3901 | 6870 | 0.0199 |
| 0.3906 | 6880 | 0.019 |
| 0.3912 | 6890 | 0.0194 |
| 0.3918 | 6900 | 0.0181 |
| 0.3923 | 6910 | 0.0186 |
| 0.3929 | 6920 | 0.0188 |
| 0.3935 | 6930 | 0.0206 |
| 0.3940 | 6940 | 0.0193 |
| 0.3946 | 6950 | 0.0195 |
| 0.3952 | 6960 | 0.0188 |
| 0.3957 | 6970 | 0.0195 |
| 0.3963 | 6980 | 0.0199 |
| 0.3969 | 6990 | 0.0197 |
| 0.3974 | 7000 | 0.0197 |
| 0.3980 | 7010 | 0.0194 |
| 0.3986 | 7020 | 0.0194 |
| 0.3991 | 7030 | 0.0188 |
| 0.3997 | 7040 | 0.0194 |
| 0.4003 | 7050 | 0.0193 |
| 0.4008 | 7060 | 0.0198 |
| 0.4014 | 7070 | 0.0192 |
| 0.4020 | 7080 | 0.0207 |
| 0.4025 | 7090 | 0.0203 |
| 0.4031 | 7100 | 0.0189 |
| 0.4037 | 7110 | 0.02 |
| 0.4042 | 7120 | 0.0196 |
| 0.4048 | 7130 | 0.0203 |
| 0.4054 | 7140 | 0.019 |
| 0.4060 | 7150 | 0.0191 |
| 0.4065 | 7160 | 0.0196 |
| 0.4071 | 7170 | 0.0196 |
| 0.4077 | 7180 | 0.0197 |
| 0.4082 | 7190 | 0.0187 |
| 0.4088 | 7200 | 0.0193 |
| 0.4094 | 7210 | 0.0191 |
| 0.4099 | 7220 | 0.0201 |
| 0.4105 | 7230 | 0.0185 |
| 0.4111 | 7240 | 0.0192 |
| 0.4116 | 7250 | 0.0194 |
| 0.4122 | 7260 | 0.0187 |
| 0.4128 | 7270 | 0.0187 |
| 0.4133 | 7280 | 0.0193 |
| 0.4139 | 7290 | 0.0193 |
| 0.4145 | 7300 | 0.0188 |
| 0.4150 | 7310 | 0.0193 |
| 0.4156 | 7320 | 0.0194 |
| 0.4162 | 7330 | 0.0189 |
| 0.4167 | 7340 | 0.0195 |
| 0.4173 | 7350 | 0.0179 |
| 0.4179 | 7360 | 0.0192 |
| 0.4184 | 7370 | 0.0201 |
| 0.4190 | 7380 | 0.0197 |
| 0.4196 | 7390 | 0.0193 |
| 0.4201 | 7400 | 0.0191 |
| 0.4207 | 7410 | 0.0199 |
| 0.4213 | 7420 | 0.02 |
| 0.4218 | 7430 | 0.0195 |
| 0.4224 | 7440 | 0.0185 |
| 0.4230 | 7450 | 0.0185 |
| 0.4236 | 7460 | 0.02 |
| 0.4241 | 7470 | 0.0191 |
| 0.4247 | 7480 | 0.0196 |
| 0.4253 | 7490 | 0.0194 |
| 0.4258 | 7500 | 0.0187 |
| 0.4264 | 7510 | 0.0195 |
| 0.4270 | 7520 | 0.0203 |
| 0.4275 | 7530 | 0.0187 |
| 0.4281 | 7540 | 0.02 |
| 0.4287 | 7550 | 0.0189 |
| 0.4292 | 7560 | 0.0193 |
| 0.4298 | 7570 | 0.019 |
| 0.4304 | 7580 | 0.0189 |
| 0.4309 | 7590 | 0.0202 |
| 0.4315 | 7600 | 0.0192 |
| 0.4321 | 7610 | 0.0202 |
| 0.4326 | 7620 | 0.0191 |
| 0.4332 | 7630 | 0.0186 |
| 0.4338 | 7640 | 0.0195 |
| 0.4343 | 7650 | 0.0198 |
| 0.4349 | 7660 | 0.0185 |
| 0.4355 | 7670 | 0.0192 |
| 0.4360 | 7680 | 0.0184 |
| 0.4366 | 7690 | 0.0196 |
| 0.4372 | 7700 | 0.0195 |
| 0.4377 | 7710 | 0.0191 |
| 0.4383 | 7720 | 0.0192 |
| 0.4389 | 7730 | 0.0196 |
| 0.4394 | 7740 | 0.019 |
| 0.4400 | 7750 | 0.0197 |
| 0.4406 | 7760 | 0.0194 |
| 0.4412 | 7770 | 0.019 |
| 0.4417 | 7780 | 0.0197 |
| 0.4423 | 7790 | 0.0186 |
| 0.4429 | 7800 | 0.0189 |
| 0.4434 | 7810 | 0.0184 |
| 0.4440 | 7820 | 0.019 |
| 0.4446 | 7830 | 0.0182 |
| 0.4451 | 7840 | 0.0189 |
| 0.4457 | 7850 | 0.0178 |
| 0.4463 | 7860 | 0.0191 |
| 0.4468 | 7870 | 0.0188 |
| 0.4474 | 7880 | 0.0196 |
| 0.4480 | 7890 | 0.0188 |
| 0.4485 | 7900 | 0.0191 |
| 0.4491 | 7910 | 0.0183 |
| 0.4497 | 7920 | 0.0189 |
| 0.4502 | 7930 | 0.0192 |
| 0.4508 | 7940 | 0.0201 |
| 0.4514 | 7950 | 0.0192 |
| 0.4519 | 7960 | 0.0187 |
| 0.4525 | 7970 | 0.019 |
| 0.4531 | 7980 | 0.019 |
| 0.4536 | 7990 | 0.0187 |
| 0.4542 | 8000 | 0.0197 |
| 0.4548 | 8010 | 0.0189 |
| 0.4553 | 8020 | 0.0198 |
| 0.4559 | 8030 | 0.019 |
| 0.4565 | 8040 | 0.0193 |
| 0.4570 | 8050 | 0.0187 |
| 0.4576 | 8060 | 0.0195 |
| 0.4582 | 8070 | 0.0184 |
| 0.4588 | 8080 | 0.0199 |
| 0.4593 | 8090 | 0.02 |
| 0.4599 | 8100 | 0.0194 |
| 0.4605 | 8110 | 0.0198 |
| 0.4610 | 8120 | 0.0199 |
| 0.4616 | 8130 | 0.0191 |
| 0.4622 | 8140 | 0.019 |
| 0.4627 | 8150 | 0.0204 |
| 0.4633 | 8160 | 0.0189 |
| 0.4639 | 8170 | 0.0199 |
| 0.4644 | 8180 | 0.0187 |
| 0.4650 | 8190 | 0.0192 |
| 0.4656 | 8200 | 0.0192 |
| 0.4661 | 8210 | 0.0179 |
| 0.4667 | 8220 | 0.0198 |
| 0.4673 | 8230 | 0.0194 |
| 0.4678 | 8240 | 0.0191 |
| 0.4684 | 8250 | 0.0188 |
| 0.4690 | 8260 | 0.0193 |
| 0.4695 | 8270 | 0.0193 |
| 0.4701 | 8280 | 0.019 |
| 0.4707 | 8290 | 0.0197 |
| 0.4712 | 8300 | 0.018 |
| 0.4718 | 8310 | 0.0184 |
| 0.4724 | 8320 | 0.019 |
| 0.4729 | 8330 | 0.0195 |
| 0.4735 | 8340 | 0.0186 |
| 0.4741 | 8350 | 0.0186 |
| 0.4746 | 8360 | 0.0193 |
| 0.4752 | 8370 | 0.0187 |
| 0.4758 | 8380 | 0.0189 |
| 0.4764 | 8390 | 0.0194 |
| 0.4769 | 8400 | 0.0191 |
| 0.4775 | 8410 | 0.0185 |
| 0.4781 | 8420 | 0.0183 |
| 0.4786 | 8430 | 0.0181 |
| 0.4792 | 8440 | 0.0191 |
| 0.4798 | 8450 | 0.0196 |
| 0.4803 | 8460 | 0.0194 |
| 0.4809 | 8470 | 0.0188 |
| 0.4815 | 8480 | 0.019 |
| 0.4820 | 8490 | 0.0184 |
| 0.4826 | 8500 | 0.0192 |
| 0.4832 | 8510 | 0.0192 |
| 0.4837 | 8520 | 0.0191 |
| 0.4843 | 8530 | 0.0187 |
| 0.4849 | 8540 | 0.0192 |
| 0.4854 | 8550 | 0.019 |
| 0.4860 | 8560 | 0.0184 |
| 0.4866 | 8570 | 0.0186 |
| 0.4871 | 8580 | 0.0201 |
| 0.4877 | 8590 | 0.0189 |
| 0.4883 | 8600 | 0.0184 |
| 0.4888 | 8610 | 0.0194 |
| 0.4894 | 8620 | 0.0192 |
| 0.4900 | 8630 | 0.0188 |
| 0.4905 | 8640 | 0.0186 |
| 0.4911 | 8650 | 0.0193 |
| 0.4917 | 8660 | 0.0187 |
| 0.4923 | 8670 | 0.0189 |
| 0.4928 | 8680 | 0.019 |
| 0.4934 | 8690 | 0.0188 |
| 0.4940 | 8700 | 0.0194 |
| 0.4945 | 8710 | 0.0187 |
| 0.4951 | 8720 | 0.0183 |
| 0.4957 | 8730 | 0.0192 |
| 0.4962 | 8740 | 0.0183 |
| 0.4968 | 8750 | 0.0179 |
| 0.4974 | 8760 | 0.0184 |
| 0.4979 | 8770 | 0.0197 |
| 0.4985 | 8780 | 0.0191 |
| 0.4991 | 8790 | 0.0197 |
| 0.4996 | 8800 | 0.0206 |
| 0.5002 | 8810 | 0.0183 |
| 0.5008 | 8820 | 0.0186 |
| 0.5013 | 8830 | 0.0178 |
| 0.5019 | 8840 | 0.0185 |
| 0.5025 | 8850 | 0.0191 |
| 0.5030 | 8860 | 0.0189 |
| 0.5036 | 8870 | 0.0191 |
| 0.5042 | 8880 | 0.0186 |
| 0.5047 | 8890 | 0.0192 |
| 0.5053 | 8900 | 0.019 |
| 0.5059 | 8910 | 0.0194 |
| 0.5064 | 8920 | 0.0193 |
| 0.5070 | 8930 | 0.0184 |
| 0.5076 | 8940 | 0.0185 |
| 0.5081 | 8950 | 0.0189 |
| 0.5087 | 8960 | 0.0191 |
| 0.5093 | 8970 | 0.0177 |
| 0.5099 | 8980 | 0.0186 |
| 0.5104 | 8990 | 0.0195 |
| 0.5110 | 9000 | 0.0179 |
| 0.5116 | 9010 | 0.0177 |
| 0.5121 | 9020 | 0.0183 |
| 0.5127 | 9030 | 0.0191 |
| 0.5133 | 9040 | 0.0185 |
| 0.5138 | 9050 | 0.0196 |
| 0.5144 | 9060 | 0.019 |
| 0.5150 | 9070 | 0.0192 |
| 0.5155 | 9080 | 0.0195 |
| 0.5161 | 9090 | 0.0194 |
| 0.5167 | 9100 | 0.0191 |
| 0.5172 | 9110 | 0.0208 |
| 0.5178 | 9120 | 0.0192 |
| 0.5184 | 9130 | 0.0189 |
| 0.5189 | 9140 | 0.0179 |
| 0.5195 | 9150 | 0.0198 |
| 0.5201 | 9160 | 0.0185 |
| 0.5206 | 9170 | 0.0191 |
| 0.5212 | 9180 | 0.0185 |
| 0.5218 | 9190 | 0.0188 |
| 0.5223 | 9200 | 0.0191 |
| 0.5229 | 9210 | 0.0189 |
| 0.5235 | 9220 | 0.0186 |
| 0.5240 | 9230 | 0.0196 |
| 0.5246 | 9240 | 0.0193 |
| 0.5252 | 9250 | 0.0199 |
| 0.5257 | 9260 | 0.0199 |
| 0.5263 | 9270 | 0.0192 |
| 0.5269 | 9280 | 0.0187 |
| 0.5275 | 9290 | 0.0188 |
| 0.5280 | 9300 | 0.0181 |
| 0.5286 | 9310 | 0.0189 |
| 0.5292 | 9320 | 0.0193 |
| 0.5297 | 9330 | 0.0183 |
| 0.5303 | 9340 | 0.0194 |
| 0.5309 | 9350 | 0.017 |
| 0.5314 | 9360 | 0.0183 |
| 0.5320 | 9370 | 0.0192 |
| 0.5326 | 9380 | 0.0187 |
| 0.5331 | 9390 | 0.0184 |
| 0.5337 | 9400 | 0.0186 |
| 0.5343 | 9410 | 0.0191 |
| 0.5348 | 9420 | 0.0194 |
| 0.5354 | 9430 | 0.0187 |
| 0.5360 | 9440 | 0.0184 |
| 0.5365 | 9450 | 0.0193 |
| 0.5371 | 9460 | 0.0191 |
| 0.5377 | 9470 | 0.0188 |
| 0.5382 | 9480 | 0.0184 |
| 0.5388 | 9490 | 0.0184 |
| 0.5394 | 9500 | 0.0197 |
| 0.5399 | 9510 | 0.0183 |
| 0.5405 | 9520 | 0.0181 |
| 0.5411 | 9530 | 0.0188 |
| 0.5416 | 9540 | 0.0194 |
| 0.5422 | 9550 | 0.0186 |
| 0.5428 | 9560 | 0.0187 |
| 0.5433 | 9570 | 0.0187 |
| 0.5439 | 9580 | 0.0182 |
| 0.5445 | 9590 | 0.0188 |
| 0.5451 | 9600 | 0.019 |
| 0.5456 | 9610 | 0.0201 |
| 0.5462 | 9620 | 0.0191 |
| 0.5468 | 9630 | 0.0188 |
| 0.5473 | 9640 | 0.0187 |
| 0.5479 | 9650 | 0.0189 |
| 0.5485 | 9660 | 0.0193 |
| 0.5490 | 9670 | 0.0182 |
| 0.5496 | 9680 | 0.0193 |
| 0.5502 | 9690 | 0.0196 |
| 0.5507 | 9700 | 0.0184 |
| 0.5513 | 9710 | 0.0187 |
| 0.5519 | 9720 | 0.0191 |
| 0.5524 | 9730 | 0.0185 |
| 0.5530 | 9740 | 0.0189 |
| 0.5536 | 9750 | 0.0186 |
| 0.5541 | 9760 | 0.0194 |
| 0.5547 | 9770 | 0.0189 |
| 0.5553 | 9780 | 0.0187 |
| 0.5558 | 9790 | 0.0187 |
| 0.5564 | 9800 | 0.0185 |
| 0.5570 | 9810 | 0.0179 |
| 0.5575 | 9820 | 0.0197 |
| 0.5581 | 9830 | 0.0182 |
| 0.5587 | 9840 | 0.018 |
| 0.5592 | 9850 | 0.0187 |
| 0.5598 | 9860 | 0.0186 |
| 0.5604 | 9870 | 0.0188 |
| 0.5609 | 9880 | 0.0181 |
| 0.5615 | 9890 | 0.0186 |
| 0.5621 | 9900 | 0.0197 |
| 0.5627 | 9910 | 0.0191 |
| 0.5632 | 9920 | 0.019 |
| 0.5638 | 9930 | 0.019 |
| 0.5644 | 9940 | 0.019 |
| 0.5649 | 9950 | 0.0192 |
| 0.5655 | 9960 | 0.0177 |
| 0.5661 | 9970 | 0.0184 |
| 0.5666 | 9980 | 0.0186 |
| 0.5672 | 9990 | 0.0187 |
| 0.5678 | 10000 | 0.0181 |
| 0.5683 | 10010 | 0.0189 |
| 0.5689 | 10020 | 0.0194 |
| 0.5695 | 10030 | 0.0181 |
| 0.5700 | 10040 | 0.0184 |
| 0.5706 | 10050 | 0.0175 |
| 0.5712 | 10060 | 0.0181 |
| 0.5717 | 10070 | 0.0187 |
| 0.5723 | 10080 | 0.0194 |
| 0.5729 | 10090 | 0.0188 |
| 0.5734 | 10100 | 0.0185 |
| 0.5740 | 10110 | 0.019 |
| 0.5746 | 10120 | 0.0192 |
| 0.5751 | 10130 | 0.0192 |
| 0.5757 | 10140 | 0.0185 |
| 0.5763 | 10150 | 0.0178 |
| 0.5768 | 10160 | 0.0179 |
| 0.5774 | 10170 | 0.0194 |
| 0.5780 | 10180 | 0.0196 |
| 0.5785 | 10190 | 0.0183 |
| 0.5791 | 10200 | 0.0184 |
| 0.5797 | 10210 | 0.0184 |
| 0.5803 | 10220 | 0.0192 |
| 0.5808 | 10230 | 0.0193 |
| 0.5814 | 10240 | 0.0185 |
| 0.5820 | 10250 | 0.0192 |
| 0.5825 | 10260 | 0.0195 |
| 0.5831 | 10270 | 0.0192 |
| 0.5837 | 10280 | 0.0189 |
| 0.5842 | 10290 | 0.0183 |
| 0.5848 | 10300 | 0.019 |
| 0.5854 | 10310 | 0.0188 |
| 0.5859 | 10320 | 0.019 |
| 0.5865 | 10330 | 0.0192 |
| 0.5871 | 10340 | 0.0185 |
| 0.5876 | 10350 | 0.0193 |
| 0.5882 | 10360 | 0.0183 |
| 0.5888 | 10370 | 0.0196 |
| 0.5893 | 10380 | 0.0187 |
| 0.5899 | 10390 | 0.0185 |
| 0.5905 | 10400 | 0.0186 |
| 0.5910 | 10410 | 0.0182 |
| 0.5916 | 10420 | 0.0193 |
| 0.5922 | 10430 | 0.019 |
| 0.5927 | 10440 | 0.0186 |
| 0.5933 | 10450 | 0.0184 |
| 0.5939 | 10460 | 0.0192 |
| 0.5944 | 10470 | 0.0173 |
| 0.5950 | 10480 | 0.0184 |
| 0.5956 | 10490 | 0.0186 |
| 0.5962 | 10500 | 0.0176 |
| 0.5967 | 10510 | 0.0183 |
| 0.5973 | 10520 | 0.0183 |
| 0.5979 | 10530 | 0.0182 |
| 0.5984 | 10540 | 0.0191 |
| 0.5990 | 10550 | 0.0188 |
| 0.5996 | 10560 | 0.0191 |
| 0.6001 | 10570 | 0.0189 |
| 0.6007 | 10580 | 0.0183 |
| 0.6013 | 10590 | 0.0178 |
| 0.6018 | 10600 | 0.0175 |
| 0.6024 | 10610 | 0.0204 |
| 0.6030 | 10620 | 0.019 |
| 0.6035 | 10630 | 0.0189 |
| 0.6041 | 10640 | 0.0183 |
| 0.6047 | 10650 | 0.0188 |
| 0.6052 | 10660 | 0.0191 |
| 0.6058 | 10670 | 0.0193 |
| 0.6064 | 10680 | 0.0182 |
| 0.6069 | 10690 | 0.019 |
| 0.6075 | 10700 | 0.0183 |
| 0.6081 | 10710 | 0.0183 |
| 0.6086 | 10720 | 0.0186 |
| 0.6092 | 10730 | 0.0189 |
| 0.6098 | 10740 | 0.0175 |
| 0.6103 | 10750 | 0.0187 |
| 0.6109 | 10760 | 0.0186 |
| 0.6115 | 10770 | 0.0192 |
| 0.6120 | 10780 | 0.0186 |
| 0.6126 | 10790 | 0.0181 |
| 0.6132 | 10800 | 0.0188 |
| 0.6138 | 10810 | 0.0176 |
| 0.6143 | 10820 | 0.019 |
| 0.6149 | 10830 | 0.0176 |
| 0.6155 | 10840 | 0.0185 |
| 0.6160 | 10850 | 0.019 |
| 0.6166 | 10860 | 0.0185 |
| 0.6172 | 10870 | 0.019 |
| 0.6177 | 10880 | 0.018 |
| 0.6183 | 10890 | 0.0185 |
| 0.6189 | 10900 | 0.0185 |
| 0.6194 | 10910 | 0.0182 |
| 0.6200 | 10920 | 0.0185 |
| 0.6206 | 10930 | 0.0177 |
| 0.6211 | 10940 | 0.0186 |
| 0.6217 | 10950 | 0.0184 |
| 0.6223 | 10960 | 0.0187 |
| 0.6228 | 10970 | 0.018 |
| 0.6234 | 10980 | 0.0186 |
| 0.6240 | 10990 | 0.0184 |
| 0.6245 | 11000 | 0.0188 |
| 0.6251 | 11010 | 0.0187 |
| 0.6257 | 11020 | 0.0188 |
| 0.6262 | 11030 | 0.0189 |
| 0.6268 | 11040 | 0.019 |
| 0.6274 | 11050 | 0.0187 |
| 0.6279 | 11060 | 0.018 |
| 0.6285 | 11070 | 0.019 |
| 0.6291 | 11080 | 0.0185 |
| 0.6296 | 11090 | 0.0183 |
| 0.6302 | 11100 | 0.0184 |
| 0.6308 | 11110 | 0.0186 |
| 0.6314 | 11120 | 0.0187 |
| 0.6319 | 11130 | 0.0179 |
| 0.6325 | 11140 | 0.0183 |
| 0.6331 | 11150 | 0.0172 |
| 0.6336 | 11160 | 0.0181 |
| 0.6342 | 11170 | 0.0182 |
| 0.6348 | 11180 | 0.0194 |
| 0.6353 | 11190 | 0.018 |
| 0.6359 | 11200 | 0.0197 |
| 0.6365 | 11210 | 0.0181 |
| 0.6370 | 11220 | 0.0182 |
| 0.6376 | 11230 | 0.0187 |
| 0.6382 | 11240 | 0.019 |
| 0.6387 | 11250 | 0.0186 |
| 0.6393 | 11260 | 0.0181 |
| 0.6399 | 11270 | 0.0193 |
| 0.6404 | 11280 | 0.0193 |
| 0.6410 | 11290 | 0.0174 |
| 0.6416 | 11300 | 0.0184 |
| 0.6421 | 11310 | 0.0189 |
| 0.6427 | 11320 | 0.0182 |
| 0.6433 | 11330 | 0.0184 |
| 0.6438 | 11340 | 0.0181 |
| 0.6444 | 11350 | 0.0183 |
| 0.6450 | 11360 | 0.0191 |
| 0.6455 | 11370 | 0.018 |
| 0.6461 | 11380 | 0.0185 |
| 0.6467 | 11390 | 0.0189 |
| 0.6472 | 11400 | 0.0185 |
| 0.6478 | 11410 | 0.0183 |
| 0.6484 | 11420 | 0.0183 |
| 0.6490 | 11430 | 0.0186 |
| 0.6495 | 11440 | 0.0179 |
| 0.6501 | 11450 | 0.0174 |
| 0.6507 | 11460 | 0.0181 |
| 0.6512 | 11470 | 0.0175 |
| 0.6518 | 11480 | 0.0179 |
| 0.6524 | 11490 | 0.0183 |
| 0.6529 | 11500 | 0.0179 |
| 0.6535 | 11510 | 0.0184 |
| 0.6541 | 11520 | 0.0186 |
| 0.6546 | 11530 | 0.018 |
| 0.6552 | 11540 | 0.0183 |
| 0.6558 | 11550 | 0.0183 |
| 0.6563 | 11560 | 0.0185 |
| 0.6569 | 11570 | 0.0181 |
| 0.6575 | 11580 | 0.0181 |
| 0.6580 | 11590 | 0.0188 |
| 0.6586 | 11600 | 0.0188 |
| 0.6592 | 11610 | 0.0182 |
| 0.6597 | 11620 | 0.0188 |
| 0.6603 | 11630 | 0.0194 |
| 0.6609 | 11640 | 0.0185 |
| 0.6614 | 11650 | 0.0194 |
| 0.6620 | 11660 | 0.019 |
| 0.6626 | 11670 | 0.0186 |
| 0.6631 | 11680 | 0.0181 |
| 0.6637 | 11690 | 0.0181 |
| 0.6643 | 11700 | 0.0183 |
| 0.6648 | 11710 | 0.0187 |
| 0.6654 | 11720 | 0.0184 |
| 0.6660 | 11730 | 0.0183 |
| 0.6666 | 11740 | 0.0177 |
| 0.6671 | 11750 | 0.018 |
| 0.6677 | 11760 | 0.0182 |
| 0.6683 | 11770 | 0.0184 |
| 0.6688 | 11780 | 0.0185 |
| 0.6694 | 11790 | 0.0191 |
| 0.6700 | 11800 | 0.0187 |
| 0.6705 | 11810 | 0.018 |
| 0.6711 | 11820 | 0.0187 |
| 0.6717 | 11830 | 0.0178 |
| 0.6722 | 11840 | 0.0178 |
| 0.6728 | 11850 | 0.0192 |
| 0.6734 | 11860 | 0.0186 |
| 0.6739 | 11870 | 0.019 |
| 0.6745 | 11880 | 0.0188 |
| 0.6751 | 11890 | 0.0185 |
| 0.6756 | 11900 | 0.0188 |
| 0.6762 | 11910 | 0.0176 |
| 0.6768 | 11920 | 0.0186 |
| 0.6773 | 11930 | 0.0188 |
| 0.6779 | 11940 | 0.0183 |
| 0.6785 | 11950 | 0.02 |
| 0.6790 | 11960 | 0.0192 |
| 0.6796 | 11970 | 0.0184 |
| 0.6802 | 11980 | 0.0175 |
| 0.6807 | 11990 | 0.0179 |
| 0.6813 | 12000 | 0.0185 |
| 0.6819 | 12010 | 0.0185 |
| 0.6825 | 12020 | 0.019 |
| 0.6830 | 12030 | 0.02 |
| 0.6836 | 12040 | 0.018 |
| 0.6842 | 12050 | 0.0181 |
| 0.6847 | 12060 | 0.0169 |
| 0.6853 | 12070 | 0.018 |
| 0.6859 | 12080 | 0.0182 |
| 0.6864 | 12090 | 0.019 |
| 0.6870 | 12100 | 0.0182 |
| 0.6876 | 12110 | 0.0183 |
| 0.6881 | 12120 | 0.0175 |
| 0.6887 | 12130 | 0.0182 |
| 0.6893 | 12140 | 0.0196 |
| 0.6898 | 12150 | 0.0185 |
| 0.6904 | 12160 | 0.0179 |
| 0.6910 | 12170 | 0.0193 |
| 0.6915 | 12180 | 0.0177 |
| 0.6921 | 12190 | 0.0188 |
| 0.6927 | 12200 | 0.0197 |
| 0.6932 | 12210 | 0.0199 |
| 0.6938 | 12220 | 0.018 |
| 0.6944 | 12230 | 0.0171 |
| 0.6949 | 12240 | 0.0178 |
| 0.6955 | 12250 | 0.0197 |
| 0.6961 | 12260 | 0.0191 |
| 0.6966 | 12270 | 0.0175 |
| 0.6972 | 12280 | 0.0178 |
| 0.6978 | 12290 | 0.0191 |
| 0.6983 | 12300 | 0.0185 |
| 0.6989 | 12310 | 0.0194 |
| 0.6995 | 12320 | 0.0178 |
| 0.7001 | 12330 | 0.018 |
| 0.7006 | 12340 | 0.0179 |
| 0.7012 | 12350 | 0.0176 |
| 0.7018 | 12360 | 0.0188 |
| 0.7023 | 12370 | 0.0177 |
| 0.7029 | 12380 | 0.0179 |
| 0.7035 | 12390 | 0.0194 |
| 0.7040 | 12400 | 0.0181 |
| 0.7046 | 12410 | 0.018 |
| 0.7052 | 12420 | 0.0188 |
| 0.7057 | 12430 | 0.0184 |
| 0.7063 | 12440 | 0.0178 |
| 0.7069 | 12450 | 0.0175 |
| 0.7074 | 12460 | 0.0182 |
| 0.7080 | 12470 | 0.0189 |
| 0.7086 | 12480 | 0.0182 |
| 0.7091 | 12490 | 0.0193 |
| 0.7097 | 12500 | 0.0187 |
| 0.7103 | 12510 | 0.0179 |
| 0.7108 | 12520 | 0.0182 |
| 0.7114 | 12530 | 0.0194 |
| 0.7120 | 12540 | 0.018 |
| 0.7125 | 12550 | 0.0184 |
| 0.7131 | 12560 | 0.0176 |
| 0.7137 | 12570 | 0.0192 |
| 0.7142 | 12580 | 0.0186 |
| 0.7148 | 12590 | 0.0178 |
| 0.7154 | 12600 | 0.0196 |
| 0.7159 | 12610 | 0.0187 |
| 0.7165 | 12620 | 0.0183 |
| 0.7171 | 12630 | 0.0192 |
| 0.7177 | 12640 | 0.0192 |
| 0.7182 | 12650 | 0.0182 |
| 0.7188 | 12660 | 0.0192 |
| 0.7194 | 12670 | 0.0189 |
| 0.7199 | 12680 | 0.018 |
| 0.7205 | 12690 | 0.0182 |
| 0.7211 | 12700 | 0.0185 |
| 0.7216 | 12710 | 0.0187 |
| 0.7222 | 12720 | 0.0186 |
| 0.7228 | 12730 | 0.0185 |
| 0.7233 | 12740 | 0.0182 |
| 0.7239 | 12750 | 0.0182 |
| 0.7245 | 12760 | 0.0181 |
| 0.7250 | 12770 | 0.0191 |
| 0.7256 | 12780 | 0.0184 |
| 0.7262 | 12790 | 0.0185 |
| 0.7267 | 12800 | 0.0182 |
| 0.7273 | 12810 | 0.0187 |
| 0.7279 | 12820 | 0.0189 |
| 0.7284 | 12830 | 0.0183 |
| 0.7290 | 12840 | 0.0178 |
| 0.7296 | 12850 | 0.0178 |
| 0.7301 | 12860 | 0.0182 |
| 0.7307 | 12870 | 0.0186 |
| 0.7313 | 12880 | 0.0189 |
| 0.7318 | 12890 | 0.0186 |
| 0.7324 | 12900 | 0.0193 |
| 0.7330 | 12910 | 0.0185 |
| 0.7335 | 12920 | 0.0172 |
| 0.7341 | 12930 | 0.0177 |
| 0.7347 | 12940 | 0.0186 |
| 0.7353 | 12950 | 0.0179 |
| 0.7358 | 12960 | 0.0189 |
| 0.7364 | 12970 | 0.0185 |
| 0.7370 | 12980 | 0.0174 |
| 0.7375 | 12990 | 0.0181 |
| 0.7381 | 13000 | 0.0185 |
| 0.7387 | 13010 | 0.0184 |
| 0.7392 | 13020 | 0.0176 |
| 0.7398 | 13030 | 0.019 |
| 0.7404 | 13040 | 0.0184 |
| 0.7409 | 13050 | 0.0177 |
| 0.7415 | 13060 | 0.0175 |
| 0.7421 | 13070 | 0.0201 |
| 0.7426 | 13080 | 0.0195 |
| 0.7432 | 13090 | 0.0188 |
| 0.7438 | 13100 | 0.0192 |
| 0.7443 | 13110 | 0.0168 |
| 0.7449 | 13120 | 0.0176 |
| 0.7455 | 13130 | 0.0192 |
| 0.7460 | 13140 | 0.0188 |
| 0.7466 | 13150 | 0.0184 |
| 0.7472 | 13160 | 0.0173 |
| 0.7477 | 13170 | 0.0184 |
| 0.7483 | 13180 | 0.0195 |
| 0.7489 | 13190 | 0.0174 |
| 0.7494 | 13200 | 0.0177 |
| 0.7500 | 13210 | 0.0183 |
| 0.7506 | 13220 | 0.0179 |
| 0.7511 | 13230 | 0.0186 |
| 0.7517 | 13240 | 0.0172 |
| 0.7523 | 13250 | 0.0188 |
| 0.7529 | 13260 | 0.0194 |
| 0.7534 | 13270 | 0.0185 |
| 0.7540 | 13280 | 0.0172 |
| 0.7546 | 13290 | 0.0182 |
| 0.7551 | 13300 | 0.0174 |
| 0.7557 | 13310 | 0.0183 |
| 0.7563 | 13320 | 0.0176 |
| 0.7568 | 13330 | 0.0187 |
| 0.7574 | 13340 | 0.0191 |
| 0.7580 | 13350 | 0.0175 |
| 0.7585 | 13360 | 0.0184 |
| 0.7591 | 13370 | 0.0182 |
| 0.7597 | 13380 | 0.0179 |
| 0.7602 | 13390 | 0.0183 |
| 0.7608 | 13400 | 0.0185 |
| 0.7614 | 13410 | 0.0188 |
| 0.7619 | 13420 | 0.0178 |
| 0.7625 | 13430 | 0.0181 |
| 0.7631 | 13440 | 0.0176 |
| 0.7636 | 13450 | 0.0189 |
| 0.7642 | 13460 | 0.0186 |
| 0.7648 | 13470 | 0.0186 |
| 0.7653 | 13480 | 0.0186 |
| 0.7659 | 13490 | 0.0186 |
| 0.7665 | 13500 | 0.0186 |
| 0.7670 | 13510 | 0.0183 |
| 0.7676 | 13520 | 0.0184 |
| 0.7682 | 13530 | 0.0178 |
| 0.7688 | 13540 | 0.0185 |
| 0.7693 | 13550 | 0.0178 |
| 0.7699 | 13560 | 0.0171 |
| 0.7705 | 13570 | 0.018 |
| 0.7710 | 13580 | 0.0181 |
| 0.7716 | 13590 | 0.0182 |
| 0.7722 | 13600 | 0.0179 |
| 0.7727 | 13610 | 0.018 |
| 0.7733 | 13620 | 0.0191 |
| 0.7739 | 13630 | 0.0183 |
| 0.7744 | 13640 | 0.0181 |
| 0.7750 | 13650 | 0.0176 |
| 0.7756 | 13660 | 0.0195 |
| 0.7761 | 13670 | 0.0192 |
| 0.7767 | 13680 | 0.0169 |
| 0.7773 | 13690 | 0.018 |
| 0.7778 | 13700 | 0.0175 |
| 0.7784 | 13710 | 0.019 |
| 0.7790 | 13720 | 0.0184 |
| 0.7795 | 13730 | 0.0194 |
| 0.7801 | 13740 | 0.0183 |
| 0.7807 | 13750 | 0.0182 |
| 0.7812 | 13760 | 0.0174 |
| 0.7818 | 13770 | 0.0185 |
| 0.7824 | 13780 | 0.018 |
| 0.7829 | 13790 | 0.0178 |
| 0.7835 | 13800 | 0.0179 |
| 0.7841 | 13810 | 0.019 |
| 0.7846 | 13820 | 0.0174 |
| 0.7852 | 13830 | 0.0178 |
| 0.7858 | 13840 | 0.0184 |
| 0.7864 | 13850 | 0.0177 |
| 0.7869 | 13860 | 0.0186 |
| 0.7875 | 13870 | 0.0175 |
| 0.7881 | 13880 | 0.0183 |
| 0.7886 | 13890 | 0.0179 |
| 0.7892 | 13900 | 0.0197 |
| 0.7898 | 13910 | 0.0183 |
| 0.7903 | 13920 | 0.0172 |
| 0.7909 | 13930 | 0.019 |
| 0.7915 | 13940 | 0.0184 |
| 0.7920 | 13950 | 0.0187 |
| 0.7926 | 13960 | 0.0175 |
| 0.7932 | 13970 | 0.0175 |
| 0.7937 | 13980 | 0.0184 |
| 0.7943 | 13990 | 0.019 |
| 0.7949 | 14000 | 0.0183 |
| 0.7954 | 14010 | 0.018 |
| 0.7960 | 14020 | 0.0191 |
| 0.7966 | 14030 | 0.0186 |
| 0.7971 | 14040 | 0.0179 |
| 0.7977 | 14050 | 0.0176 |
| 0.7983 | 14060 | 0.0181 |
| 0.7988 | 14070 | 0.0187 |
| 0.7994 | 14080 | 0.0186 |
| 0.8000 | 14090 | 0.0176 |
| 0.8005 | 14100 | 0.0185 |
| 0.8011 | 14110 | 0.0184 |
| 0.8017 | 14120 | 0.0189 |
| 0.8022 | 14130 | 0.018 |
| 0.8028 | 14140 | 0.018 |
| 0.8034 | 14150 | 0.0165 |
| 0.8040 | 14160 | 0.0181 |
| 0.8045 | 14170 | 0.0188 |
| 0.8051 | 14180 | 0.0176 |
| 0.8057 | 14190 | 0.0173 |
| 0.8062 | 14200 | 0.0182 |
| 0.8068 | 14210 | 0.0182 |
| 0.8074 | 14220 | 0.0181 |
| 0.8079 | 14230 | 0.0177 |
| 0.8085 | 14240 | 0.0181 |
| 0.8091 | 14250 | 0.0192 |
| 0.8096 | 14260 | 0.0182 |
| 0.8102 | 14270 | 0.0192 |
| 0.8108 | 14280 | 0.0176 |
| 0.8113 | 14290 | 0.0177 |
| 0.8119 | 14300 | 0.0173 |
| 0.8125 | 14310 | 0.0179 |
| 0.8130 | 14320 | 0.0185 |
| 0.8136 | 14330 | 0.0174 |
| 0.8142 | 14340 | 0.018 |
| 0.8147 | 14350 | 0.0181 |
| 0.8153 | 14360 | 0.0183 |
| 0.8159 | 14370 | 0.0185 |
| 0.8164 | 14380 | 0.0183 |
| 0.8170 | 14390 | 0.0179 |
| 0.8176 | 14400 | 0.0181 |
| 0.8181 | 14410 | 0.0189 |
| 0.8187 | 14420 | 0.0191 |
| 0.8193 | 14430 | 0.0185 |
| 0.8198 | 14440 | 0.0179 |
| 0.8204 | 14450 | 0.0182 |
| 0.8210 | 14460 | 0.0177 |
| 0.8216 | 14470 | 0.0181 |
| 0.8221 | 14480 | 0.0181 |
| 0.8227 | 14490 | 0.0183 |
| 0.8233 | 14500 | 0.0176 |
| 0.8238 | 14510 | 0.0178 |
| 0.8244 | 14520 | 0.0177 |
| 0.8250 | 14530 | 0.0193 |
| 0.8255 | 14540 | 0.0179 |
| 0.8261 | 14550 | 0.0182 |
| 0.8267 | 14560 | 0.0192 |
| 0.8272 | 14570 | 0.0188 |
| 0.8278 | 14580 | 0.0188 |
| 0.8284 | 14590 | 0.0188 |
| 0.8289 | 14600 | 0.0187 |
| 0.8295 | 14610 | 0.0177 |
| 0.8301 | 14620 | 0.0178 |
| 0.8306 | 14630 | 0.0174 |
| 0.8312 | 14640 | 0.0178 |
| 0.8318 | 14650 | 0.0184 |
| 0.8323 | 14660 | 0.018 |
| 0.8329 | 14670 | 0.0177 |
| 0.8335 | 14680 | 0.019 |
| 0.8340 | 14690 | 0.0187 |
| 0.8346 | 14700 | 0.0176 |
| 0.8352 | 14710 | 0.0181 |
| 0.8357 | 14720 | 0.0183 |
| 0.8363 | 14730 | 0.0182 |
| 0.8369 | 14740 | 0.0188 |
| 0.8374 | 14750 | 0.0184 |
| 0.8380 | 14760 | 0.0177 |
| 0.8386 | 14770 | 0.0176 |
| 0.8392 | 14780 | 0.0179 |
| 0.8397 | 14790 | 0.0181 |
| 0.8403 | 14800 | 0.0185 |
| 0.8409 | 14810 | 0.0177 |
| 0.8414 | 14820 | 0.0184 |
| 0.8420 | 14830 | 0.0171 |
| 0.8426 | 14840 | 0.0186 |
| 0.8431 | 14850 | 0.0183 |
| 0.8437 | 14860 | 0.018 |
| 0.8443 | 14870 | 0.0192 |
| 0.8448 | 14880 | 0.0183 |
| 0.8454 | 14890 | 0.017 |
| 0.8460 | 14900 | 0.0175 |
| 0.8465 | 14910 | 0.0188 |
| 0.8471 | 14920 | 0.0168 |
| 0.8477 | 14930 | 0.0175 |
| 0.8482 | 14940 | 0.0165 |
| 0.8488 | 14950 | 0.0183 |
| 0.8494 | 14960 | 0.0184 |
| 0.8499 | 14970 | 0.0181 |
| 0.8505 | 14980 | 0.0184 |
| 0.8511 | 14990 | 0.0188 |
| 0.8516 | 15000 | 0.0183 |
| 0.8522 | 15010 | 0.0177 |
| 0.8528 | 15020 | 0.0185 |
| 0.8533 | 15030 | 0.0179 |
| 0.8539 | 15040 | 0.0171 |
| 0.8545 | 15050 | 0.0175 |
| 0.8551 | 15060 | 0.018 |
| 0.8556 | 15070 | 0.0181 |
| 0.8562 | 15080 | 0.019 |
| 0.8568 | 15090 | 0.0185 |
| 0.8573 | 15100 | 0.0185 |
| 0.8579 | 15110 | 0.0175 |
| 0.8585 | 15120 | 0.0178 |
| 0.8590 | 15130 | 0.0184 |
| 0.8596 | 15140 | 0.018 |
| 0.8602 | 15150 | 0.0188 |
| 0.8607 | 15160 | 0.0188 |
| 0.8613 | 15170 | 0.0179 |
| 0.8619 | 15180 | 0.0189 |
| 0.8624 | 15190 | 0.0182 |
| 0.8630 | 15200 | 0.0188 |
| 0.8636 | 15210 | 0.0168 |
| 0.8641 | 15220 | 0.0189 |
| 0.8647 | 15230 | 0.0182 |
| 0.8653 | 15240 | 0.0183 |
| 0.8658 | 15250 | 0.0188 |
| 0.8664 | 15260 | 0.0182 |
| 0.8670 | 15270 | 0.0176 |
| 0.8675 | 15280 | 0.0195 |
| 0.8681 | 15290 | 0.0185 |
| 0.8687 | 15300 | 0.0178 |
| 0.8692 | 15310 | 0.0174 |
| 0.8698 | 15320 | 0.0177 |
| 0.8704 | 15330 | 0.0182 |
| 0.8709 | 15340 | 0.018 |
| 0.8715 | 15350 | 0.018 |
| 0.8721 | 15360 | 0.0178 |
| 0.8727 | 15370 | 0.0186 |
| 0.8732 | 15380 | 0.0181 |
| 0.8738 | 15390 | 0.018 |
| 0.8744 | 15400 | 0.0175 |
| 0.8749 | 15410 | 0.019 |
| 0.8755 | 15420 | 0.0175 |
| 0.8761 | 15430 | 0.0184 |
| 0.8766 | 15440 | 0.0179 |
| 0.8772 | 15450 | 0.0181 |
| 0.8778 | 15460 | 0.0172 |
| 0.8783 | 15470 | 0.0183 |
| 0.8789 | 15480 | 0.0183 |
| 0.8795 | 15490 | 0.0177 |
| 0.8800 | 15500 | 0.0186 |
| 0.8806 | 15510 | 0.0183 |
| 0.8812 | 15520 | 0.0185 |
| 0.8817 | 15530 | 0.0187 |
| 0.8823 | 15540 | 0.0182 |
| 0.8829 | 15550 | 0.0174 |
| 0.8834 | 15560 | 0.0179 |
| 0.8840 | 15570 | 0.017 |
| 0.8846 | 15580 | 0.0194 |
| 0.8851 | 15590 | 0.0188 |
| 0.8857 | 15600 | 0.0183 |
| 0.8863 | 15610 | 0.0183 |
| 0.8868 | 15620 | 0.0176 |
| 0.8874 | 15630 | 0.0176 |
| 0.8880 | 15640 | 0.0172 |
| 0.8885 | 15650 | 0.017 |
| 0.8891 | 15660 | 0.0173 |
| 0.8897 | 15670 | 0.0173 |
| 0.8903 | 15680 | 0.0182 |
| 0.8908 | 15690 | 0.0171 |
| 0.8914 | 15700 | 0.0175 |
| 0.8920 | 15710 | 0.0198 |
| 0.8925 | 15720 | 0.0177 |
| 0.8931 | 15730 | 0.0176 |
| 0.8937 | 15740 | 0.0178 |
| 0.8942 | 15750 | 0.0181 |
| 0.8948 | 15760 | 0.0176 |
| 0.8954 | 15770 | 0.0173 |
| 0.8959 | 15780 | 0.0183 |
| 0.8965 | 15790 | 0.0183 |
| 0.8971 | 15800 | 0.0181 |
| 0.8976 | 15810 | 0.0178 |
| 0.8982 | 15820 | 0.0183 |
| 0.8988 | 15830 | 0.0168 |
| 0.8993 | 15840 | 0.0186 |
| 0.8999 | 15850 | 0.0179 |
| 0.9005 | 15860 | 0.0188 |
| 0.9010 | 15870 | 0.018 |
| 0.9016 | 15880 | 0.0187 |
| 0.9022 | 15890 | 0.0173 |
| 0.9027 | 15900 | 0.0177 |
| 0.9033 | 15910 | 0.0174 |
| 0.9039 | 15920 | 0.0177 |
| 0.9044 | 15930 | 0.0185 |
| 0.9050 | 15940 | 0.0187 |
| 0.9056 | 15950 | 0.0174 |
| 0.9061 | 15960 | 0.0179 |
| 0.9067 | 15970 | 0.0181 |
| 0.9073 | 15980 | 0.0185 |
| 0.9079 | 15990 | 0.0184 |
| 0.9084 | 16000 | 0.0177 |
| 0.9090 | 16010 | 0.018 |
| 0.9096 | 16020 | 0.0187 |
| 0.9101 | 16030 | 0.0185 |
| 0.9107 | 16040 | 0.0184 |
| 0.9113 | 16050 | 0.017 |
| 0.9118 | 16060 | 0.0178 |
| 0.9124 | 16070 | 0.0173 |
| 0.9130 | 16080 | 0.0172 |
| 0.9135 | 16090 | 0.0188 |
| 0.9141 | 16100 | 0.0191 |
| 0.9147 | 16110 | 0.0176 |
| 0.9152 | 16120 | 0.0179 |
| 0.9158 | 16130 | 0.0188 |
| 0.9164 | 16140 | 0.0175 |
| 0.9169 | 16150 | 0.0186 |
| 0.9175 | 16160 | 0.0182 |
| 0.9181 | 16170 | 0.0178 |
| 0.9186 | 16180 | 0.0176 |
| 0.9192 | 16190 | 0.0171 |
| 0.9198 | 16200 | 0.0187 |
| 0.9203 | 16210 | 0.0177 |
| 0.9209 | 16220 | 0.0183 |
| 0.9215 | 16230 | 0.0182 |
| 0.9220 | 16240 | 0.0188 |
| 0.9226 | 16250 | 0.0179 |
| 0.9232 | 16260 | 0.0188 |
| 0.9237 | 16270 | 0.0181 |
| 0.9243 | 16280 | 0.018 |
| 0.9249 | 16290 | 0.0172 |
| 0.9255 | 16300 | 0.0174 |
| 0.9260 | 16310 | 0.0176 |
| 0.9266 | 16320 | 0.0188 |
| 0.9272 | 16330 | 0.0187 |
| 0.9277 | 16340 | 0.0181 |
| 0.9283 | 16350 | 0.0183 |
| 0.9289 | 16360 | 0.0181 |
| 0.9294 | 16370 | 0.0175 |
| 0.9300 | 16380 | 0.0192 |
| 0.9306 | 16390 | 0.0178 |
| 0.9311 | 16400 | 0.0175 |
| 0.9317 | 16410 | 0.0173 |
| 0.9323 | 16420 | 0.0187 |
| 0.9328 | 16430 | 0.0178 |
| 0.9334 | 16440 | 0.0176 |
| 0.9340 | 16450 | 0.0184 |
| 0.9345 | 16460 | 0.019 |
| 0.9351 | 16470 | 0.0186 |
| 0.9357 | 16480 | 0.0177 |
| 0.9362 | 16490 | 0.0177 |
| 0.9368 | 16500 | 0.0177 |
| 0.9374 | 16510 | 0.0189 |
| 0.9379 | 16520 | 0.0182 |
| 0.9385 | 16530 | 0.0178 |
| 0.9391 | 16540 | 0.0174 |
| 0.9396 | 16550 | 0.0187 |
| 0.9402 | 16560 | 0.0184 |
| 0.9408 | 16570 | 0.0184 |
| 0.9414 | 16580 | 0.0177 |
| 0.9419 | 16590 | 0.0176 |
| 0.9425 | 16600 | 0.0179 |
| 0.9431 | 16610 | 0.0183 |
| 0.9436 | 16620 | 0.0178 |
| 0.9442 | 16630 | 0.0182 |
| 0.9448 | 16640 | 0.0178 |
| 0.9453 | 16650 | 0.0187 |
| 0.9459 | 16660 | 0.0186 |
| 0.9465 | 16670 | 0.0176 |
| 0.9470 | 16680 | 0.0194 |
| 0.9476 | 16690 | 0.018 |
| 0.9482 | 16700 | 0.0171 |
| 0.9487 | 16710 | 0.0186 |
| 0.9493 | 16720 | 0.0179 |
| 0.9499 | 16730 | 0.018 |
| 0.9504 | 16740 | 0.0181 |
| 0.9510 | 16750 | 0.0184 |
| 0.9516 | 16760 | 0.0186 |
| 0.9521 | 16770 | 0.0179 |
| 0.9527 | 16780 | 0.0179 |
| 0.9533 | 16790 | 0.0198 |
| 0.9538 | 16800 | 0.0191 |
| 0.9544 | 16810 | 0.0181 |
| 0.9550 | 16820 | 0.0173 |
| 0.9555 | 16830 | 0.0171 |
| 0.9561 | 16840 | 0.0177 |
| 0.9567 | 16850 | 0.0178 |
| 0.9572 | 16860 | 0.0176 |
| 0.9578 | 16870 | 0.0181 |
| 0.9584 | 16880 | 0.0175 |
| 0.9590 | 16890 | 0.0166 |
| 0.9595 | 16900 | 0.0189 |
| 0.9601 | 16910 | 0.0171 |
| 0.9607 | 16920 | 0.018 |
| 0.9612 | 16930 | 0.0179 |
| 0.9618 | 16940 | 0.0177 |
| 0.9624 | 16950 | 0.0168 |
| 0.9629 | 16960 | 0.0169 |
| 0.9635 | 16970 | 0.0184 |
| 0.9641 | 16980 | 0.0178 |
| 0.9646 | 16990 | 0.0185 |
| 0.9652 | 17000 | 0.0174 |
| 0.9658 | 17010 | 0.0186 |
| 0.9663 | 17020 | 0.0178 |
| 0.9669 | 17030 | 0.0179 |
| 0.9675 | 17040 | 0.0179 |
| 0.9680 | 17050 | 0.018 |
| 0.9686 | 17060 | 0.0169 |
| 0.9692 | 17070 | 0.0184 |
| 0.9697 | 17080 | 0.0182 |
| 0.9703 | 17090 | 0.0175 |
| 0.9709 | 17100 | 0.0165 |
| 0.9714 | 17110 | 0.0177 |
| 0.9720 | 17120 | 0.0173 |
| 0.9726 | 17130 | 0.0184 |
| 0.9731 | 17140 | 0.0158 |
| 0.9737 | 17150 | 0.0176 |
| 0.9743 | 17160 | 0.0183 |
| 0.9748 | 17170 | 0.0173 |
| 0.9754 | 17180 | 0.0186 |
| 0.9760 | 17190 | 0.019 |
| 0.9766 | 17200 | 0.0184 |
| 0.9771 | 17210 | 0.0179 |
| 0.9777 | 17220 | 0.018 |
| 0.9783 | 17230 | 0.018 |
| 0.9788 | 17240 | 0.0187 |
| 0.9794 | 17250 | 0.0176 |
| 0.9800 | 17260 | 0.0173 |
| 0.9805 | 17270 | 0.0187 |
| 0.9811 | 17280 | 0.0171 |
| 0.9817 | 17290 | 0.0171 |
| 0.9822 | 17300 | 0.0184 |
| 0.9828 | 17310 | 0.0177 |
| 0.9834 | 17320 | 0.0187 |
| 0.9839 | 17330 | 0.0183 |
| 0.9845 | 17340 | 0.0186 |
| 0.9851 | 17350 | 0.0179 |
| 0.9856 | 17360 | 0.0167 |
| 0.9862 | 17370 | 0.0182 |
| 0.9868 | 17380 | 0.0192 |
| 0.9873 | 17390 | 0.0186 |
| 0.9879 | 17400 | 0.0175 |
| 0.9885 | 17410 | 0.0183 |
| 0.9890 | 17420 | 0.018 |
| 0.9896 | 17430 | 0.0171 |
| 0.9902 | 17440 | 0.0183 |
| 0.9907 | 17450 | 0.0184 |
| 0.9913 | 17460 | 0.018 |
| 0.9919 | 17470 | 0.0177 |
| 0.9924 | 17480 | 0.018 |
| 0.9930 | 17490 | 0.0184 |
| 0.9936 | 17500 | 0.0176 |
| 0.9942 | 17510 | 0.0182 |
| 0.9947 | 17520 | 0.0181 |
| 0.9953 | 17530 | 0.0176 |
| 0.9959 | 17540 | 0.0173 |
| 0.9964 | 17550 | 0.0179 |
| 0.9970 | 17560 | 0.0181 |
| 0.9976 | 17570 | 0.0181 |
| 0.9981 | 17580 | 0.0183 |
| 0.9987 | 17590 | 0.0177 |
| 0.9993 | 17600 | 0.0178 |
| 0.9998 | 17610 | 0.0179 |
| 1.0004 | 17620 | 0.0181 |
| 1.0010 | 17630 | 0.0174 |
| 1.0015 | 17640 | 0.0172 |
| 1.0021 | 17650 | 0.0167 |
| 1.0027 | 17660 | 0.017 |
| 1.0032 | 17670 | 0.0172 |
| 1.0038 | 17680 | 0.0176 |
| 1.0044 | 17690 | 0.0171 |
| 1.0049 | 17700 | 0.017 |
| 1.0055 | 17710 | 0.017 |
| 1.0061 | 17720 | 0.0166 |
| 1.0066 | 17730 | 0.0179 |
| 1.0072 | 17740 | 0.0172 |
| 1.0078 | 17750 | 0.0181 |
| 1.0083 | 17760 | 0.0171 |
| 1.0089 | 17770 | 0.0161 |
| 1.0095 | 17780 | 0.0173 |
| 1.0100 | 17790 | 0.0179 |
| 1.0106 | 17800 | 0.0173 |
| 1.0112 | 17810 | 0.0165 |
| 1.0118 | 17820 | 0.0174 |
| 1.0123 | 17830 | 0.0163 |
| 1.0129 | 17840 | 0.0175 |
| 1.0135 | 17850 | 0.0175 |
| 1.0140 | 17860 | 0.0177 |
| 1.0146 | 17870 | 0.0167 |
| 1.0152 | 17880 | 0.0173 |
| 1.0157 | 17890 | 0.0166 |
| 1.0163 | 17900 | 0.0172 |
| 1.0169 | 17910 | 0.0165 |
| 1.0174 | 17920 | 0.0175 |
| 1.0180 | 17930 | 0.0167 |
| 1.0186 | 17940 | 0.0178 |
| 1.0191 | 17950 | 0.0168 |
| 1.0197 | 17960 | 0.0172 |
| 1.0203 | 17970 | 0.0173 |
| 1.0208 | 17980 | 0.016 |
| 1.0214 | 17990 | 0.0179 |
| 1.0220 | 18000 | 0.0177 |
| 1.0225 | 18010 | 0.0174 |
| 1.0231 | 18020 | 0.0168 |
| 1.0237 | 18030 | 0.0167 |
| 1.0242 | 18040 | 0.0169 |
| 1.0248 | 18050 | 0.0168 |
| 1.0254 | 18060 | 0.0172 |
| 1.0259 | 18070 | 0.017 |
| 1.0265 | 18080 | 0.0167 |
| 1.0271 | 18090 | 0.0177 |
| 1.0277 | 18100 | 0.0172 |
| 1.0282 | 18110 | 0.0171 |
| 1.0288 | 18120 | 0.0174 |
| 1.0294 | 18130 | 0.0166 |
| 1.0299 | 18140 | 0.0161 |
| 1.0305 | 18150 | 0.0161 |
| 1.0311 | 18160 | 0.0166 |
| 1.0316 | 18170 | 0.0174 |
| 1.0322 | 18180 | 0.017 |
| 1.0328 | 18190 | 0.017 |
| 1.0333 | 18200 | 0.0167 |
| 1.0339 | 18210 | 0.0173 |
| 1.0345 | 18220 | 0.0177 |
| 1.0350 | 18230 | 0.0158 |
| 1.0356 | 18240 | 0.0174 |
| 1.0362 | 18250 | 0.0167 |
| 1.0367 | 18260 | 0.017 |
| 1.0373 | 18270 | 0.0184 |
| 1.0379 | 18280 | 0.0168 |
| 1.0384 | 18290 | 0.0174 |
| 1.0390 | 18300 | 0.0164 |
| 1.0396 | 18310 | 0.0167 |
| 1.0401 | 18320 | 0.017 |
| 1.0407 | 18330 | 0.0176 |
| 1.0413 | 18340 | 0.0168 |
| 1.0418 | 18350 | 0.0172 |
| 1.0424 | 18360 | 0.0169 |
| 1.0430 | 18370 | 0.0168 |
| 1.0435 | 18380 | 0.0178 |
| 1.0441 | 18390 | 0.0174 |
| 1.0447 | 18400 | 0.0177 |
| 1.0453 | 18410 | 0.0154 |
| 1.0458 | 18420 | 0.0174 |
| 1.0464 | 18430 | 0.0165 |
| 1.0470 | 18440 | 0.0168 |
| 1.0475 | 18450 | 0.0175 |
| 1.0481 | 18460 | 0.0175 |
| 1.0487 | 18470 | 0.0161 |
| 1.0492 | 18480 | 0.0176 |
| 1.0498 | 18490 | 0.0168 |
| 1.0504 | 18500 | 0.0171 |
| 1.0509 | 18510 | 0.0166 |
| 1.0515 | 18520 | 0.016 |
| 1.0521 | 18530 | 0.0167 |
| 1.0526 | 18540 | 0.0171 |
| 1.0532 | 18550 | 0.017 |
| 1.0538 | 18560 | 0.0174 |
| 1.0543 | 18570 | 0.0165 |
| 1.0549 | 18580 | 0.0174 |
| 1.0555 | 18590 | 0.0165 |
| 1.0560 | 18600 | 0.0167 |
| 1.0566 | 18610 | 0.0158 |
| 1.0572 | 18620 | 0.0167 |
| 1.0577 | 18630 | 0.0161 |
| 1.0583 | 18640 | 0.0164 |
| 1.0589 | 18650 | 0.017 |
| 1.0594 | 18660 | 0.0168 |
| 1.0600 | 18670 | 0.0164 |
| 1.0606 | 18680 | 0.0166 |
| 1.0611 | 18690 | 0.0159 |
| 1.0617 | 18700 | 0.0174 |
| 1.0623 | 18710 | 0.0174 |
| 1.0629 | 18720 | 0.0179 |
| 1.0634 | 18730 | 0.0183 |
| 1.0640 | 18740 | 0.0173 |
| 1.0646 | 18750 | 0.0164 |
| 1.0651 | 18760 | 0.0169 |
| 1.0657 | 18770 | 0.0172 |
| 1.0663 | 18780 | 0.0166 |
| 1.0668 | 18790 | 0.0172 |
| 1.0674 | 18800 | 0.0166 |
| 1.0680 | 18810 | 0.0166 |
| 1.0685 | 18820 | 0.0166 |
| 1.0691 | 18830 | 0.0171 |
| 1.0697 | 18840 | 0.0177 |
| 1.0702 | 18850 | 0.0172 |
| 1.0708 | 18860 | 0.0163 |
| 1.0714 | 18870 | 0.0177 |
| 1.0719 | 18880 | 0.0168 |
| 1.0725 | 18890 | 0.0168 |
| 1.0731 | 18900 | 0.0163 |
| 1.0736 | 18910 | 0.0162 |
| 1.0742 | 18920 | 0.0172 |
| 1.0748 | 18930 | 0.0178 |
| 1.0753 | 18940 | 0.0171 |
| 1.0759 | 18950 | 0.0167 |
| 1.0765 | 18960 | 0.0168 |
| 1.0770 | 18970 | 0.0165 |
| 1.0776 | 18980 | 0.0168 |
| 1.0782 | 18990 | 0.0163 |
| 1.0787 | 19000 | 0.0171 |
| 1.0793 | 19010 | 0.0169 |
| 1.0799 | 19020 | 0.0166 |
| 1.0805 | 19030 | 0.0166 |
| 1.0810 | 19040 | 0.0175 |
| 1.0816 | 19050 | 0.0167 |
| 1.0822 | 19060 | 0.0166 |
| 1.0827 | 19070 | 0.017 |
| 1.0833 | 19080 | 0.017 |
| 1.0839 | 19090 | 0.0171 |
| 1.0844 | 19100 | 0.0171 |
| 1.0850 | 19110 | 0.0165 |
| 1.0856 | 19120 | 0.017 |
| 1.0861 | 19130 | 0.017 |
| 1.0867 | 19140 | 0.016 |
| 1.0873 | 19150 | 0.0165 |
| 1.0878 | 19160 | 0.0161 |
| 1.0884 | 19170 | 0.0176 |
| 1.0890 | 19180 | 0.0175 |
| 1.0895 | 19190 | 0.0171 |
| 1.0901 | 19200 | 0.0185 |
| 1.0907 | 19210 | 0.0168 |
| 1.0912 | 19220 | 0.0169 |
| 1.0918 | 19230 | 0.0164 |
| 1.0924 | 19240 | 0.0171 |
| 1.0929 | 19250 | 0.017 |
| 1.0935 | 19260 | 0.0166 |
| 1.0941 | 19270 | 0.0166 |
| 1.0946 | 19280 | 0.0175 |
| 1.0952 | 19290 | 0.0171 |
| 1.0958 | 19300 | 0.016 |
| 1.0963 | 19310 | 0.0168 |
| 1.0969 | 19320 | 0.0159 |
| 1.0975 | 19330 | 0.0165 |
| 1.0981 | 19340 | 0.0167 |
| 1.0986 | 19350 | 0.0173 |
| 1.0992 | 19360 | 0.0162 |
| 1.0998 | 19370 | 0.0177 |
| 1.1003 | 19380 | 0.0173 |
| 1.1009 | 19390 | 0.0167 |
| 1.1015 | 19400 | 0.017 |
| 1.1020 | 19410 | 0.0172 |
| 1.1026 | 19420 | 0.0164 |
| 1.1032 | 19430 | 0.017 |
| 1.1037 | 19440 | 0.0163 |
| 1.1043 | 19450 | 0.016 |
| 1.1049 | 19460 | 0.0169 |
| 1.1054 | 19470 | 0.0164 |
| 1.1060 | 19480 | 0.0169 |
| 1.1066 | 19490 | 0.0164 |
| 1.1071 | 19500 | 0.0171 |
| 1.1077 | 19510 | 0.0165 |
| 1.1083 | 19520 | 0.0167 |
| 1.1088 | 19530 | 0.0166 |
| 1.1094 | 19540 | 0.0174 |
| 1.1100 | 19550 | 0.0175 |
| 1.1105 | 19560 | 0.0164 |
| 1.1111 | 19570 | 0.0165 |
| 1.1117 | 19580 | 0.0166 |
| 1.1122 | 19590 | 0.0169 |
| 1.1128 | 19600 | 0.0176 |
| 1.1134 | 19610 | 0.0182 |
| 1.1139 | 19620 | 0.0171 |
| 1.1145 | 19630 | 0.0165 |
| 1.1151 | 19640 | 0.0174 |
| 1.1157 | 19650 | 0.0166 |
| 1.1162 | 19660 | 0.0163 |
| 1.1168 | 19670 | 0.0179 |
| 1.1174 | 19680 | 0.0165 |
| 1.1179 | 19690 | 0.0172 |
| 1.1185 | 19700 | 0.017 |
| 1.1191 | 19710 | 0.0166 |
| 1.1196 | 19720 | 0.0166 |
| 1.1202 | 19730 | 0.0173 |
| 1.1208 | 19740 | 0.016 |
| 1.1213 | 19750 | 0.0173 |
| 1.1219 | 19760 | 0.0161 |
| 1.1225 | 19770 | 0.0171 |
| 1.1230 | 19780 | 0.0167 |
| 1.1236 | 19790 | 0.0162 |
| 1.1242 | 19800 | 0.0173 |
| 1.1247 | 19810 | 0.0168 |
| 1.1253 | 19820 | 0.0174 |
| 1.1259 | 19830 | 0.0174 |
| 1.1264 | 19840 | 0.0175 |
| 1.1270 | 19850 | 0.017 |
| 1.1276 | 19860 | 0.0172 |
| 1.1281 | 19870 | 0.0168 |
| 1.1287 | 19880 | 0.0172 |
| 1.1293 | 19890 | 0.0163 |
| 1.1298 | 19900 | 0.0163 |
| 1.1304 | 19910 | 0.0159 |
| 1.1310 | 19920 | 0.0165 |
| 1.1316 | 19930 | 0.0165 |
| 1.1321 | 19940 | 0.0167 |
| 1.1327 | 19950 | 0.0168 |
| 1.1333 | 19960 | 0.0166 |
| 1.1338 | 19970 | 0.0165 |
| 1.1344 | 19980 | 0.0173 |
| 1.1350 | 19990 | 0.0172 |
| 1.1355 | 20000 | 0.0161 |
| 1.1361 | 20010 | 0.0164 |
| 1.1367 | 20020 | 0.0166 |
| 1.1372 | 20030 | 0.0172 |
| 1.1378 | 20040 | 0.0172 |
| 1.1384 | 20050 | 0.0156 |
| 1.1389 | 20060 | 0.0176 |
| 1.1395 | 20070 | 0.016 |
| 1.1401 | 20080 | 0.017 |
| 1.1406 | 20090 | 0.0166 |
| 1.1412 | 20100 | 0.0169 |
| 1.1418 | 20110 | 0.018 |
| 1.1423 | 20120 | 0.0168 |
| 1.1429 | 20130 | 0.0168 |
| 1.1435 | 20140 | 0.0174 |
| 1.1440 | 20150 | 0.0172 |
| 1.1446 | 20160 | 0.0167 |
| 1.1452 | 20170 | 0.0167 |
| 1.1457 | 20180 | 0.0168 |
| 1.1463 | 20190 | 0.0168 |
| 1.1469 | 20200 | 0.0164 |
| 1.1474 | 20210 | 0.0167 |
| 1.1480 | 20220 | 0.0174 |
| 1.1486 | 20230 | 0.0172 |
| 1.1492 | 20240 | 0.0178 |
| 1.1497 | 20250 | 0.0171 |
| 1.1503 | 20260 | 0.016 |
| 1.1509 | 20270 | 0.0172 |
| 1.1514 | 20280 | 0.0174 |
| 1.1520 | 20290 | 0.0164 |
| 1.1526 | 20300 | 0.0155 |
| 1.1531 | 20310 | 0.0176 |
| 1.1537 | 20320 | 0.016 |
| 1.1543 | 20330 | 0.0168 |
| 1.1548 | 20340 | 0.0163 |
| 1.1554 | 20350 | 0.0174 |
| 1.1560 | 20360 | 0.0166 |
| 1.1565 | 20370 | 0.0161 |
| 1.1571 | 20380 | 0.0163 |
| 1.1577 | 20390 | 0.0169 |
| 1.1582 | 20400 | 0.0166 |
| 1.1588 | 20410 | 0.0166 |
| 1.1594 | 20420 | 0.0168 |
| 1.1599 | 20430 | 0.0159 |
| 1.1605 | 20440 | 0.0165 |
| 1.1611 | 20450 | 0.0173 |
| 1.1616 | 20460 | 0.0168 |
| 1.1622 | 20470 | 0.018 |
| 1.1628 | 20480 | 0.0169 |
| 1.1633 | 20490 | 0.0166 |
| 1.1639 | 20500 | 0.0176 |
| 1.1645 | 20510 | 0.0167 |
| 1.1650 | 20520 | 0.0171 |
| 1.1656 | 20530 | 0.0173 |
| 1.1662 | 20540 | 0.0175 |
| 1.1668 | 20550 | 0.0165 |
| 1.1673 | 20560 | 0.0163 |
| 1.1679 | 20570 | 0.0165 |
| 1.1685 | 20580 | 0.0176 |
| 1.1690 | 20590 | 0.0167 |
| 1.1696 | 20600 | 0.017 |
| 1.1702 | 20610 | 0.0167 |
| 1.1707 | 20620 | 0.0163 |
| 1.1713 | 20630 | 0.017 |
| 1.1719 | 20640 | 0.0167 |
| 1.1724 | 20650 | 0.0168 |
| 1.1730 | 20660 | 0.0168 |
| 1.1736 | 20670 | 0.0172 |
| 1.1741 | 20680 | 0.0165 |
| 1.1747 | 20690 | 0.0166 |
| 1.1753 | 20700 | 0.017 |
| 1.1758 | 20710 | 0.0162 |
| 1.1764 | 20720 | 0.0166 |
| 1.1770 | 20730 | 0.0169 |
| 1.1775 | 20740 | 0.016 |
| 1.1781 | 20750 | 0.0162 |
| 1.1787 | 20760 | 0.0166 |
| 1.1792 | 20770 | 0.0169 |
| 1.1798 | 20780 | 0.0168 |
| 1.1804 | 20790 | 0.0163 |
| 1.1809 | 20800 | 0.0164 |
| 1.1815 | 20810 | 0.0164 |
| 1.1821 | 20820 | 0.0171 |
| 1.1826 | 20830 | 0.0167 |
| 1.1832 | 20840 | 0.0155 |
| 1.1838 | 20850 | 0.0163 |
| 1.1844 | 20860 | 0.0167 |
| 1.1849 | 20870 | 0.0166 |
| 1.1855 | 20880 | 0.0163 |
| 1.1861 | 20890 | 0.0158 |
| 1.1866 | 20900 | 0.017 |
| 1.1872 | 20910 | 0.0169 |
| 1.1878 | 20920 | 0.017 |
| 1.1883 | 20930 | 0.0164 |
| 1.1889 | 20940 | 0.0155 |
| 1.1895 | 20950 | 0.0177 |
| 1.1900 | 20960 | 0.0163 |
| 1.1906 | 20970 | 0.0175 |
| 1.1912 | 20980 | 0.0168 |
| 1.1917 | 20990 | 0.0171 |
| 1.1923 | 21000 | 0.0159 |
| 1.1929 | 21010 | 0.0167 |
| 1.1934 | 21020 | 0.0166 |
| 1.1940 | 21030 | 0.0169 |
| 1.1946 | 21040 | 0.0166 |
| 1.1951 | 21050 | 0.017 |
| 1.1957 | 21060 | 0.0169 |
| 1.1963 | 21070 | 0.0167 |
| 1.1968 | 21080 | 0.017 |
| 1.1974 | 21090 | 0.016 |
| 1.1980 | 21100 | 0.0157 |
| 1.1985 | 21110 | 0.016 |
| 1.1991 | 21120 | 0.017 |
| 1.1997 | 21130 | 0.0168 |
| 1.2002 | 21140 | 0.0161 |
| 1.2008 | 21150 | 0.0174 |
| 1.2014 | 21160 | 0.0169 |
| 1.2020 | 21170 | 0.0161 |
| 1.2025 | 21180 | 0.0174 |
| 1.2031 | 21190 | 0.0179 |
| 1.2037 | 21200 | 0.0173 |
| 1.2042 | 21210 | 0.0159 |
| 1.2048 | 21220 | 0.0172 |
| 1.2054 | 21230 | 0.017 |
| 1.2059 | 21240 | 0.0164 |
| 1.2065 | 21250 | 0.0171 |
| 1.2071 | 21260 | 0.0159 |
| 1.2076 | 21270 | 0.0175 |
| 1.2082 | 21280 | 0.0169 |
| 1.2088 | 21290 | 0.0175 |
| 1.2093 | 21300 | 0.017 |
| 1.2099 | 21310 | 0.0161 |
| 1.2105 | 21320 | 0.0171 |
| 1.2110 | 21330 | 0.0162 |
| 1.2116 | 21340 | 0.0165 |
| 1.2122 | 21350 | 0.0169 |
| 1.2127 | 21360 | 0.017 |
| 1.2133 | 21370 | 0.0168 |
| 1.2139 | 21380 | 0.0172 |
| 1.2144 | 21390 | 0.0169 |
| 1.2150 | 21400 | 0.0164 |
| 1.2156 | 21410 | 0.0174 |
| 1.2161 | 21420 | 0.0166 |
| 1.2167 | 21430 | 0.0164 |
| 1.2173 | 21440 | 0.0167 |
| 1.2179 | 21450 | 0.0167 |
| 1.2184 | 21460 | 0.0168 |
| 1.2190 | 21470 | 0.0171 |
| 1.2196 | 21480 | 0.0165 |
| 1.2201 | 21490 | 0.0166 |
| 1.2207 | 21500 | 0.0165 |
| 1.2213 | 21510 | 0.0168 |
| 1.2218 | 21520 | 0.0173 |
| 1.2224 | 21530 | 0.0172 |
| 1.2230 | 21540 | 0.0166 |
| 1.2235 | 21550 | 0.0162 |
| 1.2241 | 21560 | 0.0163 |
| 1.2247 | 21570 | 0.0169 |
| 1.2252 | 21580 | 0.0171 |
| 1.2258 | 21590 | 0.0161 |
| 1.2264 | 21600 | 0.017 |
| 1.2269 | 21610 | 0.0171 |
| 1.2275 | 21620 | 0.0174 |
| 1.2281 | 21630 | 0.0168 |
| 1.2286 | 21640 | 0.0169 |
| 1.2292 | 21650 | 0.0165 |
| 1.2298 | 21660 | 0.0164 |
| 1.2303 | 21670 | 0.0168 |
| 1.2309 | 21680 | 0.0162 |
| 1.2315 | 21690 | 0.0168 |
| 1.2320 | 21700 | 0.0164 |
| 1.2326 | 21710 | 0.0163 |
| 1.2332 | 21720 | 0.0167 |
| 1.2337 | 21730 | 0.0165 |
| 1.2343 | 21740 | 0.0168 |
| 1.2349 | 21750 | 0.0169 |
| 1.2355 | 21760 | 0.0162 |
| 1.2360 | 21770 | 0.0168 |
| 1.2366 | 21780 | 0.0159 |
| 1.2372 | 21790 | 0.0172 |
| 1.2377 | 21800 | 0.0165 |
| 1.2383 | 21810 | 0.0167 |
| 1.2389 | 21820 | 0.0168 |
| 1.2394 | 21830 | 0.0171 |
| 1.2400 | 21840 | 0.0157 |
| 1.2406 | 21850 | 0.0166 |
| 1.2411 | 21860 | 0.0169 |
| 1.2417 | 21870 | 0.0168 |
| 1.2423 | 21880 | 0.0172 |
| 1.2428 | 21890 | 0.0161 |
| 1.2434 | 21900 | 0.0172 |
| 1.2440 | 21910 | 0.017 |
| 1.2445 | 21920 | 0.0173 |
| 1.2451 | 21930 | 0.0162 |
| 1.2457 | 21940 | 0.0161 |
| 1.2462 | 21950 | 0.0173 |
| 1.2468 | 21960 | 0.0168 |
| 1.2474 | 21970 | 0.0168 |
| 1.2479 | 21980 | 0.0175 |
| 1.2485 | 21990 | 0.0171 |
| 1.2491 | 22000 | 0.0166 |
| 1.2496 | 22010 | 0.0163 |
| 1.2502 | 22020 | 0.0168 |
| 1.2508 | 22030 | 0.0178 |
| 1.2513 | 22040 | 0.017 |
| 1.2519 | 22050 | 0.0167 |
| 1.2525 | 22060 | 0.0165 |
| 1.2531 | 22070 | 0.0166 |
| 1.2536 | 22080 | 0.0166 |
| 1.2542 | 22090 | 0.0156 |
| 1.2548 | 22100 | 0.0176 |
| 1.2553 | 22110 | 0.0166 |
| 1.2559 | 22120 | 0.0168 |
| 1.2565 | 22130 | 0.0168 |
| 1.2570 | 22140 | 0.0177 |
| 1.2576 | 22150 | 0.0172 |
| 1.2582 | 22160 | 0.0164 |
| 1.2587 | 22170 | 0.017 |
| 1.2593 | 22180 | 0.0162 |
| 1.2599 | 22190 | 0.0158 |
| 1.2604 | 22200 | 0.0165 |
| 1.2610 | 22210 | 0.0172 |
| 1.2616 | 22220 | 0.0173 |
| 1.2621 | 22230 | 0.0164 |
| 1.2627 | 22240 | 0.0176 |
| 1.2633 | 22250 | 0.0165 |
| 1.2638 | 22260 | 0.0166 |
| 1.2644 | 22270 | 0.0171 |
| 1.2650 | 22280 | 0.016 |
| 1.2655 | 22290 | 0.0159 |
| 1.2661 | 22300 | 0.0172 |
| 1.2667 | 22310 | 0.0173 |
| 1.2672 | 22320 | 0.0171 |
| 1.2678 | 22330 | 0.0177 |
| 1.2684 | 22340 | 0.0167 |
| 1.2689 | 22350 | 0.0165 |
| 1.2695 | 22360 | 0.0165 |
| 1.2701 | 22370 | 0.0171 |
| 1.2707 | 22380 | 0.0163 |
| 1.2712 | 22390 | 0.0165 |
| 1.2718 | 22400 | 0.0173 |
| 1.2724 | 22410 | 0.0156 |
| 1.2729 | 22420 | 0.0172 |
| 1.2735 | 22430 | 0.0169 |
| 1.2741 | 22440 | 0.0176 |
| 1.2746 | 22450 | 0.0161 |
| 1.2752 | 22460 | 0.0156 |
| 1.2758 | 22470 | 0.0173 |
| 1.2763 | 22480 | 0.0172 |
| 1.2769 | 22490 | 0.0162 |
| 1.2775 | 22500 | 0.0161 |
| 1.2780 | 22510 | 0.0158 |
| 1.2786 | 22520 | 0.0174 |
| 1.2792 | 22530 | 0.016 |
| 1.2797 | 22540 | 0.0165 |
| 1.2803 | 22550 | 0.017 |
| 1.2809 | 22560 | 0.017 |
| 1.2814 | 22570 | 0.0162 |
| 1.2820 | 22580 | 0.0162 |
| 1.2826 | 22590 | 0.0176 |
| 1.2831 | 22600 | 0.017 |
| 1.2837 | 22610 | 0.0158 |
| 1.2843 | 22620 | 0.0168 |
| 1.2848 | 22630 | 0.0171 |
| 1.2854 | 22640 | 0.0169 |
| 1.2860 | 22650 | 0.0174 |
| 1.2865 | 22660 | 0.0163 |
| 1.2871 | 22670 | 0.0173 |
| 1.2877 | 22680 | 0.0174 |
| 1.2883 | 22690 | 0.0176 |
| 1.2888 | 22700 | 0.0169 |
| 1.2894 | 22710 | 0.0175 |
| 1.2900 | 22720 | 0.0167 |
| 1.2905 | 22730 | 0.017 |
| 1.2911 | 22740 | 0.0173 |
| 1.2917 | 22750 | 0.0155 |
| 1.2922 | 22760 | 0.0169 |
| 1.2928 | 22770 | 0.0166 |
| 1.2934 | 22780 | 0.0156 |
| 1.2939 | 22790 | 0.0171 |
| 1.2945 | 22800 | 0.017 |
| 1.2951 | 22810 | 0.0163 |
| 1.2956 | 22820 | 0.0173 |
| 1.2962 | 22830 | 0.0166 |
| 1.2968 | 22840 | 0.0167 |
| 1.2973 | 22850 | 0.017 |
| 1.2979 | 22860 | 0.0166 |
| 1.2985 | 22870 | 0.0159 |
| 1.2990 | 22880 | 0.0169 |
| 1.2996 | 22890 | 0.0166 |
| 1.3002 | 22900 | 0.016 |
| 1.3007 | 22910 | 0.0166 |
| 1.3013 | 22920 | 0.0167 |
| 1.3019 | 22930 | 0.0161 |
| 1.3024 | 22940 | 0.0172 |
| 1.3030 | 22950 | 0.0159 |
| 1.3036 | 22960 | 0.0167 |
| 1.3042 | 22970 | 0.0167 |
| 1.3047 | 22980 | 0.0165 |
| 1.3053 | 22990 | 0.017 |
| 1.3059 | 23000 | 0.0168 |
| 1.3064 | 23010 | 0.0176 |
| 1.3070 | 23020 | 0.0169 |
| 1.3076 | 23030 | 0.0165 |
| 1.3081 | 23040 | 0.0158 |
| 1.3087 | 23050 | 0.0164 |
| 1.3093 | 23060 | 0.017 |
| 1.3098 | 23070 | 0.0165 |
| 1.3104 | 23080 | 0.0167 |
| 1.3110 | 23090 | 0.0164 |
| 1.3115 | 23100 | 0.0157 |
| 1.3121 | 23110 | 0.0169 |
| 1.3127 | 23120 | 0.0168 |
| 1.3132 | 23130 | 0.0169 |
| 1.3138 | 23140 | 0.017 |
| 1.3144 | 23150 | 0.0172 |
| 1.3149 | 23160 | 0.0172 |
| 1.3155 | 23170 | 0.0166 |
| 1.3161 | 23180 | 0.0175 |
| 1.3166 | 23190 | 0.0172 |
| 1.3172 | 23200 | 0.0163 |
| 1.3178 | 23210 | 0.0163 |
| 1.3183 | 23220 | 0.0167 |
| 1.3189 | 23230 | 0.0165 |
| 1.3195 | 23240 | 0.0165 |
| 1.3200 | 23250 | 0.0161 |
| 1.3206 | 23260 | 0.016 |
| 1.3212 | 23270 | 0.016 |
| 1.3218 | 23280 | 0.0168 |
| 1.3223 | 23290 | 0.0171 |
| 1.3229 | 23300 | 0.0171 |
| 1.3235 | 23310 | 0.0163 |
| 1.3240 | 23320 | 0.0162 |
| 1.3246 | 23330 | 0.017 |
| 1.3252 | 23340 | 0.0174 |
| 1.3257 | 23350 | 0.0161 |
| 1.3263 | 23360 | 0.0171 |
| 1.3269 | 23370 | 0.016 |
| 1.3274 | 23380 | 0.0169 |
| 1.3280 | 23390 | 0.0154 |
| 1.3286 | 23400 | 0.0184 |
| 1.3291 | 23410 | 0.0164 |
| 1.3297 | 23420 | 0.0168 |
| 1.3303 | 23430 | 0.0167 |
| 1.3308 | 23440 | 0.0162 |
| 1.3314 | 23450 | 0.0155 |
| 1.3320 | 23460 | 0.0154 |
| 1.3325 | 23470 | 0.0165 |
| 1.3331 | 23480 | 0.0164 |
| 1.3337 | 23490 | 0.0157 |
| 1.3342 | 23500 | 0.0161 |
| 1.3348 | 23510 | 0.0167 |
| 1.3354 | 23520 | 0.017 |
| 1.3359 | 23530 | 0.0169 |
| 1.3365 | 23540 | 0.0167 |
| 1.3371 | 23550 | 0.0162 |
| 1.3376 | 23560 | 0.0162 |
| 1.3382 | 23570 | 0.0166 |
| 1.3388 | 23580 | 0.0166 |
| 1.3394 | 23590 | 0.0165 |
| 1.3399 | 23600 | 0.0162 |
| 1.3405 | 23610 | 0.0164 |
| 1.3411 | 23620 | 0.0173 |
| 1.3416 | 23630 | 0.0158 |
| 1.3422 | 23640 | 0.0163 |
| 1.3428 | 23650 | 0.0169 |
| 1.3433 | 23660 | 0.0168 |
| 1.3439 | 23670 | 0.0167 |
| 1.3445 | 23680 | 0.0168 |
| 1.3450 | 23690 | 0.0169 |
| 1.3456 | 23700 | 0.0153 |
| 1.3462 | 23710 | 0.0157 |
| 1.3467 | 23720 | 0.0171 |
| 1.3473 | 23730 | 0.0168 |
| 1.3479 | 23740 | 0.0161 |
| 1.3484 | 23750 | 0.0162 |
| 1.3490 | 23760 | 0.0166 |
| 1.3496 | 23770 | 0.017 |
| 1.3501 | 23780 | 0.0159 |
| 1.3507 | 23790 | 0.0169 |
| 1.3513 | 23800 | 0.0163 |
| 1.3518 | 23810 | 0.0166 |
| 1.3524 | 23820 | 0.0165 |
| 1.3530 | 23830 | 0.0157 |
| 1.3535 | 23840 | 0.0152 |
| 1.3541 | 23850 | 0.0161 |
| 1.3547 | 23860 | 0.0165 |
| 1.3552 | 23870 | 0.017 |
| 1.3558 | 23880 | 0.017 |
| 1.3564 | 23890 | 0.0162 |
| 1.3570 | 23900 | 0.0161 |
| 1.3575 | 23910 | 0.0173 |
| 1.3581 | 23920 | 0.0164 |
| 1.3587 | 23930 | 0.017 |
| 1.3592 | 23940 | 0.0173 |
| 1.3598 | 23950 | 0.0179 |
| 1.3604 | 23960 | 0.0166 |
| 1.3609 | 23970 | 0.0177 |
| 1.3615 | 23980 | 0.0163 |
| 1.3621 | 23990 | 0.0161 |
| 1.3626 | 24000 | 0.0172 |
| 1.3632 | 24010 | 0.0172 |
| 1.3638 | 24020 | 0.0163 |
| 1.3643 | 24030 | 0.0176 |
| 1.3649 | 24040 | 0.0159 |
| 1.3655 | 24050 | 0.0161 |
| 1.3660 | 24060 | 0.0168 |
| 1.3666 | 24070 | 0.0172 |
| 1.3672 | 24080 | 0.0172 |
| 1.3677 | 24090 | 0.0161 |
| 1.3683 | 24100 | 0.0171 |
| 1.3689 | 24110 | 0.0159 |
| 1.3694 | 24120 | 0.0168 |
| 1.3700 | 24130 | 0.0164 |
| 1.3706 | 24140 | 0.0166 |
| 1.3711 | 24150 | 0.0161 |
| 1.3717 | 24160 | 0.0154 |
| 1.3723 | 24170 | 0.0165 |
| 1.3728 | 24180 | 0.0168 |
| 1.3734 | 24190 | 0.0171 |
| 1.3740 | 24200 | 0.0166 |
| 1.3746 | 24210 | 0.0163 |
| 1.3751 | 24220 | 0.0166 |
| 1.3757 | 24230 | 0.0166 |
| 1.3763 | 24240 | 0.016 |
| 1.3768 | 24250 | 0.0166 |
| 1.3774 | 24260 | 0.0165 |
| 1.3780 | 24270 | 0.0161 |
| 1.3785 | 24280 | 0.0164 |
| 1.3791 | 24290 | 0.0155 |
| 1.3797 | 24300 | 0.0166 |
| 1.3802 | 24310 | 0.0158 |
| 1.3808 | 24320 | 0.0171 |
| 1.3814 | 24330 | 0.0177 |
| 1.3819 | 24340 | 0.0165 |
| 1.3825 | 24350 | 0.0169 |
| 1.3831 | 24360 | 0.0158 |
| 1.3836 | 24370 | 0.0167 |
| 1.3842 | 24380 | 0.0165 |
| 1.3848 | 24390 | 0.0161 |
| 1.3853 | 24400 | 0.0174 |
| 1.3859 | 24410 | 0.0175 |
| 1.3865 | 24420 | 0.0166 |
| 1.3870 | 24430 | 0.0167 |
| 1.3876 | 24440 | 0.0168 |
| 1.3882 | 24450 | 0.0164 |
| 1.3887 | 24460 | 0.016 |
| 1.3893 | 24470 | 0.0162 |
| 1.3899 | 24480 | 0.0168 |
| 1.3905 | 24490 | 0.0156 |
| 1.3910 | 24500 | 0.016 |
| 1.3916 | 24510 | 0.0161 |
| 1.3922 | 24520 | 0.0164 |
| 1.3927 | 24530 | 0.0171 |
| 1.3933 | 24540 | 0.0155 |
| 1.3939 | 24550 | 0.0168 |
| 1.3944 | 24560 | 0.0167 |
| 1.3950 | 24570 | 0.0158 |
| 1.3956 | 24580 | 0.0161 |
| 1.3961 | 24590 | 0.0163 |
| 1.3967 | 24600 | 0.0166 |
| 1.3973 | 24610 | 0.0168 |
| 1.3978 | 24620 | 0.0159 |
| 1.3984 | 24630 | 0.0172 |
| 1.3990 | 24640 | 0.0165 |
| 1.3995 | 24650 | 0.0158 |
| 1.4001 | 24660 | 0.0162 |
| 1.4007 | 24670 | 0.0159 |
| 1.4012 | 24680 | 0.0164 |
| 1.4018 | 24690 | 0.0165 |
| 1.4024 | 24700 | 0.0166 |
| 1.4029 | 24710 | 0.0163 |
| 1.4035 | 24720 | 0.0163 |
| 1.4041 | 24730 | 0.0167 |
| 1.4046 | 24740 | 0.0164 |
| 1.4052 | 24750 | 0.0171 |
| 1.4058 | 24760 | 0.0163 |
| 1.4063 | 24770 | 0.0161 |
| 1.4069 | 24780 | 0.0165 |
| 1.4075 | 24790 | 0.0155 |
| 1.4081 | 24800 | 0.0167 |
| 1.4086 | 24810 | 0.0173 |
| 1.4092 | 24820 | 0.0177 |
| 1.4098 | 24830 | 0.0168 |
| 1.4103 | 24840 | 0.0166 |
| 1.4109 | 24850 | 0.017 |
| 1.4115 | 24860 | 0.0161 |
| 1.4120 | 24870 | 0.0166 |
| 1.4126 | 24880 | 0.0165 |
| 1.4132 | 24890 | 0.0168 |
| 1.4137 | 24900 | 0.0163 |
| 1.4143 | 24910 | 0.016 |
| 1.4149 | 24920 | 0.0166 |
| 1.4154 | 24930 | 0.0169 |
| 1.4160 | 24940 | 0.0164 |
| 1.4166 | 24950 | 0.0164 |
| 1.4171 | 24960 | 0.0158 |
| 1.4177 | 24970 | 0.0164 |
| 1.4183 | 24980 | 0.0165 |
| 1.4188 | 24990 | 0.0161 |
| 1.4194 | 25000 | 0.0158 |
| 1.4200 | 25010 | 0.0165 |
| 1.4205 | 25020 | 0.0161 |
| 1.4211 | 25030 | 0.0152 |
| 1.4217 | 25040 | 0.0166 |
| 1.4222 | 25050 | 0.0163 |
| 1.4228 | 25060 | 0.0162 |
| 1.4234 | 25070 | 0.0155 |
| 1.4239 | 25080 | 0.0167 |
| 1.4245 | 25090 | 0.0163 |
| 1.4251 | 25100 | 0.0163 |
| 1.4257 | 25110 | 0.0163 |
| 1.4262 | 25120 | 0.0165 |
| 1.4268 | 25130 | 0.0171 |
| 1.4274 | 25140 | 0.0173 |
| 1.4279 | 25150 | 0.0165 |
| 1.4285 | 25160 | 0.0172 |
| 1.4291 | 25170 | 0.017 |
| 1.4296 | 25180 | 0.0166 |
| 1.4302 | 25190 | 0.0165 |
| 1.4308 | 25200 | 0.0157 |
| 1.4313 | 25210 | 0.0158 |
| 1.4319 | 25220 | 0.0162 |
| 1.4325 | 25230 | 0.016 |
| 1.4330 | 25240 | 0.0164 |
| 1.4336 | 25250 | 0.0175 |
| 1.4342 | 25260 | 0.0166 |
| 1.4347 | 25270 | 0.017 |
| 1.4353 | 25280 | 0.0153 |
| 1.4359 | 25290 | 0.0165 |
| 1.4364 | 25300 | 0.0161 |
| 1.4370 | 25310 | 0.0174 |
| 1.4376 | 25320 | 0.0161 |
| 1.4381 | 25330 | 0.0156 |
| 1.4387 | 25340 | 0.017 |
| 1.4393 | 25350 | 0.0159 |
| 1.4398 | 25360 | 0.0168 |
| 1.4404 | 25370 | 0.017 |
| 1.4410 | 25380 | 0.0169 |
| 1.4415 | 25390 | 0.0162 |
| 1.4421 | 25400 | 0.0169 |
| 1.4427 | 25410 | 0.0172 |
| 1.4433 | 25420 | 0.017 |
| 1.4438 | 25430 | 0.0171 |
| 1.4444 | 25440 | 0.0164 |
| 1.4450 | 25450 | 0.0175 |
| 1.4455 | 25460 | 0.0164 |
| 1.4461 | 25470 | 0.0161 |
| 1.4467 | 25480 | 0.0171 |
| 1.4472 | 25490 | 0.0156 |
| 1.4478 | 25500 | 0.0166 |
| 1.4484 | 25510 | 0.0172 |
| 1.4489 | 25520 | 0.0163 |
| 1.4495 | 25530 | 0.0168 |
| 1.4501 | 25540 | 0.0167 |
| 1.4506 | 25550 | 0.0171 |
| 1.4512 | 25560 | 0.0171 |
| 1.4518 | 25570 | 0.0169 |
| 1.4523 | 25580 | 0.0158 |
| 1.4529 | 25590 | 0.0168 |
| 1.4535 | 25600 | 0.0174 |
| 1.4540 | 25610 | 0.0165 |
| 1.4546 | 25620 | 0.0158 |
| 1.4552 | 25630 | 0.0163 |
| 1.4557 | 25640 | 0.0165 |
| 1.4563 | 25650 | 0.0173 |
| 1.4569 | 25660 | 0.0167 |
| 1.4574 | 25670 | 0.0171 |
| 1.4580 | 25680 | 0.0173 |
| 1.4586 | 25690 | 0.0163 |
| 1.4591 | 25700 | 0.0174 |
| 1.4597 | 25710 | 0.0174 |
| 1.4603 | 25720 | 0.0171 |
| 1.4609 | 25730 | 0.0159 |
| 1.4614 | 25740 | 0.0156 |
| 1.4620 | 25750 | 0.0166 |
| 1.4626 | 25760 | 0.0168 |
| 1.4631 | 25770 | 0.017 |
| 1.4637 | 25780 | 0.0165 |
| 1.4643 | 25790 | 0.0166 |
| 1.4648 | 25800 | 0.0163 |
| 1.4654 | 25810 | 0.0164 |
| 1.4660 | 25820 | 0.0171 |
| 1.4665 | 25830 | 0.0163 |
| 1.4671 | 25840 | 0.0164 |
| 1.4677 | 25850 | 0.0162 |
| 1.4682 | 25860 | 0.0167 |
| 1.4688 | 25870 | 0.0171 |
| 1.4694 | 25880 | 0.0149 |
| 1.4699 | 25890 | 0.0156 |
| 1.4705 | 25900 | 0.0158 |
| 1.4711 | 25910 | 0.0176 |
| 1.4716 | 25920 | 0.0163 |
| 1.4722 | 25930 | 0.0166 |
| 1.4728 | 25940 | 0.0163 |
| 1.4733 | 25950 | 0.0169 |
| 1.4739 | 25960 | 0.0164 |
| 1.4745 | 25970 | 0.0174 |
| 1.4750 | 25980 | 0.0159 |
| 1.4756 | 25990 | 0.0161 |
| 1.4762 | 26000 | 0.0161 |
| 1.4768 | 26010 | 0.0163 |
| 1.4773 | 26020 | 0.0161 |
| 1.4779 | 26030 | 0.0167 |
| 1.4785 | 26040 | 0.016 |
| 1.4790 | 26050 | 0.016 |
| 1.4796 | 26060 | 0.0169 |
| 1.4802 | 26070 | 0.016 |
| 1.4807 | 26080 | 0.0167 |
| 1.4813 | 26090 | 0.0171 |
| 1.4819 | 26100 | 0.017 |
| 1.4824 | 26110 | 0.017 |
| 1.4830 | 26120 | 0.0161 |
| 1.4836 | 26130 | 0.016 |
| 1.4841 | 26140 | 0.0167 |
| 1.4847 | 26150 | 0.0173 |
| 1.4853 | 26160 | 0.0167 |
| 1.4858 | 26170 | 0.0159 |
| 1.4864 | 26180 | 0.0164 |
| 1.4870 | 26190 | 0.016 |
| 1.4875 | 26200 | 0.0161 |
| 1.4881 | 26210 | 0.0148 |
| 1.4887 | 26220 | 0.016 |
| 1.4892 | 26230 | 0.0166 |
| 1.4898 | 26240 | 0.0164 |
| 1.4904 | 26250 | 0.0164 |
| 1.4909 | 26260 | 0.0165 |
| 1.4915 | 26270 | 0.0155 |
| 1.4921 | 26280 | 0.017 |
| 1.4926 | 26290 | 0.0165 |
| 1.4932 | 26300 | 0.0168 |
| 1.4938 | 26310 | 0.0168 |
| 1.4944 | 26320 | 0.0173 |
| 1.4949 | 26330 | 0.0161 |
| 1.4955 | 26340 | 0.0159 |
| 1.4961 | 26350 | 0.0169 |
| 1.4966 | 26360 | 0.017 |
| 1.4972 | 26370 | 0.0164 |
| 1.4978 | 26380 | 0.017 |
| 1.4983 | 26390 | 0.017 |
| 1.4989 | 26400 | 0.0158 |
| 1.4995 | 26410 | 0.0164 |
| 1.5000 | 26420 | 0.0159 |
| 1.5006 | 26430 | 0.0157 |
| 1.5012 | 26440 | 0.0166 |
| 1.5017 | 26450 | 0.0168 |
| 1.5023 | 26460 | 0.0161 |
| 1.5029 | 26470 | 0.0162 |
| 1.5034 | 26480 | 0.016 |
| 1.5040 | 26490 | 0.0158 |
| 1.5046 | 26500 | 0.0169 |
| 1.5051 | 26510 | 0.0165 |
| 1.5057 | 26520 | 0.0168 |
| 1.5063 | 26530 | 0.0167 |
| 1.5068 | 26540 | 0.0157 |
| 1.5074 | 26550 | 0.0168 |
| 1.5080 | 26560 | 0.0162 |
| 1.5085 | 26570 | 0.0166 |
| 1.5091 | 26580 | 0.0163 |
| 1.5097 | 26590 | 0.0155 |
| 1.5102 | 26600 | 0.0166 |
| 1.5108 | 26610 | 0.0171 |
| 1.5114 | 26620 | 0.0156 |
| 1.5120 | 26630 | 0.0163 |
| 1.5125 | 26640 | 0.0163 |
| 1.5131 | 26650 | 0.0161 |
| 1.5137 | 26660 | 0.0158 |
| 1.5142 | 26670 | 0.0159 |
| 1.5148 | 26680 | 0.0167 |
| 1.5154 | 26690 | 0.0172 |
| 1.5159 | 26700 | 0.0158 |
| 1.5165 | 26710 | 0.0173 |
| 1.5171 | 26720 | 0.0168 |
| 1.5176 | 26730 | 0.0168 |
| 1.5182 | 26740 | 0.0164 |
| 1.5188 | 26750 | 0.017 |
| 1.5193 | 26760 | 0.016 |
| 1.5199 | 26770 | 0.0161 |
| 1.5205 | 26780 | 0.0167 |
| 1.5210 | 26790 | 0.016 |
| 1.5216 | 26800 | 0.0165 |
| 1.5222 | 26810 | 0.016 |
| 1.5227 | 26820 | 0.017 |
| 1.5233 | 26830 | 0.0168 |
| 1.5239 | 26840 | 0.0168 |
| 1.5244 | 26850 | 0.0163 |
| 1.5250 | 26860 | 0.0168 |
| 1.5256 | 26870 | 0.0165 |
| 1.5261 | 26880 | 0.0158 |
| 1.5267 | 26890 | 0.016 |
| 1.5273 | 26900 | 0.0161 |
| 1.5278 | 26910 | 0.0163 |
| 1.5284 | 26920 | 0.0158 |
| 1.5290 | 26930 | 0.0165 |
| 1.5296 | 26940 | 0.0164 |
| 1.5301 | 26950 | 0.0174 |
| 1.5307 | 26960 | 0.0152 |
| 1.5313 | 26970 | 0.0155 |
| 1.5318 | 26980 | 0.0165 |
| 1.5324 | 26990 | 0.0161 |
| 1.5330 | 27000 | 0.0159 |
| 1.5335 | 27010 | 0.0169 |
| 1.5341 | 27020 | 0.016 |
| 1.5347 | 27030 | 0.0162 |
| 1.5352 | 27040 | 0.0155 |
| 1.5358 | 27050 | 0.0158 |
| 1.5364 | 27060 | 0.0164 |
| 1.5369 | 27070 | 0.0159 |
| 1.5375 | 27080 | 0.0162 |
| 1.5381 | 27090 | 0.0162 |
| 1.5386 | 27100 | 0.0162 |
| 1.5392 | 27110 | 0.0168 |
| 1.5398 | 27120 | 0.0158 |
| 1.5403 | 27130 | 0.0173 |
| 1.5409 | 27140 | 0.016 |
| 1.5415 | 27150 | 0.0181 |
| 1.5420 | 27160 | 0.0167 |
| 1.5426 | 27170 | 0.0165 |
| 1.5432 | 27180 | 0.0161 |
| 1.5437 | 27190 | 0.016 |
| 1.5443 | 27200 | 0.017 |
| 1.5449 | 27210 | 0.0165 |
| 1.5454 | 27220 | 0.0164 |
| 1.5460 | 27230 | 0.0171 |
| 1.5466 | 27240 | 0.0168 |
| 1.5472 | 27250 | 0.0161 |
| 1.5477 | 27260 | 0.017 |
| 1.5483 | 27270 | 0.0163 |
| 1.5489 | 27280 | 0.0173 |
| 1.5494 | 27290 | 0.0158 |
| 1.5500 | 27300 | 0.0169 |
| 1.5506 | 27310 | 0.0153 |
| 1.5511 | 27320 | 0.0166 |
| 1.5517 | 27330 | 0.0155 |
| 1.5523 | 27340 | 0.017 |
| 1.5528 | 27350 | 0.017 |
| 1.5534 | 27360 | 0.0159 |
| 1.5540 | 27370 | 0.0166 |
| 1.5545 | 27380 | 0.0156 |
| 1.5551 | 27390 | 0.0158 |
| 1.5557 | 27400 | 0.0164 |
| 1.5562 | 27410 | 0.0164 |
| 1.5568 | 27420 | 0.0159 |
| 1.5574 | 27430 | 0.0155 |
| 1.5579 | 27440 | 0.017 |
| 1.5585 | 27450 | 0.017 |
| 1.5591 | 27460 | 0.0166 |
| 1.5596 | 27470 | 0.0164 |
| 1.5602 | 27480 | 0.0172 |
| 1.5608 | 27490 | 0.0166 |
| 1.5613 | 27500 | 0.017 |
| 1.5619 | 27510 | 0.0167 |
| 1.5625 | 27520 | 0.0163 |
| 1.5631 | 27530 | 0.0164 |
| 1.5636 | 27540 | 0.0161 |
| 1.5642 | 27550 | 0.0167 |
| 1.5648 | 27560 | 0.0168 |
| 1.5653 | 27570 | 0.0165 |
| 1.5659 | 27580 | 0.0167 |
| 1.5665 | 27590 | 0.016 |
| 1.5670 | 27600 | 0.0158 |
| 1.5676 | 27610 | 0.0161 |
| 1.5682 | 27620 | 0.0164 |
| 1.5687 | 27630 | 0.0161 |
| 1.5693 | 27640 | 0.0173 |
| 1.5699 | 27650 | 0.0161 |
| 1.5704 | 27660 | 0.0177 |
| 1.5710 | 27670 | 0.0168 |
| 1.5716 | 27680 | 0.0167 |
| 1.5721 | 27690 | 0.0163 |
| 1.5727 | 27700 | 0.0161 |
| 1.5733 | 27710 | 0.016 |
| 1.5738 | 27720 | 0.016 |
| 1.5744 | 27730 | 0.0163 |
| 1.5750 | 27740 | 0.0171 |
| 1.5755 | 27750 | 0.0168 |
| 1.5761 | 27760 | 0.0159 |
| 1.5767 | 27770 | 0.0172 |
| 1.5772 | 27780 | 0.0166 |
| 1.5778 | 27790 | 0.0165 |
| 1.5784 | 27800 | 0.0168 |
| 1.5789 | 27810 | 0.0176 |
| 1.5795 | 27820 | 0.0177 |
| 1.5801 | 27830 | 0.016 |
| 1.5807 | 27840 | 0.0162 |
| 1.5812 | 27850 | 0.0162 |
| 1.5818 | 27860 | 0.0162 |
| 1.5824 | 27870 | 0.0164 |
| 1.5829 | 27880 | 0.0173 |
| 1.5835 | 27890 | 0.0168 |
| 1.5841 | 27900 | 0.0166 |
| 1.5846 | 27910 | 0.0162 |
| 1.5852 | 27920 | 0.0158 |
| 1.5858 | 27930 | 0.0154 |
| 1.5863 | 27940 | 0.016 |
| 1.5869 | 27950 | 0.0164 |
| 1.5875 | 27960 | 0.0166 |
| 1.5880 | 27970 | 0.0164 |
| 1.5886 | 27980 | 0.0163 |
| 1.5892 | 27990 | 0.0153 |
| 1.5897 | 28000 | 0.0163 |
| 1.5903 | 28010 | 0.0174 |
| 1.5909 | 28020 | 0.0163 |
| 1.5914 | 28030 | 0.0165 |
| 1.5920 | 28040 | 0.0172 |
| 1.5926 | 28050 | 0.0162 |
| 1.5931 | 28060 | 0.0159 |
| 1.5937 | 28070 | 0.0163 |
| 1.5943 | 28080 | 0.0152 |
| 1.5948 | 28090 | 0.0159 |
| 1.5954 | 28100 | 0.016 |
| 1.5960 | 28110 | 0.0165 |
| 1.5965 | 28120 | 0.0158 |
| 1.5971 | 28130 | 0.0167 |
| 1.5977 | 28140 | 0.0174 |
| 1.5983 | 28150 | 0.0171 |
| 1.5988 | 28160 | 0.0162 |
| 1.5994 | 28170 | 0.0171 |
| 1.6000 | 28180 | 0.0162 |
| 1.6005 | 28190 | 0.0157 |
| 1.6011 | 28200 | 0.0165 |
| 1.6017 | 28210 | 0.0165 |
| 1.6022 | 28220 | 0.0164 |
| 1.6028 | 28230 | 0.0166 |
| 1.6034 | 28240 | 0.0152 |
| 1.6039 | 28250 | 0.0172 |
| 1.6045 | 28260 | 0.0169 |
| 1.6051 | 28270 | 0.0164 |
| 1.6056 | 28280 | 0.0161 |
| 1.6062 | 28290 | 0.0155 |
| 1.6068 | 28300 | 0.0168 |
| 1.6073 | 28310 | 0.0164 |
| 1.6079 | 28320 | 0.0168 |
| 1.6085 | 28330 | 0.0152 |
| 1.6090 | 28340 | 0.0155 |
| 1.6096 | 28350 | 0.015 |
| 1.6102 | 28360 | 0.0166 |
| 1.6107 | 28370 | 0.0169 |
| 1.6113 | 28380 | 0.0149 |
| 1.6119 | 28390 | 0.0159 |
| 1.6124 | 28400 | 0.0168 |
| 1.6130 | 28410 | 0.0158 |
| 1.6136 | 28420 | 0.0172 |
| 1.6141 | 28430 | 0.0169 |
| 1.6147 | 28440 | 0.0164 |
| 1.6153 | 28450 | 0.0162 |
| 1.6159 | 28460 | 0.0169 |
| 1.6164 | 28470 | 0.0164 |
| 1.6170 | 28480 | 0.0155 |
| 1.6176 | 28490 | 0.0158 |
| 1.6181 | 28500 | 0.0156 |
| 1.6187 | 28510 | 0.0154 |
| 1.6193 | 28520 | 0.0162 |
| 1.6198 | 28530 | 0.0167 |
| 1.6204 | 28540 | 0.0172 |
| 1.6210 | 28550 | 0.017 |
| 1.6215 | 28560 | 0.0163 |
| 1.6221 | 28570 | 0.0162 |
| 1.6227 | 28580 | 0.0166 |
| 1.6232 | 28590 | 0.0154 |
| 1.6238 | 28600 | 0.0166 |
| 1.6244 | 28610 | 0.0157 |
| 1.6249 | 28620 | 0.0167 |
| 1.6255 | 28630 | 0.0168 |
| 1.6261 | 28640 | 0.0157 |
| 1.6266 | 28650 | 0.0165 |
| 1.6272 | 28660 | 0.0156 |
| 1.6278 | 28670 | 0.0162 |
| 1.6283 | 28680 | 0.0163 |
| 1.6289 | 28690 | 0.0165 |
| 1.6295 | 28700 | 0.0164 |
| 1.6300 | 28710 | 0.017 |
| 1.6306 | 28720 | 0.0167 |
| 1.6312 | 28730 | 0.0173 |
| 1.6317 | 28740 | 0.0167 |
| 1.6323 | 28750 | 0.0172 |
| 1.6329 | 28760 | 0.0154 |
| 1.6335 | 28770 | 0.0173 |
| 1.6340 | 28780 | 0.0166 |
| 1.6346 | 28790 | 0.0159 |
| 1.6352 | 28800 | 0.0153 |
| 1.6357 | 28810 | 0.0159 |
| 1.6363 | 28820 | 0.0161 |
| 1.6369 | 28830 | 0.0174 |
| 1.6374 | 28840 | 0.0164 |
| 1.6380 | 28850 | 0.0166 |
| 1.6386 | 28860 | 0.0164 |
| 1.6391 | 28870 | 0.016 |
| 1.6397 | 28880 | 0.0164 |
| 1.6403 | 28890 | 0.0165 |
| 1.6408 | 28900 | 0.0159 |
| 1.6414 | 28910 | 0.0159 |
| 1.6420 | 28920 | 0.0153 |
| 1.6425 | 28930 | 0.0159 |
| 1.6431 | 28940 | 0.0164 |
| 1.6437 | 28950 | 0.016 |
| 1.6442 | 28960 | 0.0156 |
| 1.6448 | 28970 | 0.016 |
| 1.6454 | 28980 | 0.016 |
| 1.6459 | 28990 | 0.0165 |
| 1.6465 | 29000 | 0.016 |
| 1.6471 | 29010 | 0.0163 |
| 1.6476 | 29020 | 0.0159 |
| 1.6482 | 29030 | 0.0157 |
| 1.6488 | 29040 | 0.017 |
| 1.6493 | 29050 | 0.016 |
| 1.6499 | 29060 | 0.017 |
| 1.6505 | 29070 | 0.0159 |
| 1.6511 | 29080 | 0.0159 |
| 1.6516 | 29090 | 0.0168 |
| 1.6522 | 29100 | 0.0168 |
| 1.6528 | 29110 | 0.0165 |
| 1.6533 | 29120 | 0.0162 |
| 1.6539 | 29130 | 0.017 |
| 1.6545 | 29140 | 0.0163 |
| 1.6550 | 29150 | 0.016 |
| 1.6556 | 29160 | 0.0163 |
| 1.6562 | 29170 | 0.0165 |
| 1.6567 | 29180 | 0.0164 |
| 1.6573 | 29190 | 0.0156 |
| 1.6579 | 29200 | 0.0162 |
| 1.6584 | 29210 | 0.0164 |
| 1.6590 | 29220 | 0.0153 |
| 1.6596 | 29230 | 0.0174 |
| 1.6601 | 29240 | 0.0171 |
| 1.6607 | 29250 | 0.0156 |
| 1.6613 | 29260 | 0.0163 |
| 1.6618 | 29270 | 0.0168 |
| 1.6624 | 29280 | 0.0161 |
| 1.6630 | 29290 | 0.0165 |
| 1.6635 | 29300 | 0.0165 |
| 1.6641 | 29310 | 0.0173 |
| 1.6647 | 29320 | 0.0164 |
| 1.6652 | 29330 | 0.0157 |
| 1.6658 | 29340 | 0.0154 |
| 1.6664 | 29350 | 0.0167 |
| 1.6670 | 29360 | 0.016 |
| 1.6675 | 29370 | 0.0169 |
| 1.6681 | 29380 | 0.0165 |
| 1.6687 | 29390 | 0.0167 |
| 1.6692 | 29400 | 0.0158 |
| 1.6698 | 29410 | 0.0159 |
| 1.6704 | 29420 | 0.0164 |
| 1.6709 | 29430 | 0.0166 |
| 1.6715 | 29440 | 0.017 |
| 1.6721 | 29450 | 0.0163 |
| 1.6726 | 29460 | 0.0165 |
| 1.6732 | 29470 | 0.0164 |
| 1.6738 | 29480 | 0.0159 |
| 1.6743 | 29490 | 0.0154 |
| 1.6749 | 29500 | 0.0161 |
| 1.6755 | 29510 | 0.0152 |
| 1.6760 | 29520 | 0.0157 |
| 1.6766 | 29530 | 0.0164 |
| 1.6772 | 29540 | 0.0156 |
| 1.6777 | 29550 | 0.0162 |
| 1.6783 | 29560 | 0.0166 |
| 1.6789 | 29570 | 0.0165 |
| 1.6794 | 29580 | 0.016 |
| 1.6800 | 29590 | 0.0162 |
| 1.6806 | 29600 | 0.0163 |
| 1.6811 | 29610 | 0.0157 |
| 1.6817 | 29620 | 0.0163 |
| 1.6823 | 29630 | 0.0163 |
| 1.6828 | 29640 | 0.0165 |
| 1.6834 | 29650 | 0.015 |
| 1.6840 | 29660 | 0.0164 |
| 1.6846 | 29670 | 0.0151 |
| 1.6851 | 29680 | 0.0164 |
| 1.6857 | 29690 | 0.0161 |
| 1.6863 | 29700 | 0.0162 |
| 1.6868 | 29710 | 0.016 |
| 1.6874 | 29720 | 0.0165 |
| 1.6880 | 29730 | 0.0159 |
| 1.6885 | 29740 | 0.0171 |
| 1.6891 | 29750 | 0.0157 |
| 1.6897 | 29760 | 0.0158 |
| 1.6902 | 29770 | 0.0162 |
| 1.6908 | 29780 | 0.0168 |
| 1.6914 | 29790 | 0.0165 |
| 1.6919 | 29800 | 0.0155 |
| 1.6925 | 29810 | 0.0163 |
| 1.6931 | 29820 | 0.0158 |
| 1.6936 | 29830 | 0.0163 |
| 1.6942 | 29840 | 0.0165 |
| 1.6948 | 29850 | 0.0155 |
| 1.6953 | 29860 | 0.0161 |
| 1.6959 | 29870 | 0.0163 |
| 1.6965 | 29880 | 0.0165 |
| 1.6970 | 29890 | 0.0159 |
| 1.6976 | 29900 | 0.0169 |
| 1.6982 | 29910 | 0.0154 |
| 1.6987 | 29920 | 0.0163 |
| 1.6993 | 29930 | 0.0161 |
| 1.6999 | 29940 | 0.0156 |
| 1.7004 | 29950 | 0.0157 |
| 1.7010 | 29960 | 0.016 |
| 1.7016 | 29970 | 0.0165 |
| 1.7022 | 29980 | 0.0161 |
| 1.7027 | 29990 | 0.0166 |
| 1.7033 | 30000 | 0.0152 |
| 1.7039 | 30010 | 0.0164 |
| 1.7044 | 30020 | 0.0156 |
| 1.7050 | 30030 | 0.0158 |
| 1.7056 | 30040 | 0.016 |
| 1.7061 | 30050 | 0.0154 |
| 1.7067 | 30060 | 0.0168 |
| 1.7073 | 30070 | 0.016 |
| 1.7078 | 30080 | 0.0163 |
| 1.7084 | 30090 | 0.0161 |
| 1.7090 | 30100 | 0.0159 |
| 1.7095 | 30110 | 0.0164 |
| 1.7101 | 30120 | 0.0159 |
| 1.7107 | 30130 | 0.0169 |
| 1.7112 | 30140 | 0.016 |
| 1.7118 | 30150 | 0.0157 |
| 1.7124 | 30160 | 0.016 |
| 1.7129 | 30170 | 0.016 |
| 1.7135 | 30180 | 0.0157 |
| 1.7141 | 30190 | 0.0151 |
| 1.7146 | 30200 | 0.0159 |
| 1.7152 | 30210 | 0.0158 |
| 1.7158 | 30220 | 0.0161 |
| 1.7163 | 30230 | 0.016 |
| 1.7169 | 30240 | 0.0172 |
| 1.7175 | 30250 | 0.0164 |
| 1.7180 | 30260 | 0.0164 |
| 1.7186 | 30270 | 0.0163 |
| 1.7192 | 30280 | 0.0159 |
| 1.7198 | 30290 | 0.0162 |
| 1.7203 | 30300 | 0.016 |
| 1.7209 | 30310 | 0.0168 |
| 1.7215 | 30320 | 0.0164 |
| 1.7220 | 30330 | 0.0169 |
| 1.7226 | 30340 | 0.0157 |
| 1.7232 | 30350 | 0.0163 |
| 1.7237 | 30360 | 0.0157 |
| 1.7243 | 30370 | 0.0165 |
| 1.7249 | 30380 | 0.0176 |
| 1.7254 | 30390 | 0.0161 |
| 1.7260 | 30400 | 0.0165 |
| 1.7266 | 30410 | 0.0152 |
| 1.7271 | 30420 | 0.0166 |
| 1.7277 | 30430 | 0.0158 |
| 1.7283 | 30440 | 0.0168 |
| 1.7288 | 30450 | 0.0162 |
| 1.7294 | 30460 | 0.0164 |
| 1.7300 | 30470 | 0.0165 |
| 1.7305 | 30480 | 0.0174 |
| 1.7311 | 30490 | 0.0167 |
| 1.7317 | 30500 | 0.0162 |
| 1.7322 | 30510 | 0.0157 |
| 1.7328 | 30520 | 0.0162 |
| 1.7334 | 30530 | 0.0163 |
| 1.7339 | 30540 | 0.0163 |
| 1.7345 | 30550 | 0.0167 |
| 1.7351 | 30560 | 0.0167 |
| 1.7356 | 30570 | 0.0158 |
| 1.7362 | 30580 | 0.0169 |
| 1.7368 | 30590 | 0.0168 |
| 1.7374 | 30600 | 0.0158 |
| 1.7379 | 30610 | 0.0161 |
| 1.7385 | 30620 | 0.0156 |
| 1.7391 | 30630 | 0.0161 |
| 1.7396 | 30640 | 0.0156 |
| 1.7402 | 30650 | 0.0155 |
| 1.7408 | 30660 | 0.016 |
| 1.7413 | 30670 | 0.0167 |
| 1.7419 | 30680 | 0.0165 |
| 1.7425 | 30690 | 0.0157 |
| 1.7430 | 30700 | 0.0157 |
| 1.7436 | 30710 | 0.0164 |
| 1.7442 | 30720 | 0.0167 |
| 1.7447 | 30730 | 0.0163 |
| 1.7453 | 30740 | 0.0162 |
| 1.7459 | 30750 | 0.0159 |
| 1.7464 | 30760 | 0.017 |
| 1.7470 | 30770 | 0.0159 |
| 1.7476 | 30780 | 0.016 |
| 1.7481 | 30790 | 0.0168 |
| 1.7487 | 30800 | 0.0162 |
| 1.7493 | 30810 | 0.016 |
| 1.7498 | 30820 | 0.0158 |
| 1.7504 | 30830 | 0.0163 |
| 1.7510 | 30840 | 0.0164 |
| 1.7515 | 30850 | 0.0159 |
| 1.7521 | 30860 | 0.0166 |
| 1.7527 | 30870 | 0.0161 |
| 1.7533 | 30880 | 0.0167 |
| 1.7538 | 30890 | 0.0156 |
| 1.7544 | 30900 | 0.0169 |
| 1.7550 | 30910 | 0.0154 |
| 1.7555 | 30920 | 0.0161 |
| 1.7561 | 30930 | 0.016 |
| 1.7567 | 30940 | 0.0158 |
| 1.7572 | 30950 | 0.0169 |
| 1.7578 | 30960 | 0.0151 |
| 1.7584 | 30970 | 0.0161 |
| 1.7589 | 30980 | 0.017 |
| 1.7595 | 30990 | 0.0166 |
| 1.7601 | 31000 | 0.017 |
| 1.7606 | 31010 | 0.0164 |
| 1.7612 | 31020 | 0.0161 |
| 1.7618 | 31030 | 0.0166 |
| 1.7623 | 31040 | 0.0154 |
| 1.7629 | 31050 | 0.0166 |
| 1.7635 | 31060 | 0.0159 |
| 1.7640 | 31070 | 0.0164 |
| 1.7646 | 31080 | 0.0156 |
| 1.7652 | 31090 | 0.016 |
| 1.7657 | 31100 | 0.0168 |
| 1.7663 | 31110 | 0.0164 |
| 1.7669 | 31120 | 0.0158 |
| 1.7674 | 31130 | 0.0165 |
| 1.7680 | 31140 | 0.015 |
| 1.7686 | 31150 | 0.0169 |
| 1.7691 | 31160 | 0.0154 |
| 1.7697 | 31170 | 0.0162 |
| 1.7703 | 31180 | 0.0171 |
| 1.7709 | 31190 | 0.0153 |
| 1.7714 | 31200 | 0.016 |
| 1.7720 | 31210 | 0.0154 |
| 1.7726 | 31220 | 0.0148 |
| 1.7731 | 31230 | 0.0165 |
| 1.7737 | 31240 | 0.0165 |
| 1.7743 | 31250 | 0.0163 |
| 1.7748 | 31260 | 0.0162 |
| 1.7754 | 31270 | 0.016 |
| 1.7760 | 31280 | 0.0164 |
| 1.7765 | 31290 | 0.0156 |
| 1.7771 | 31300 | 0.0149 |
| 1.7777 | 31310 | 0.0163 |
| 1.7782 | 31320 | 0.0166 |
| 1.7788 | 31330 | 0.0172 |
| 1.7794 | 31340 | 0.0156 |
| 1.7799 | 31350 | 0.0149 |
| 1.7805 | 31360 | 0.0164 |
| 1.7811 | 31370 | 0.0168 |
| 1.7816 | 31380 | 0.0144 |
| 1.7822 | 31390 | 0.0157 |
| 1.7828 | 31400 | 0.0169 |
| 1.7833 | 31410 | 0.0151 |
| 1.7839 | 31420 | 0.0164 |
| 1.7845 | 31430 | 0.016 |
| 1.7850 | 31440 | 0.0157 |
| 1.7856 | 31450 | 0.0165 |
| 1.7862 | 31460 | 0.0158 |
| 1.7867 | 31470 | 0.0159 |
| 1.7873 | 31480 | 0.0161 |
| 1.7879 | 31490 | 0.0166 |
| 1.7885 | 31500 | 0.0155 |
| 1.7890 | 31510 | 0.0159 |
| 1.7896 | 31520 | 0.0155 |
| 1.7902 | 31530 | 0.0163 |
| 1.7907 | 31540 | 0.0147 |
| 1.7913 | 31550 | 0.017 |
| 1.7919 | 31560 | 0.0164 |
| 1.7924 | 31570 | 0.0157 |
| 1.7930 | 31580 | 0.0159 |
| 1.7936 | 31590 | 0.0165 |
| 1.7941 | 31600 | 0.016 |
| 1.7947 | 31610 | 0.0161 |
| 1.7953 | 31620 | 0.0173 |
| 1.7958 | 31630 | 0.0161 |
| 1.7964 | 31640 | 0.0158 |
| 1.7970 | 31650 | 0.0157 |
| 1.7975 | 31660 | 0.0156 |
| 1.7981 | 31670 | 0.0157 |
| 1.7987 | 31680 | 0.0163 |
| 1.7992 | 31690 | 0.0167 |
| 1.7998 | 31700 | 0.0165 |
| 1.8004 | 31710 | 0.0166 |
| 1.8009 | 31720 | 0.0163 |
| 1.8015 | 31730 | 0.0156 |
| 1.8021 | 31740 | 0.0161 |
| 1.8026 | 31750 | 0.0164 |
| 1.8032 | 31760 | 0.0153 |
| 1.8038 | 31770 | 0.0166 |
| 1.8043 | 31780 | 0.0161 |
| 1.8049 | 31790 | 0.0167 |
| 1.8055 | 31800 | 0.0166 |
| 1.8061 | 31810 | 0.0165 |
| 1.8066 | 31820 | 0.016 |
| 1.8072 | 31830 | 0.0154 |
| 1.8078 | 31840 | 0.0167 |
| 1.8083 | 31850 | 0.0154 |
| 1.8089 | 31860 | 0.0166 |
| 1.8095 | 31870 | 0.0151 |
| 1.8100 | 31880 | 0.0162 |
| 1.8106 | 31890 | 0.0161 |
| 1.8112 | 31900 | 0.0163 |
| 1.8117 | 31910 | 0.0159 |
| 1.8123 | 31920 | 0.0166 |
| 1.8129 | 31930 | 0.0156 |
| 1.8134 | 31940 | 0.0158 |
| 1.8140 | 31950 | 0.0159 |
| 1.8146 | 31960 | 0.0159 |
| 1.8151 | 31970 | 0.0152 |
| 1.8157 | 31980 | 0.0161 |
| 1.8163 | 31990 | 0.0157 |
| 1.8168 | 32000 | 0.0154 |
| 1.8174 | 32010 | 0.0162 |
| 1.8180 | 32020 | 0.0154 |
| 1.8185 | 32030 | 0.0157 |
| 1.8191 | 32040 | 0.016 |
| 1.8197 | 32050 | 0.0156 |
| 1.8202 | 32060 | 0.0157 |
| 1.8208 | 32070 | 0.0157 |
| 1.8214 | 32080 | 0.0161 |
| 1.8219 | 32090 | 0.0163 |
| 1.8225 | 32100 | 0.0153 |
| 1.8231 | 32110 | 0.0153 |
| 1.8237 | 32120 | 0.0161 |
| 1.8242 | 32130 | 0.0152 |
| 1.8248 | 32140 | 0.016 |
| 1.8254 | 32150 | 0.016 |
| 1.8259 | 32160 | 0.0167 |
| 1.8265 | 32170 | 0.0154 |
| 1.8271 | 32180 | 0.0166 |
| 1.8276 | 32190 | 0.015 |
| 1.8282 | 32200 | 0.0163 |
| 1.8288 | 32210 | 0.0159 |
| 1.8293 | 32220 | 0.0153 |
| 1.8299 | 32230 | 0.015 |
| 1.8305 | 32240 | 0.0159 |
| 1.8310 | 32250 | 0.0159 |
| 1.8316 | 32260 | 0.0163 |
| 1.8322 | 32270 | 0.0157 |
| 1.8327 | 32280 | 0.0165 |
| 1.8333 | 32290 | 0.0158 |
| 1.8339 | 32300 | 0.0164 |
| 1.8344 | 32310 | 0.0159 |
| 1.8350 | 32320 | 0.0154 |
| 1.8356 | 32330 | 0.0159 |
| 1.8361 | 32340 | 0.0156 |
| 1.8367 | 32350 | 0.0162 |
| 1.8373 | 32360 | 0.0159 |
| 1.8378 | 32370 | 0.0164 |
| 1.8384 | 32380 | 0.016 |
| 1.8390 | 32390 | 0.0166 |
| 1.8396 | 32400 | 0.0151 |
| 1.8401 | 32410 | 0.0161 |
| 1.8407 | 32420 | 0.0158 |
| 1.8413 | 32430 | 0.0169 |
| 1.8418 | 32440 | 0.0162 |
| 1.8424 | 32450 | 0.016 |
| 1.8430 | 32460 | 0.0162 |
| 1.8435 | 32470 | 0.0159 |
| 1.8441 | 32480 | 0.0163 |
| 1.8447 | 32490 | 0.0153 |
| 1.8452 | 32500 | 0.017 |
| 1.8458 | 32510 | 0.0163 |
| 1.8464 | 32520 | 0.016 |
| 1.8469 | 32530 | 0.017 |
| 1.8475 | 32540 | 0.0151 |
| 1.8481 | 32550 | 0.0159 |
| 1.8486 | 32560 | 0.0163 |
| 1.8492 | 32570 | 0.015 |
| 1.8498 | 32580 | 0.0163 |
| 1.8503 | 32590 | 0.0155 |
| 1.8509 | 32600 | 0.0168 |
| 1.8515 | 32610 | 0.0149 |
| 1.8520 | 32620 | 0.0168 |
| 1.8526 | 32630 | 0.0163 |
| 1.8532 | 32640 | 0.0158 |
| 1.8537 | 32650 | 0.0161 |
| 1.8543 | 32660 | 0.0157 |
| 1.8549 | 32670 | 0.0151 |
| 1.8554 | 32680 | 0.0153 |
| 1.8560 | 32690 | 0.0162 |
| 1.8566 | 32700 | 0.0173 |
| 1.8572 | 32710 | 0.0159 |
| 1.8577 | 32720 | 0.0167 |
| 1.8583 | 32730 | 0.0166 |
| 1.8589 | 32740 | 0.0162 |
| 1.8594 | 32750 | 0.0162 |
| 1.8600 | 32760 | 0.016 |
| 1.8606 | 32770 | 0.016 |
| 1.8611 | 32780 | 0.0159 |
| 1.8617 | 32790 | 0.0161 |
| 1.8623 | 32800 | 0.0154 |
| 1.8628 | 32810 | 0.0164 |
| 1.8634 | 32820 | 0.0164 |
| 1.8640 | 32830 | 0.0156 |
| 1.8645 | 32840 | 0.016 |
| 1.8651 | 32850 | 0.0163 |
| 1.8657 | 32860 | 0.0157 |
| 1.8662 | 32870 | 0.0158 |
| 1.8668 | 32880 | 0.0155 |
| 1.8674 | 32890 | 0.0152 |
| 1.8679 | 32900 | 0.0163 |
| 1.8685 | 32910 | 0.0166 |
| 1.8691 | 32920 | 0.0163 |
| 1.8696 | 32930 | 0.0149 |
| 1.8702 | 32940 | 0.0159 |
| 1.8708 | 32950 | 0.0164 |
| 1.8713 | 32960 | 0.0167 |
| 1.8719 | 32970 | 0.0166 |
| 1.8725 | 32980 | 0.0162 |
| 1.8730 | 32990 | 0.0158 |
| 1.8736 | 33000 | 0.0158 |
| 1.8742 | 33010 | 0.0156 |
| 1.8748 | 33020 | 0.0173 |
| 1.8753 | 33030 | 0.0169 |
| 1.8759 | 33040 | 0.016 |
| 1.8765 | 33050 | 0.0156 |
| 1.8770 | 33060 | 0.0148 |
| 1.8776 | 33070 | 0.0162 |
| 1.8782 | 33080 | 0.0171 |
| 1.8787 | 33090 | 0.0151 |
| 1.8793 | 33100 | 0.0161 |
| 1.8799 | 33110 | 0.016 |
| 1.8804 | 33120 | 0.0168 |
| 1.8810 | 33130 | 0.0159 |
| 1.8816 | 33140 | 0.0156 |
| 1.8821 | 33150 | 0.0155 |
| 1.8827 | 33160 | 0.0155 |
| 1.8833 | 33170 | 0.0162 |
| 1.8838 | 33180 | 0.0163 |
| 1.8844 | 33190 | 0.0151 |
| 1.8850 | 33200 | 0.0161 |
| 1.8855 | 33210 | 0.0148 |
| 1.8861 | 33220 | 0.0163 |
| 1.8867 | 33230 | 0.0159 |
| 1.8872 | 33240 | 0.0165 |
| 1.8878 | 33250 | 0.0154 |
| 1.8884 | 33260 | 0.0155 |
| 1.8889 | 33270 | 0.0153 |
| 1.8895 | 33280 | 0.0159 |
| 1.8901 | 33290 | 0.0161 |
| 1.8906 | 33300 | 0.0159 |
| 1.8912 | 33310 | 0.0159 |
| 1.8918 | 33320 | 0.015 |
| 1.8924 | 33330 | 0.0159 |
| 1.8929 | 33340 | 0.0163 |
| 1.8935 | 33350 | 0.0156 |
| 1.8941 | 33360 | 0.0153 |
| 1.8946 | 33370 | 0.0163 |
| 1.8952 | 33380 | 0.0159 |
| 1.8958 | 33390 | 0.0168 |
| 1.8963 | 33400 | 0.016 |
| 1.8969 | 33410 | 0.0162 |
| 1.8975 | 33420 | 0.0157 |
| 1.8980 | 33430 | 0.0172 |
| 1.8986 | 33440 | 0.0155 |
| 1.8992 | 33450 | 0.0151 |
| 1.8997 | 33460 | 0.0152 |
| 1.9003 | 33470 | 0.0162 |
| 1.9009 | 33480 | 0.0154 |
| 1.9014 | 33490 | 0.0163 |
| 1.9020 | 33500 | 0.0157 |
| 1.9026 | 33510 | 0.0159 |
| 1.9031 | 33520 | 0.0168 |
| 1.9037 | 33530 | 0.0153 |
| 1.9043 | 33540 | 0.0156 |
| 1.9048 | 33550 | 0.0162 |
| 1.9054 | 33560 | 0.0161 |
| 1.9060 | 33570 | 0.0158 |
| 1.9065 | 33580 | 0.0169 |
| 1.9071 | 33590 | 0.0158 |
| 1.9077 | 33600 | 0.0168 |
| 1.9082 | 33610 | 0.0157 |
| 1.9088 | 33620 | 0.0155 |
| 1.9094 | 33630 | 0.0159 |
| 1.9100 | 33640 | 0.016 |
| 1.9105 | 33650 | 0.0158 |
| 1.9111 | 33660 | 0.0155 |
| 1.9117 | 33670 | 0.0166 |
| 1.9122 | 33680 | 0.0159 |
| 1.9128 | 33690 | 0.0168 |
| 1.9134 | 33700 | 0.0166 |
| 1.9139 | 33710 | 0.0163 |
| 1.9145 | 33720 | 0.0154 |
| 1.9151 | 33730 | 0.0165 |
| 1.9156 | 33740 | 0.0162 |
| 1.9162 | 33750 | 0.0157 |
| 1.9168 | 33760 | 0.0153 |
| 1.9173 | 33770 | 0.0159 |
| 1.9179 | 33780 | 0.0155 |
| 1.9185 | 33790 | 0.0164 |
| 1.9190 | 33800 | 0.0166 |
| 1.9196 | 33810 | 0.0156 |
| 1.9202 | 33820 | 0.0158 |
| 1.9207 | 33830 | 0.0163 |
| 1.9213 | 33840 | 0.0158 |
| 1.9219 | 33850 | 0.0167 |
| 1.9224 | 33860 | 0.0159 |
| 1.9230 | 33870 | 0.0158 |
| 1.9236 | 33880 | 0.0157 |
| 1.9241 | 33890 | 0.0156 |
| 1.9247 | 33900 | 0.0162 |
| 1.9253 | 33910 | 0.0151 |
| 1.9259 | 33920 | 0.0157 |
| 1.9264 | 33930 | 0.0166 |
| 1.9270 | 33940 | 0.015 |
| 1.9276 | 33950 | 0.0158 |
| 1.9281 | 33960 | 0.017 |
| 1.9287 | 33970 | 0.0155 |
| 1.9293 | 33980 | 0.0151 |
| 1.9298 | 33990 | 0.016 |
| 1.9304 | 34000 | 0.0163 |
| 1.9310 | 34010 | 0.0163 |
| 1.9315 | 34020 | 0.0167 |
| 1.9321 | 34030 | 0.0152 |
| 1.9327 | 34040 | 0.0161 |
| 1.9332 | 34050 | 0.0156 |
| 1.9338 | 34060 | 0.0167 |
| 1.9344 | 34070 | 0.0154 |
| 1.9349 | 34080 | 0.0147 |
| 1.9355 | 34090 | 0.0155 |
| 1.9361 | 34100 | 0.0171 |
| 1.9366 | 34110 | 0.0156 |
| 1.9372 | 34120 | 0.0153 |
| 1.9378 | 34130 | 0.0156 |
| 1.9383 | 34140 | 0.0144 |
| 1.9389 | 34150 | 0.0163 |
| 1.9395 | 34160 | 0.0168 |
| 1.9400 | 34170 | 0.0157 |
| 1.9406 | 34180 | 0.0166 |
| 1.9412 | 34190 | 0.0164 |
| 1.9417 | 34200 | 0.0145 |
| 1.9423 | 34210 | 0.0167 |
| 1.9429 | 34220 | 0.016 |
| 1.9435 | 34230 | 0.0161 |
| 1.9440 | 34240 | 0.0163 |
| 1.9446 | 34250 | 0.0165 |
| 1.9452 | 34260 | 0.0166 |
| 1.9457 | 34270 | 0.0159 |
| 1.9463 | 34280 | 0.0154 |
| 1.9469 | 34290 | 0.016 |
| 1.9474 | 34300 | 0.015 |
| 1.9480 | 34310 | 0.0152 |
| 1.9486 | 34320 | 0.016 |
| 1.9491 | 34330 | 0.0162 |
| 1.9497 | 34340 | 0.0152 |
| 1.9503 | 34350 | 0.0154 |
| 1.9508 | 34360 | 0.0169 |
| 1.9514 | 34370 | 0.0161 |
| 1.9520 | 34380 | 0.0158 |
| 1.9525 | 34390 | 0.0153 |
| 1.9531 | 34400 | 0.0157 |
| 1.9537 | 34410 | 0.0162 |
| 1.9542 | 34420 | 0.0159 |
| 1.9548 | 34430 | 0.0163 |
| 1.9554 | 34440 | 0.0156 |
| 1.9559 | 34450 | 0.016 |
| 1.9565 | 34460 | 0.0159 |
| 1.9571 | 34470 | 0.0157 |
| 1.9576 | 34480 | 0.0155 |
| 1.9582 | 34490 | 0.0167 |
| 1.9588 | 34500 | 0.0157 |
| 1.9593 | 34510 | 0.0155 |
| 1.9599 | 34520 | 0.0155 |
| 1.9605 | 34530 | 0.0158 |
| 1.9611 | 34540 | 0.0157 |
| 1.9616 | 34550 | 0.0162 |
| 1.9622 | 34560 | 0.0153 |
| 1.9628 | 34570 | 0.0151 |
| 1.9633 | 34580 | 0.0162 |
| 1.9639 | 34590 | 0.0152 |
| 1.9645 | 34600 | 0.0164 |
| 1.9650 | 34610 | 0.0154 |
| 1.9656 | 34620 | 0.0164 |
| 1.9662 | 34630 | 0.0167 |
| 1.9667 | 34640 | 0.016 |
| 1.9673 | 34650 | 0.0154 |
| 1.9679 | 34660 | 0.0159 |
| 1.9684 | 34670 | 0.0151 |
| 1.9690 | 34680 | 0.0156 |
| 1.9696 | 34690 | 0.0153 |
| 1.9701 | 34700 | 0.0153 |
| 1.9707 | 34710 | 0.0154 |
| 1.9713 | 34720 | 0.0168 |
| 1.9718 | 34730 | 0.0162 |
| 1.9724 | 34740 | 0.0154 |
| 1.9730 | 34750 | 0.0165 |
| 1.9735 | 34760 | 0.0163 |
| 1.9741 | 34770 | 0.0162 |
| 1.9747 | 34780 | 0.0162 |
| 1.9752 | 34790 | 0.0155 |
| 1.9758 | 34800 | 0.0164 |
| 1.9764 | 34810 | 0.0153 |
| 1.9769 | 34820 | 0.0155 |
| 1.9775 | 34830 | 0.0165 |
| 1.9781 | 34840 | 0.0168 |
| 1.9787 | 34850 | 0.0161 |
| 1.9792 | 34860 | 0.0155 |
| 1.9798 | 34870 | 0.0159 |
| 1.9804 | 34880 | 0.0148 |
| 1.9809 | 34890 | 0.0155 |
| 1.9815 | 34900 | 0.0165 |
| 1.9821 | 34910 | 0.016 |
| 1.9826 | 34920 | 0.0164 |
| 1.9832 | 34930 | 0.0156 |
| 1.9838 | 34940 | 0.016 |
| 1.9843 | 34950 | 0.0155 |
| 1.9849 | 34960 | 0.0157 |
| 1.9855 | 34970 | 0.0161 |
| 1.9860 | 34980 | 0.0159 |
| 1.9866 | 34990 | 0.0156 |
| 1.9872 | 35000 | 0.0166 |
| 1.9877 | 35010 | 0.0154 |
| 1.9883 | 35020 | 0.0156 |
| 1.9889 | 35030 | 0.0155 |
| 1.9894 | 35040 | 0.0168 |
| 1.9900 | 35050 | 0.0163 |
| 1.9906 | 35060 | 0.0159 |
| 1.9911 | 35070 | 0.0164 |
| 1.9917 | 35080 | 0.0161 |
| 1.9923 | 35090 | 0.0158 |
| 1.9928 | 35100 | 0.0157 |
| 1.9934 | 35110 | 0.0159 |
| 1.9940 | 35120 | 0.016 |
| 1.9945 | 35130 | 0.0153 |
| 1.9951 | 35140 | 0.0158 |
| 1.9957 | 35150 | 0.0162 |
| 1.9963 | 35160 | 0.0169 |
| 1.9968 | 35170 | 0.0155 |
| 1.9974 | 35180 | 0.0159 |
| 1.9980 | 35190 | 0.0152 |
| 1.9985 | 35200 | 0.0151 |
| 1.9991 | 35210 | 0.0151 |
| 1.9997 | 35220 | 0.0158 |
| 2.0002 | 35230 | 0.0151 |
| 2.0008 | 35240 | 0.0145 |
| 2.0014 | 35250 | 0.0143 |
| 2.0019 | 35260 | 0.015 |
| 2.0025 | 35270 | 0.0147 |
| 2.0031 | 35280 | 0.0154 |
| 2.0036 | 35290 | 0.0148 |
| 2.0042 | 35300 | 0.0148 |
</details>
### Framework Versions
- Python: 3.12.0
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.3
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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| null |
Non_BioNLP
|
# SentenceTransformer based on Tochka-AI/ruRoPEBert-e5-base-512
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) on the csv dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Tochka-AI/ruRoPEBert-e5-base-512](https://huggingface.co/Tochka-AI/ruRoPEBert-e5-base-512) <!-- at revision faaef0a859c7623bafa93254af2bedcffab6e361 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RoPEBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("asaakyan/ruRoPEBert-e5-base-512-allRU-authorship")
# Run inference
sentences = [
'Решил своей девушке сделать подарок ко дню рождения. А именно - котенка (давно хотела завести). Нашел девушку, которая отдает маленького милого котенка в добрые руки. Созвонились, договорились о встрече, хозяйка сказала, что как только вернется домой с магазина - можно приехать и забрать пушистого зверька. Через час поступил звонок, своей девушке сказал, что надо по делам рабочим сгонять. В итоге девушка оказалась непорядочной, когда приехал, она сообщила, что котенка уже забрали 10 минут назад, мол, приехали раньше меня. Но это полбеды.\nВечером следующего дня я понял, что записать в девушку в телефоне как "Катя(Котёнок)" было очень глупой идеей...',
'на работе выключили свет. Диспетчер сидит в своей программе, делает табеля. бесперебойник периодически пищит.\n\\- д не пищи ты!!\n\\- пиик пиик пиик\n\\- д знаю я что света нет, не ори!!\n\\- пиик пиик пиик\n\\- ну сейчас сейчас, доделаю и выключу\n\\- пиик пиик пиик\n\\- какой же ты глупый!!!\n\\- пиик пиик пиик\n\\- как же ты достал, ну сейчас, еще 10 человек осталось и выключаю\n\\- пиииииииииик...........\nкомпьютер гаснет.....пауза......злая тётенька диспетчер осознает что произошло\n\\- он что вырубился????? так я же ничего не сохранила!!!! какого черта??? мне же теперь все заново делать!!!!! что за компьютер глупый такой!!!\nпочти собралась сисадмину звонить )))) но не стала)))\nЗа компьютер обидно, он же из последних сил терпел, кричал, а его глупым...)',
'Чтобы факт, признанный наукой назвать "дурацким стереотипом"? Ну да, как по мне, требуется осведомлённость повыше обывательской. И ещё раз. С вами никто не спорит. Бывает всяко, в том числе так, как вы написали. Вариантов столько же, сколько живых людей. А паттерны, усвоенные в семье ребёнком, присущи большому количеству взрослых.\nИ речь изначально шла о модели семьи и вариантах поведения, связанных с ней. То есть "мальчик копирует отношение папы к маме". Это не значит, что его ждёт такая же судьба, у него будет такой же характер, или он тоже разведётся в 27. Но по умолчанию, пока не наживёт своего опыта (а некоторые и дальше), мальчик будет опираться на то, что видел в семье. Ваш пример с эмпатией - верен. Только в этом контексте ничего не доказывает.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 2,465,773 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 97 tokens</li><li>mean: 304.32 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 93 tokens</li><li>mean: 304.08 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>0: ~49.90%</li><li>1: ~50.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>С целью повышения безопасности я ограничил сумму перевода со своих счетов 30 тысячами, а тут нужно жене перекинуть поболее.<br>Для голосового подтверждения я раньше звонил на номер <PERSON>, переходил на пункт ввода кода обслуживания в тональном режиме и далее подтверждал сумму и получателя.<br>Позвонив этот раз в Сбербанк ответила мне робот, голосовой помощник и обещала мне помочь, только скажи. Говорю "подтвердить операцию" на что робот говорит что не понимает меня. И со второй попытки тоже. После этого, не найдя контакта она предлагает мне соединить с оператором, на что я говорю Да, и-и-и, пара секунд и короткие гудки.<br><PERSON> повторно и сразу говорю "соединить с оператором", попадаю на живого человека, который выслушав меня говорит: ноль проблем, переключаю на ввод кода. И кто меня ждёт с той стороны? Правильно, опять не доделанный ИИ, который опять меня не понимает.<br>Прошу соединить с живым человеком, соединяет. На что новый сотрудник говорит, что по коду нельзя теперь авторизоваться, т...</code> | <code>Очень часто вижу негативные посты про опсосов и банки. И вот что хочу сказать: бесспорно это просто бизнес, но в тоже время как не ругай банки, они многим нужны. Так вот на волне негатива хочу сказать немного хорошего.<br>У меня, как и у большинства россиян зелёный банк. За время моего сотрудничества с ним я переоценил свое отношение к нему. Да, очереди, проблемы и косяки, были, но или мне повезло или как, но косяки все решились мне не в убыток.<br>Итак: с момента как у меня появилась зарплатная карта:<br>\- я купил первую квартиру в ипотеку, студию, деньги дал банк. Ипотеку гасил как мог быстро. Результат: кредит погасил до того момента, когда проценты превысили рыночную стоимость на момент закрытия кредита. Причем даже с профитом. И это при ставке 12.5%.<br>\- я купил бу машину, взяв потребительский кредит, погасил, переплата 12 тыс, но взял машину на 50% дороже, чем взял бы на свои накопления.<br>\- банк предложил кредиту виза серебряная первый год бесплатно. Взял, научился льготному периоду. Закр...</code> | <code>1</code> |
| <code>Тоже был на Канарах, там проходило одно студенческое мероприятие. Как-то вечером иду с знакомым поляком по вечернему городу, заходим в случайный бар за пивом. Иду уверенным шагом к барной стойке и тут слышу сзади меня: "Ооо, курваа..." и дальше не переводимая игра слов на польском. Оказалось, что мой знакомый встретил своего друга поляка и при этом, оба друг другу не говорили о том, что собираются куда-либо поехать. Вот так тоже подумаешь, несколько тысяч километров и два знакомых встречаются случайно.<br>Еще одну историю услышал от знакомых. Дело было в Барселоне. На пляже услышали родную речь и разговорились. Оказалось, мало того, что с одного города, так еще и с одного дома, только разные подъезды.</code> | <code>Есть у меня друг. Ему за тридцать, живет в деревне с матерью и сестрой. Все его друзья перебрались в город. И излюбленным его нытьем стало то, что они зазнались, зазвездились, каблуки и т.д. Звонит как то, говорит что в городе и предлагает сходить на новоселье к одному из них, своих старых друзей. Я малость удивился, с человеком не знаком, зачем я то пойду. Уверяет меня, что друг человек компанейский, новым друзьям всегда рад, а квартира его новая вообще в моем доме расположена, в другом подъезде ток. Ну, думаю, раз так, зайду, в случае чего можно и уйти под каким нибудь предлогом. Приходим, компании никакой нет, но накрыт стол в гостиной, бегает пацаненок лет пяти, с кухни выходит сильно беременная супруга. Познакомились с новыми соседями, сели, выпили. Не буду затягивать с рассказом: друга моего деревенского понесло, бутылки на троих ему было мало, сгонял еще за одной, потом еще...Потом горячо уговаривал всех в караоке поехать, ну а что, душа просит праздника. Утащить его с этой кв...</code> | <code>0</code> |
| <code>> никто из русских в здравом уме не пытается озвучить мысль-"всех чеченов надо убить"<br>Я искал какую-нибудь отметку о сарказме, но не нашел. Эту мысль озвучивали и озвучивают и сейчас везде - от политических деятелей до каких-нибудь Петровичей на кухне. Особенно легко такие призывы найти на Пикабу, и я тебя уверяю, они будут заплюсованы. Или спорить с этим будешь? Или скажешь, что каждый, кто это говорит не подходит под твое "в здравом уме" и потому снова несчитово?)) Тогда могу только восхититься твоей предусмотрительностью.<br>> Тем более-осуществить. Не в нашем это обычае, хотя и сил и средств достаточно, кмк.<br>Так пытались же, я расписал достаточно подробно. Убить однажды половину, в следующий раз - треть, а в последний раз четверть - разве нельзя считать это попытками? Просто не получилось, не смогли. Вся надежда на ОМП теперь.<br>Ну и вообще, не в тему твой комментарий, неполный он какой-то.</code> | <code>Да потому что вы несете бред, не связанный с реальностью. Это как дискутировать о том, что небо зеленое, а трава фиолетвая и в пупырушек. В вашем розовом мире писюнов злое правительство не дает нищасным папкам детей? Вам обьяснили почему вы можете видеть такую картину, почему существует перекос и откуда растут ноги у процента принятия решений. Но вам, походу, нужно чтобы суды исходили не из интересов ребенка, а из интересов вечно обделенных. Я то не против, я вообще считаю, что закон о принудительном оставлении ребенка в любом случае с отцом не плохо бы укрепил желание некоторых сохранять семью и выбирать адекватных партнеров</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 35
- `per_device_eval_batch_size`: 35
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 35
- `per_device_eval_batch_size`: 35
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0006 | 10 | 0.1108 |
| 0.0011 | 20 | 0.1075 |
| 0.0017 | 30 | 0.1033 |
| 0.0023 | 40 | 0.0971 |
| 0.0028 | 50 | 0.0852 |
| 0.0034 | 60 | 0.0693 |
| 0.0040 | 70 | 0.0599 |
| 0.0045 | 80 | 0.0478 |
| 0.0051 | 90 | 0.035 |
| 0.0057 | 100 | 0.03 |
| 0.0062 | 110 | 0.0285 |
| 0.0068 | 120 | 0.0292 |
| 0.0074 | 130 | 0.0288 |
| 0.0079 | 140 | 0.0282 |
| 0.0085 | 150 | 0.028 |
| 0.0091 | 160 | 0.0271 |
| 0.0097 | 170 | 0.0278 |
| 0.0102 | 180 | 0.0272 |
| 0.0108 | 190 | 0.0273 |
| 0.0114 | 200 | 0.0273 |
| 0.0119 | 210 | 0.0266 |
| 0.0125 | 220 | 0.0267 |
| 0.0131 | 230 | 0.0271 |
| 0.0136 | 240 | 0.0269 |
| 0.0142 | 250 | 0.0271 |
| 0.0148 | 260 | 0.0267 |
| 0.0153 | 270 | 0.0272 |
| 0.0159 | 280 | 0.0268 |
| 0.0165 | 290 | 0.0263 |
| 0.0170 | 300 | 0.0257 |
| 0.0176 | 310 | 0.0257 |
| 0.0182 | 320 | 0.0259 |
| 0.0187 | 330 | 0.0256 |
| 0.0193 | 340 | 0.0249 |
| 0.0199 | 350 | 0.025 |
| 0.0204 | 360 | 0.0251 |
| 0.0210 | 370 | 0.0244 |
| 0.0216 | 380 | 0.0244 |
| 0.0221 | 390 | 0.0246 |
| 0.0227 | 400 | 0.0248 |
| 0.0233 | 410 | 0.0243 |
| 0.0238 | 420 | 0.0251 |
| 0.0244 | 430 | 0.026 |
| 0.0250 | 440 | 0.0245 |
| 0.0255 | 450 | 0.0249 |
| 0.0261 | 460 | 0.0241 |
| 0.0267 | 470 | 0.0244 |
| 0.0273 | 480 | 0.0234 |
| 0.0278 | 490 | 0.0234 |
| 0.0284 | 500 | 0.0237 |
| 0.0290 | 510 | 0.0235 |
| 0.0295 | 520 | 0.0236 |
| 0.0301 | 530 | 0.0246 |
| 0.0307 | 540 | 0.0242 |
| 0.0312 | 550 | 0.023 |
| 0.0318 | 560 | 0.0231 |
| 0.0324 | 570 | 0.0232 |
| 0.0329 | 580 | 0.0233 |
| 0.0335 | 590 | 0.023 |
| 0.0341 | 600 | 0.0235 |
| 0.0346 | 610 | 0.0241 |
| 0.0352 | 620 | 0.0225 |
| 0.0358 | 630 | 0.0224 |
| 0.0363 | 640 | 0.0232 |
| 0.0369 | 650 | 0.0229 |
| 0.0375 | 660 | 0.023 |
| 0.0380 | 670 | 0.0232 |
| 0.0386 | 680 | 0.0217 |
| 0.0392 | 690 | 0.0224 |
| 0.0397 | 700 | 0.0225 |
| 0.0403 | 710 | 0.023 |
| 0.0409 | 720 | 0.0234 |
| 0.0414 | 730 | 0.023 |
| 0.0420 | 740 | 0.0215 |
| 0.0426 | 750 | 0.0216 |
| 0.0431 | 760 | 0.0219 |
| 0.0437 | 770 | 0.0208 |
| 0.0443 | 780 | 0.0222 |
| 0.0449 | 790 | 0.0233 |
| 0.0454 | 800 | 0.0224 |
| 0.0460 | 810 | 0.0217 |
| 0.0466 | 820 | 0.0225 |
| 0.0471 | 830 | 0.0219 |
| 0.0477 | 840 | 0.0219 |
| 0.0483 | 850 | 0.0209 |
| 0.0488 | 860 | 0.0218 |
| 0.0494 | 870 | 0.022 |
| 0.0500 | 880 | 0.0215 |
| 0.0505 | 890 | 0.0229 |
| 0.0511 | 900 | 0.0212 |
| 0.0517 | 910 | 0.0221 |
| 0.0522 | 920 | 0.023 |
| 0.0528 | 930 | 0.022 |
| 0.0534 | 940 | 0.0217 |
| 0.0539 | 950 | 0.022 |
| 0.0545 | 960 | 0.0213 |
| 0.0551 | 970 | 0.023 |
| 0.0556 | 980 | 0.0215 |
| 0.0562 | 990 | 0.0208 |
| 0.0568 | 1000 | 0.0225 |
| 0.0573 | 1010 | 0.0217 |
| 0.0579 | 1020 | 0.0213 |
| 0.0585 | 1030 | 0.0212 |
| 0.0590 | 1040 | 0.0214 |
| 0.0596 | 1050 | 0.0224 |
| 0.0602 | 1060 | 0.021 |
| 0.0608 | 1070 | 0.0205 |
| 0.0613 | 1080 | 0.0227 |
| 0.0619 | 1090 | 0.0224 |
| 0.0625 | 1100 | 0.0213 |
| 0.0630 | 1110 | 0.0219 |
| 0.0636 | 1120 | 0.0213 |
| 0.0642 | 1130 | 0.0216 |
| 0.0647 | 1140 | 0.0205 |
| 0.0653 | 1150 | 0.021 |
| 0.0659 | 1160 | 0.0211 |
| 0.0664 | 1170 | 0.0212 |
| 0.0670 | 1180 | 0.0207 |
| 0.0676 | 1190 | 0.0213 |
| 0.0681 | 1200 | 0.0214 |
| 0.0687 | 1210 | 0.0216 |
| 0.0693 | 1220 | 0.0221 |
| 0.0698 | 1230 | 0.0223 |
| 0.0704 | 1240 | 0.0197 |
| 0.0710 | 1250 | 0.0215 |
| 0.0715 | 1260 | 0.022 |
| 0.0721 | 1270 | 0.0218 |
| 0.0727 | 1280 | 0.0208 |
| 0.0732 | 1290 | 0.0215 |
| 0.0738 | 1300 | 0.0216 |
| 0.0744 | 1310 | 0.0204 |
| 0.0749 | 1320 | 0.0217 |
| 0.0755 | 1330 | 0.0222 |
| 0.0761 | 1340 | 0.0207 |
| 0.0766 | 1350 | 0.022 |
| 0.0772 | 1360 | 0.0211 |
| 0.0778 | 1370 | 0.0203 |
| 0.0784 | 1380 | 0.0206 |
| 0.0789 | 1390 | 0.0209 |
| 0.0795 | 1400 | 0.0207 |
| 0.0801 | 1410 | 0.0219 |
| 0.0806 | 1420 | 0.0215 |
| 0.0812 | 1430 | 0.0215 |
| 0.0818 | 1440 | 0.0206 |
| 0.0823 | 1450 | 0.0212 |
| 0.0829 | 1460 | 0.0202 |
| 0.0835 | 1470 | 0.0212 |
| 0.0840 | 1480 | 0.0214 |
| 0.0846 | 1490 | 0.0213 |
| 0.0852 | 1500 | 0.0213 |
| 0.0857 | 1510 | 0.0186 |
| 0.0863 | 1520 | 0.0206 |
| 0.0869 | 1530 | 0.0214 |
| 0.0874 | 1540 | 0.0205 |
| 0.0880 | 1550 | 0.0211 |
| 0.0886 | 1560 | 0.0213 |
| 0.0891 | 1570 | 0.021 |
| 0.0897 | 1580 | 0.0207 |
| 0.0903 | 1590 | 0.0217 |
| 0.0908 | 1600 | 0.0206 |
| 0.0914 | 1610 | 0.0202 |
| 0.0920 | 1620 | 0.0219 |
| 0.0925 | 1630 | 0.0205 |
| 0.0931 | 1640 | 0.021 |
| 0.0937 | 1650 | 0.0197 |
| 0.0942 | 1660 | 0.0206 |
| 0.0948 | 1670 | 0.0209 |
| 0.0954 | 1680 | 0.021 |
| 0.0960 | 1690 | 0.0217 |
| 0.0965 | 1700 | 0.0224 |
| 0.0971 | 1710 | 0.0208 |
| 0.0977 | 1720 | 0.0212 |
| 0.0982 | 1730 | 0.0212 |
| 0.0988 | 1740 | 0.0216 |
| 0.0994 | 1750 | 0.0205 |
| 0.0999 | 1760 | 0.0204 |
| 0.1005 | 1770 | 0.0202 |
| 0.1011 | 1780 | 0.0203 |
| 0.1016 | 1790 | 0.0207 |
| 0.1022 | 1800 | 0.0209 |
| 0.1028 | 1810 | 0.02 |
| 0.1033 | 1820 | 0.0204 |
| 0.1039 | 1830 | 0.0204 |
| 0.1045 | 1840 | 0.02 |
| 0.1050 | 1850 | 0.0218 |
| 0.1056 | 1860 | 0.0212 |
| 0.1062 | 1870 | 0.021 |
| 0.1067 | 1880 | 0.021 |
| 0.1073 | 1890 | 0.0214 |
| 0.1079 | 1900 | 0.0199 |
| 0.1084 | 1910 | 0.0207 |
| 0.1090 | 1920 | 0.0205 |
| 0.1096 | 1930 | 0.0204 |
| 0.1101 | 1940 | 0.0217 |
| 0.1107 | 1950 | 0.0217 |
| 0.1113 | 1960 | 0.0212 |
| 0.1118 | 1970 | 0.0204 |
| 0.1124 | 1980 | 0.0214 |
| 0.1130 | 1990 | 0.0206 |
| 0.1136 | 2000 | 0.0205 |
| 0.1141 | 2010 | 0.0206 |
| 0.1147 | 2020 | 0.0208 |
| 0.1153 | 2030 | 0.0209 |
| 0.1158 | 2040 | 0.0206 |
| 0.1164 | 2050 | 0.0212 |
| 0.1170 | 2060 | 0.0209 |
| 0.1175 | 2070 | 0.0206 |
| 0.1181 | 2080 | 0.02 |
| 0.1187 | 2090 | 0.0209 |
| 0.1192 | 2100 | 0.0211 |
| 0.1198 | 2110 | 0.0207 |
| 0.1204 | 2120 | 0.0211 |
| 0.1209 | 2130 | 0.0206 |
| 0.1215 | 2140 | 0.0214 |
| 0.1221 | 2150 | 0.021 |
| 0.1226 | 2160 | 0.0194 |
| 0.1232 | 2170 | 0.0206 |
| 0.1238 | 2180 | 0.0204 |
| 0.1243 | 2190 | 0.0202 |
| 0.1249 | 2200 | 0.0208 |
| 0.1255 | 2210 | 0.0207 |
| 0.1260 | 2220 | 0.0207 |
| 0.1266 | 2230 | 0.0204 |
| 0.1272 | 2240 | 0.0205 |
| 0.1277 | 2250 | 0.0207 |
| 0.1283 | 2260 | 0.0212 |
| 0.1289 | 2270 | 0.0196 |
| 0.1294 | 2280 | 0.021 |
| 0.1300 | 2290 | 0.0207 |
| 0.1306 | 2300 | 0.0222 |
| 0.1312 | 2310 | 0.0203 |
| 0.1317 | 2320 | 0.0209 |
| 0.1323 | 2330 | 0.0209 |
| 0.1329 | 2340 | 0.0205 |
| 0.1334 | 2350 | 0.0199 |
| 0.1340 | 2360 | 0.0207 |
| 0.1346 | 2370 | 0.021 |
| 0.1351 | 2380 | 0.0202 |
| 0.1357 | 2390 | 0.0213 |
| 0.1363 | 2400 | 0.0208 |
| 0.1368 | 2410 | 0.0201 |
| 0.1374 | 2420 | 0.0208 |
| 0.1380 | 2430 | 0.0201 |
| 0.1385 | 2440 | 0.0201 |
| 0.1391 | 2450 | 0.0202 |
| 0.1397 | 2460 | 0.0203 |
| 0.1402 | 2470 | 0.0203 |
| 0.1408 | 2480 | 0.0204 |
| 0.1414 | 2490 | 0.02 |
| 0.1419 | 2500 | 0.0214 |
| 0.1425 | 2510 | 0.0202 |
| 0.1431 | 2520 | 0.0203 |
| 0.1436 | 2530 | 0.0212 |
| 0.1442 | 2540 | 0.0219 |
| 0.1448 | 2550 | 0.0207 |
| 0.1453 | 2560 | 0.02 |
| 0.1459 | 2570 | 0.0208 |
| 0.1465 | 2580 | 0.0213 |
| 0.1471 | 2590 | 0.0199 |
| 0.1476 | 2600 | 0.0208 |
| 0.1482 | 2610 | 0.0207 |
| 0.1488 | 2620 | 0.0198 |
| 0.1493 | 2630 | 0.0205 |
| 0.1499 | 2640 | 0.0212 |
| 0.1505 | 2650 | 0.0215 |
| 0.1510 | 2660 | 0.0209 |
| 0.1516 | 2670 | 0.0207 |
| 0.1522 | 2680 | 0.021 |
| 0.1527 | 2690 | 0.0212 |
| 0.1533 | 2700 | 0.0208 |
| 0.1539 | 2710 | 0.0205 |
| 0.1544 | 2720 | 0.0193 |
| 0.1550 | 2730 | 0.0209 |
| 0.1556 | 2740 | 0.0208 |
| 0.1561 | 2750 | 0.02 |
| 0.1567 | 2760 | 0.0203 |
| 0.1573 | 2770 | 0.0197 |
| 0.1578 | 2780 | 0.0205 |
| 0.1584 | 2790 | 0.0198 |
| 0.1590 | 2800 | 0.0199 |
| 0.1595 | 2810 | 0.0193 |
| 0.1601 | 2820 | 0.0209 |
| 0.1607 | 2830 | 0.0206 |
| 0.1612 | 2840 | 0.0206 |
| 0.1618 | 2850 | 0.0207 |
| 0.1624 | 2860 | 0.0202 |
| 0.1629 | 2870 | 0.0203 |
| 0.1635 | 2880 | 0.0204 |
| 0.1641 | 2890 | 0.0197 |
| 0.1647 | 2900 | 0.02 |
| 0.1652 | 2910 | 0.0204 |
| 0.1658 | 2920 | 0.0206 |
| 0.1664 | 2930 | 0.0204 |
| 0.1669 | 2940 | 0.0202 |
| 0.1675 | 2950 | 0.0205 |
| 0.1681 | 2960 | 0.0199 |
| 0.1686 | 2970 | 0.0197 |
| 0.1692 | 2980 | 0.0207 |
| 0.1698 | 2990 | 0.0201 |
| 0.1703 | 3000 | 0.0199 |
| 0.1709 | 3010 | 0.0205 |
| 0.1715 | 3020 | 0.0198 |
| 0.1720 | 3030 | 0.0206 |
| 0.1726 | 3040 | 0.0189 |
| 0.1732 | 3050 | 0.0209 |
| 0.1737 | 3060 | 0.02 |
| 0.1743 | 3070 | 0.0191 |
| 0.1749 | 3080 | 0.0206 |
| 0.1754 | 3090 | 0.0198 |
| 0.1760 | 3100 | 0.0219 |
| 0.1766 | 3110 | 0.02 |
| 0.1771 | 3120 | 0.0194 |
| 0.1777 | 3130 | 0.0221 |
| 0.1783 | 3140 | 0.0196 |
| 0.1788 | 3150 | 0.0197 |
| 0.1794 | 3160 | 0.0215 |
| 0.1800 | 3170 | 0.0195 |
| 0.1805 | 3180 | 0.0198 |
| 0.1811 | 3190 | 0.021 |
| 0.1817 | 3200 | 0.0204 |
| 0.1823 | 3210 | 0.0199 |
| 0.1828 | 3220 | 0.0207 |
| 0.1834 | 3230 | 0.0194 |
| 0.1840 | 3240 | 0.0212 |
| 0.1845 | 3250 | 0.0199 |
| 0.1851 | 3260 | 0.0198 |
| 0.1857 | 3270 | 0.0201 |
| 0.1862 | 3280 | 0.0204 |
| 0.1868 | 3290 | 0.0206 |
| 0.1874 | 3300 | 0.0206 |
| 0.1879 | 3310 | 0.0195 |
| 0.1885 | 3320 | 0.0205 |
| 0.1891 | 3330 | 0.0208 |
| 0.1896 | 3340 | 0.0205 |
| 0.1902 | 3350 | 0.0208 |
| 0.1908 | 3360 | 0.0194 |
| 0.1913 | 3370 | 0.0205 |
| 0.1919 | 3380 | 0.0201 |
| 0.1925 | 3390 | 0.0212 |
| 0.1930 | 3400 | 0.019 |
| 0.1936 | 3410 | 0.0198 |
| 0.1942 | 3420 | 0.0203 |
| 0.1947 | 3430 | 0.0205 |
| 0.1953 | 3440 | 0.0207 |
| 0.1959 | 3450 | 0.0207 |
| 0.1964 | 3460 | 0.0203 |
| 0.1970 | 3470 | 0.0194 |
| 0.1976 | 3480 | 0.0198 |
| 0.1981 | 3490 | 0.0195 |
| 0.1987 | 3500 | 0.0204 |
| 0.1993 | 3510 | 0.0209 |
| 0.1999 | 3520 | 0.021 |
| 0.2004 | 3530 | 0.0194 |
| 0.2010 | 3540 | 0.0199 |
| 0.2016 | 3550 | 0.0215 |
| 0.2021 | 3560 | 0.0195 |
| 0.2027 | 3570 | 0.0202 |
| 0.2033 | 3580 | 0.0203 |
| 0.2038 | 3590 | 0.0194 |
| 0.2044 | 3600 | 0.0198 |
| 0.2050 | 3610 | 0.02 |
| 0.2055 | 3620 | 0.0205 |
| 0.2061 | 3630 | 0.0195 |
| 0.2067 | 3640 | 0.0195 |
| 0.2072 | 3650 | 0.0194 |
| 0.2078 | 3660 | 0.02 |
| 0.2084 | 3670 | 0.0198 |
| 0.2089 | 3680 | 0.0197 |
| 0.2095 | 3690 | 0.0202 |
| 0.2101 | 3700 | 0.0198 |
| 0.2106 | 3710 | 0.0208 |
| 0.2112 | 3720 | 0.0207 |
| 0.2118 | 3730 | 0.0197 |
| 0.2123 | 3740 | 0.0194 |
| 0.2129 | 3750 | 0.0188 |
| 0.2135 | 3760 | 0.0201 |
| 0.2140 | 3770 | 0.02 |
| 0.2146 | 3780 | 0.0199 |
| 0.2152 | 3790 | 0.0202 |
| 0.2157 | 3800 | 0.0198 |
| 0.2163 | 3810 | 0.0195 |
| 0.2169 | 3820 | 0.0202 |
| 0.2175 | 3830 | 0.0197 |
| 0.2180 | 3840 | 0.0209 |
| 0.2186 | 3850 | 0.0203 |
| 0.2192 | 3860 | 0.02 |
| 0.2197 | 3870 | 0.0197 |
| 0.2203 | 3880 | 0.0204 |
| 0.2209 | 3890 | 0.0198 |
| 0.2214 | 3900 | 0.0198 |
| 0.2220 | 3910 | 0.0207 |
| 0.2226 | 3920 | 0.0193 |
| 0.2231 | 3930 | 0.0194 |
| 0.2237 | 3940 | 0.0202 |
| 0.2243 | 3950 | 0.0205 |
| 0.2248 | 3960 | 0.0202 |
| 0.2254 | 3970 | 0.0192 |
| 0.2260 | 3980 | 0.02 |
| 0.2265 | 3990 | 0.0185 |
| 0.2271 | 4000 | 0.0206 |
| 0.2277 | 4010 | 0.0193 |
| 0.2282 | 4020 | 0.0209 |
| 0.2288 | 4030 | 0.0205 |
| 0.2294 | 4040 | 0.0197 |
| 0.2299 | 4050 | 0.0201 |
| 0.2305 | 4060 | 0.0197 |
| 0.2311 | 4070 | 0.0202 |
| 0.2316 | 4080 | 0.0203 |
| 0.2322 | 4090 | 0.0215 |
| 0.2328 | 4100 | 0.0206 |
| 0.2334 | 4110 | 0.0197 |
| 0.2339 | 4120 | 0.0209 |
| 0.2345 | 4130 | 0.0205 |
| 0.2351 | 4140 | 0.0214 |
| 0.2356 | 4150 | 0.02 |
| 0.2362 | 4160 | 0.0208 |
| 0.2368 | 4170 | 0.02 |
| 0.2373 | 4180 | 0.0197 |
| 0.2379 | 4190 | 0.0203 |
| 0.2385 | 4200 | 0.0207 |
| 0.2390 | 4210 | 0.0199 |
| 0.2396 | 4220 | 0.0199 |
| 0.2402 | 4230 | 0.0187 |
| 0.2407 | 4240 | 0.0201 |
| 0.2413 | 4250 | 0.0213 |
| 0.2419 | 4260 | 0.0204 |
| 0.2424 | 4270 | 0.0205 |
| 0.2430 | 4280 | 0.02 |
| 0.2436 | 4290 | 0.0206 |
| 0.2441 | 4300 | 0.0199 |
| 0.2447 | 4310 | 0.0201 |
| 0.2453 | 4320 | 0.021 |
| 0.2458 | 4330 | 0.0206 |
| 0.2464 | 4340 | 0.0206 |
| 0.2470 | 4350 | 0.019 |
| 0.2475 | 4360 | 0.0206 |
| 0.2481 | 4370 | 0.0199 |
| 0.2487 | 4380 | 0.0206 |
| 0.2492 | 4390 | 0.0202 |
| 0.2498 | 4400 | 0.0199 |
| 0.2504 | 4410 | 0.0196 |
| 0.2510 | 4420 | 0.0196 |
| 0.2515 | 4430 | 0.0206 |
| 0.2521 | 4440 | 0.0202 |
| 0.2527 | 4450 | 0.0195 |
| 0.2532 | 4460 | 0.0196 |
| 0.2538 | 4470 | 0.0205 |
| 0.2544 | 4480 | 0.0203 |
| 0.2549 | 4490 | 0.0202 |
| 0.2555 | 4500 | 0.0198 |
| 0.2561 | 4510 | 0.0201 |
| 0.2566 | 4520 | 0.0195 |
| 0.2572 | 4530 | 0.0197 |
| 0.2578 | 4540 | 0.0189 |
| 0.2583 | 4550 | 0.021 |
| 0.2589 | 4560 | 0.0197 |
| 0.2595 | 4570 | 0.021 |
| 0.2600 | 4580 | 0.0201 |
| 0.2606 | 4590 | 0.0205 |
| 0.2612 | 4600 | 0.0201 |
| 0.2617 | 4610 | 0.0186 |
| 0.2623 | 4620 | 0.0203 |
| 0.2629 | 4630 | 0.0193 |
| 0.2634 | 4640 | 0.0197 |
| 0.2640 | 4650 | 0.0209 |
| 0.2646 | 4660 | 0.0201 |
| 0.2651 | 4670 | 0.0203 |
| 0.2657 | 4680 | 0.0202 |
| 0.2663 | 4690 | 0.0193 |
| 0.2668 | 4700 | 0.02 |
| 0.2674 | 4710 | 0.0198 |
| 0.2680 | 4720 | 0.0191 |
| 0.2686 | 4730 | 0.0194 |
| 0.2691 | 4740 | 0.0195 |
| 0.2697 | 4750 | 0.0191 |
| 0.2703 | 4760 | 0.0204 |
| 0.2708 | 4770 | 0.0195 |
| 0.2714 | 4780 | 0.0197 |
| 0.2720 | 4790 | 0.0206 |
| 0.2725 | 4800 | 0.0199 |
| 0.2731 | 4810 | 0.02 |
| 0.2737 | 4820 | 0.02 |
| 0.2742 | 4830 | 0.0196 |
| 0.2748 | 4840 | 0.0202 |
| 0.2754 | 4850 | 0.0195 |
| 0.2759 | 4860 | 0.0204 |
| 0.2765 | 4870 | 0.0194 |
| 0.2771 | 4880 | 0.0194 |
| 0.2776 | 4890 | 0.0205 |
| 0.2782 | 4900 | 0.0191 |
| 0.2788 | 4910 | 0.02 |
| 0.2793 | 4920 | 0.0196 |
| 0.2799 | 4930 | 0.0181 |
| 0.2805 | 4940 | 0.0198 |
| 0.2810 | 4950 | 0.0201 |
| 0.2816 | 4960 | 0.0199 |
| 0.2822 | 4970 | 0.0202 |
| 0.2827 | 4980 | 0.0199 |
| 0.2833 | 4990 | 0.0187 |
| 0.2839 | 5000 | 0.0206 |
| 0.2844 | 5010 | 0.0198 |
| 0.2850 | 5020 | 0.0209 |
| 0.2856 | 5030 | 0.0196 |
| 0.2862 | 5040 | 0.0201 |
| 0.2867 | 5050 | 0.0201 |
| 0.2873 | 5060 | 0.0193 |
| 0.2879 | 5070 | 0.0206 |
| 0.2884 | 5080 | 0.0201 |
| 0.2890 | 5090 | 0.0191 |
| 0.2896 | 5100 | 0.0207 |
| 0.2901 | 5110 | 0.0198 |
| 0.2907 | 5120 | 0.0201 |
| 0.2913 | 5130 | 0.0198 |
| 0.2918 | 5140 | 0.0195 |
| 0.2924 | 5150 | 0.0198 |
| 0.2930 | 5160 | 0.0202 |
| 0.2935 | 5170 | 0.0189 |
| 0.2941 | 5180 | 0.0194 |
| 0.2947 | 5190 | 0.0203 |
| 0.2952 | 5200 | 0.0194 |
| 0.2958 | 5210 | 0.0195 |
| 0.2964 | 5220 | 0.0197 |
| 0.2969 | 5230 | 0.0197 |
| 0.2975 | 5240 | 0.0188 |
| 0.2981 | 5250 | 0.02 |
| 0.2986 | 5260 | 0.02 |
| 0.2992 | 5270 | 0.0199 |
| 0.2998 | 5280 | 0.0205 |
| 0.3003 | 5290 | 0.0204 |
| 0.3009 | 5300 | 0.0192 |
| 0.3015 | 5310 | 0.0206 |
| 0.3020 | 5320 | 0.0212 |
| 0.3026 | 5330 | 0.0197 |
| 0.3032 | 5340 | 0.0192 |
| 0.3038 | 5350 | 0.0197 |
| 0.3043 | 5360 | 0.0189 |
| 0.3049 | 5370 | 0.0195 |
| 0.3055 | 5380 | 0.0202 |
| 0.3060 | 5390 | 0.0199 |
| 0.3066 | 5400 | 0.0202 |
| 0.3072 | 5410 | 0.0217 |
| 0.3077 | 5420 | 0.0208 |
| 0.3083 | 5430 | 0.0207 |
| 0.3089 | 5440 | 0.0207 |
| 0.3094 | 5450 | 0.0201 |
| 0.3100 | 5460 | 0.0197 |
| 0.3106 | 5470 | 0.0192 |
| 0.3111 | 5480 | 0.0192 |
| 0.3117 | 5490 | 0.0198 |
| 0.3123 | 5500 | 0.0202 |
| 0.3128 | 5510 | 0.0182 |
| 0.3134 | 5520 | 0.0198 |
| 0.3140 | 5530 | 0.0195 |
| 0.3145 | 5540 | 0.0192 |
| 0.3151 | 5550 | 0.0193 |
| 0.3157 | 5560 | 0.0189 |
| 0.3162 | 5570 | 0.0198 |
| 0.3168 | 5580 | 0.02 |
| 0.3174 | 5590 | 0.0196 |
| 0.3179 | 5600 | 0.0196 |
| 0.3185 | 5610 | 0.0197 |
| 0.3191 | 5620 | 0.0193 |
| 0.3197 | 5630 | 0.021 |
| 0.3202 | 5640 | 0.0193 |
| 0.3208 | 5650 | 0.0204 |
| 0.3214 | 5660 | 0.0187 |
| 0.3219 | 5670 | 0.0191 |
| 0.3225 | 5680 | 0.02 |
| 0.3231 | 5690 | 0.0197 |
| 0.3236 | 5700 | 0.0198 |
| 0.3242 | 5710 | 0.0198 |
| 0.3248 | 5720 | 0.0192 |
| 0.3253 | 5730 | 0.019 |
| 0.3259 | 5740 | 0.0195 |
| 0.3265 | 5750 | 0.0192 |
| 0.3270 | 5760 | 0.0193 |
| 0.3276 | 5770 | 0.0197 |
| 0.3282 | 5780 | 0.0204 |
| 0.3287 | 5790 | 0.0199 |
| 0.3293 | 5800 | 0.0192 |
| 0.3299 | 5810 | 0.0202 |
| 0.3304 | 5820 | 0.019 |
| 0.3310 | 5830 | 0.0195 |
| 0.3316 | 5840 | 0.02 |
| 0.3321 | 5850 | 0.0197 |
| 0.3327 | 5860 | 0.0188 |
| 0.3333 | 5870 | 0.0204 |
| 0.3338 | 5880 | 0.0196 |
| 0.3344 | 5890 | 0.0203 |
| 0.3350 | 5900 | 0.0189 |
| 0.3355 | 5910 | 0.0197 |
| 0.3361 | 5920 | 0.0196 |
| 0.3367 | 5930 | 0.0194 |
| 0.3373 | 5940 | 0.0191 |
| 0.3378 | 5950 | 0.0196 |
| 0.3384 | 5960 | 0.0181 |
| 0.3390 | 5970 | 0.0205 |
| 0.3395 | 5980 | 0.0194 |
| 0.3401 | 5990 | 0.02 |
| 0.3407 | 6000 | 0.0187 |
| 0.3412 | 6010 | 0.0196 |
| 0.3418 | 6020 | 0.0206 |
| 0.3424 | 6030 | 0.019 |
| 0.3429 | 6040 | 0.0189 |
| 0.3435 | 6050 | 0.0196 |
| 0.3441 | 6060 | 0.0194 |
| 0.3446 | 6070 | 0.0205 |
| 0.3452 | 6080 | 0.0193 |
| 0.3458 | 6090 | 0.019 |
| 0.3463 | 6100 | 0.0191 |
| 0.3469 | 6110 | 0.0199 |
| 0.3475 | 6120 | 0.019 |
| 0.3480 | 6130 | 0.0206 |
| 0.3486 | 6140 | 0.0191 |
| 0.3492 | 6150 | 0.0195 |
| 0.3497 | 6160 | 0.0193 |
| 0.3503 | 6170 | 0.0191 |
| 0.3509 | 6180 | 0.0192 |
| 0.3514 | 6190 | 0.0196 |
| 0.3520 | 6200 | 0.0193 |
| 0.3526 | 6210 | 0.0191 |
| 0.3531 | 6220 | 0.0201 |
| 0.3537 | 6230 | 0.0194 |
| 0.3543 | 6240 | 0.0182 |
| 0.3549 | 6250 | 0.019 |
| 0.3554 | 6260 | 0.0201 |
| 0.3560 | 6270 | 0.0188 |
| 0.3566 | 6280 | 0.0196 |
| 0.3571 | 6290 | 0.0195 |
| 0.3577 | 6300 | 0.0195 |
| 0.3583 | 6310 | 0.019 |
| 0.3588 | 6320 | 0.0184 |
| 0.3594 | 6330 | 0.0197 |
| 0.3600 | 6340 | 0.0194 |
| 0.3605 | 6350 | 0.0196 |
| 0.3611 | 6360 | 0.0201 |
| 0.3617 | 6370 | 0.0197 |
| 0.3622 | 6380 | 0.0193 |
| 0.3628 | 6390 | 0.0189 |
| 0.3634 | 6400 | 0.0201 |
| 0.3639 | 6410 | 0.0197 |
| 0.3645 | 6420 | 0.0204 |
| 0.3651 | 6430 | 0.0202 |
| 0.3656 | 6440 | 0.0195 |
| 0.3662 | 6450 | 0.0194 |
| 0.3668 | 6460 | 0.0199 |
| 0.3673 | 6470 | 0.019 |
| 0.3679 | 6480 | 0.0193 |
| 0.3685 | 6490 | 0.0196 |
| 0.3690 | 6500 | 0.0194 |
| 0.3696 | 6510 | 0.0189 |
| 0.3702 | 6520 | 0.0183 |
| 0.3707 | 6530 | 0.0197 |
| 0.3713 | 6540 | 0.0194 |
| 0.3719 | 6550 | 0.0197 |
| 0.3725 | 6560 | 0.0199 |
| 0.3730 | 6570 | 0.0187 |
| 0.3736 | 6580 | 0.0201 |
| 0.3742 | 6590 | 0.0197 |
| 0.3747 | 6600 | 0.0192 |
| 0.3753 | 6610 | 0.02 |
| 0.3759 | 6620 | 0.0192 |
| 0.3764 | 6630 | 0.0187 |
| 0.3770 | 6640 | 0.0202 |
| 0.3776 | 6650 | 0.0201 |
| 0.3781 | 6660 | 0.0194 |
| 0.3787 | 6670 | 0.0192 |
| 0.3793 | 6680 | 0.0191 |
| 0.3798 | 6690 | 0.0198 |
| 0.3804 | 6700 | 0.0196 |
| 0.3810 | 6710 | 0.0183 |
| 0.3815 | 6720 | 0.0196 |
| 0.3821 | 6730 | 0.0194 |
| 0.3827 | 6740 | 0.0193 |
| 0.3832 | 6750 | 0.0199 |
| 0.3838 | 6760 | 0.0198 |
| 0.3844 | 6770 | 0.0191 |
| 0.3849 | 6780 | 0.0197 |
| 0.3855 | 6790 | 0.0194 |
| 0.3861 | 6800 | 0.019 |
| 0.3866 | 6810 | 0.0198 |
| 0.3872 | 6820 | 0.0192 |
| 0.3878 | 6830 | 0.0198 |
| 0.3883 | 6840 | 0.0194 |
| 0.3889 | 6850 | 0.0207 |
| 0.3895 | 6860 | 0.02 |
| 0.3901 | 6870 | 0.0199 |
| 0.3906 | 6880 | 0.019 |
| 0.3912 | 6890 | 0.0194 |
| 0.3918 | 6900 | 0.0181 |
| 0.3923 | 6910 | 0.0186 |
| 0.3929 | 6920 | 0.0188 |
| 0.3935 | 6930 | 0.0206 |
| 0.3940 | 6940 | 0.0193 |
| 0.3946 | 6950 | 0.0195 |
| 0.3952 | 6960 | 0.0188 |
| 0.3957 | 6970 | 0.0195 |
| 0.3963 | 6980 | 0.0199 |
| 0.3969 | 6990 | 0.0197 |
| 0.3974 | 7000 | 0.0197 |
| 0.3980 | 7010 | 0.0194 |
| 0.3986 | 7020 | 0.0194 |
| 0.3991 | 7030 | 0.0188 |
| 0.3997 | 7040 | 0.0194 |
| 0.4003 | 7050 | 0.0193 |
| 0.4008 | 7060 | 0.0198 |
| 0.4014 | 7070 | 0.0192 |
| 0.4020 | 7080 | 0.0207 |
| 0.4025 | 7090 | 0.0203 |
| 0.4031 | 7100 | 0.0189 |
| 0.4037 | 7110 | 0.02 |
| 0.4042 | 7120 | 0.0196 |
| 0.4048 | 7130 | 0.0203 |
| 0.4054 | 7140 | 0.019 |
| 0.4060 | 7150 | 0.0191 |
| 0.4065 | 7160 | 0.0196 |
| 0.4071 | 7170 | 0.0196 |
| 0.4077 | 7180 | 0.0197 |
| 0.4082 | 7190 | 0.0187 |
| 0.4088 | 7200 | 0.0193 |
| 0.4094 | 7210 | 0.0191 |
| 0.4099 | 7220 | 0.0201 |
| 0.4105 | 7230 | 0.0185 |
| 0.4111 | 7240 | 0.0192 |
| 0.4116 | 7250 | 0.0194 |
| 0.4122 | 7260 | 0.0187 |
| 0.4128 | 7270 | 0.0187 |
| 0.4133 | 7280 | 0.0193 |
| 0.4139 | 7290 | 0.0193 |
| 0.4145 | 7300 | 0.0188 |
| 0.4150 | 7310 | 0.0193 |
| 0.4156 | 7320 | 0.0194 |
| 0.4162 | 7330 | 0.0189 |
| 0.4167 | 7340 | 0.0195 |
| 0.4173 | 7350 | 0.0179 |
| 0.4179 | 7360 | 0.0192 |
| 0.4184 | 7370 | 0.0201 |
| 0.4190 | 7380 | 0.0197 |
| 0.4196 | 7390 | 0.0193 |
| 0.4201 | 7400 | 0.0191 |
| 0.4207 | 7410 | 0.0199 |
| 0.4213 | 7420 | 0.02 |
| 0.4218 | 7430 | 0.0195 |
| 0.4224 | 7440 | 0.0185 |
| 0.4230 | 7450 | 0.0185 |
| 0.4236 | 7460 | 0.02 |
| 0.4241 | 7470 | 0.0191 |
| 0.4247 | 7480 | 0.0196 |
| 0.4253 | 7490 | 0.0194 |
| 0.4258 | 7500 | 0.0187 |
| 0.4264 | 7510 | 0.0195 |
| 0.4270 | 7520 | 0.0203 |
| 0.4275 | 7530 | 0.0187 |
| 0.4281 | 7540 | 0.02 |
| 0.4287 | 7550 | 0.0189 |
| 0.4292 | 7560 | 0.0193 |
| 0.4298 | 7570 | 0.019 |
| 0.4304 | 7580 | 0.0189 |
| 0.4309 | 7590 | 0.0202 |
| 0.4315 | 7600 | 0.0192 |
| 0.4321 | 7610 | 0.0202 |
| 0.4326 | 7620 | 0.0191 |
| 0.4332 | 7630 | 0.0186 |
| 0.4338 | 7640 | 0.0195 |
| 0.4343 | 7650 | 0.0198 |
| 0.4349 | 7660 | 0.0185 |
| 0.4355 | 7670 | 0.0192 |
| 0.4360 | 7680 | 0.0184 |
| 0.4366 | 7690 | 0.0196 |
| 0.4372 | 7700 | 0.0195 |
| 0.4377 | 7710 | 0.0191 |
| 0.4383 | 7720 | 0.0192 |
| 0.4389 | 7730 | 0.0196 |
| 0.4394 | 7740 | 0.019 |
| 0.4400 | 7750 | 0.0197 |
| 0.4406 | 7760 | 0.0194 |
| 0.4412 | 7770 | 0.019 |
| 0.4417 | 7780 | 0.0197 |
| 0.4423 | 7790 | 0.0186 |
| 0.4429 | 7800 | 0.0189 |
| 0.4434 | 7810 | 0.0184 |
| 0.4440 | 7820 | 0.019 |
| 0.4446 | 7830 | 0.0182 |
| 0.4451 | 7840 | 0.0189 |
| 0.4457 | 7850 | 0.0178 |
| 0.4463 | 7860 | 0.0191 |
| 0.4468 | 7870 | 0.0188 |
| 0.4474 | 7880 | 0.0196 |
| 0.4480 | 7890 | 0.0188 |
| 0.4485 | 7900 | 0.0191 |
| 0.4491 | 7910 | 0.0183 |
| 0.4497 | 7920 | 0.0189 |
| 0.4502 | 7930 | 0.0192 |
| 0.4508 | 7940 | 0.0201 |
| 0.4514 | 7950 | 0.0192 |
| 0.4519 | 7960 | 0.0187 |
| 0.4525 | 7970 | 0.019 |
| 0.4531 | 7980 | 0.019 |
| 0.4536 | 7990 | 0.0187 |
| 0.4542 | 8000 | 0.0197 |
| 0.4548 | 8010 | 0.0189 |
| 0.4553 | 8020 | 0.0198 |
| 0.4559 | 8030 | 0.019 |
| 0.4565 | 8040 | 0.0193 |
| 0.4570 | 8050 | 0.0187 |
| 0.4576 | 8060 | 0.0195 |
| 0.4582 | 8070 | 0.0184 |
| 0.4588 | 8080 | 0.0199 |
| 0.4593 | 8090 | 0.02 |
| 0.4599 | 8100 | 0.0194 |
| 0.4605 | 8110 | 0.0198 |
| 0.4610 | 8120 | 0.0199 |
| 0.4616 | 8130 | 0.0191 |
| 0.4622 | 8140 | 0.019 |
| 0.4627 | 8150 | 0.0204 |
| 0.4633 | 8160 | 0.0189 |
| 0.4639 | 8170 | 0.0199 |
| 0.4644 | 8180 | 0.0187 |
| 0.4650 | 8190 | 0.0192 |
| 0.4656 | 8200 | 0.0192 |
| 0.4661 | 8210 | 0.0179 |
| 0.4667 | 8220 | 0.0198 |
| 0.4673 | 8230 | 0.0194 |
| 0.4678 | 8240 | 0.0191 |
| 0.4684 | 8250 | 0.0188 |
| 0.4690 | 8260 | 0.0193 |
| 0.4695 | 8270 | 0.0193 |
| 0.4701 | 8280 | 0.019 |
| 0.4707 | 8290 | 0.0197 |
| 0.4712 | 8300 | 0.018 |
| 0.4718 | 8310 | 0.0184 |
| 0.4724 | 8320 | 0.019 |
| 0.4729 | 8330 | 0.0195 |
| 0.4735 | 8340 | 0.0186 |
| 0.4741 | 8350 | 0.0186 |
| 0.4746 | 8360 | 0.0193 |
| 0.4752 | 8370 | 0.0187 |
| 0.4758 | 8380 | 0.0189 |
| 0.4764 | 8390 | 0.0194 |
| 0.4769 | 8400 | 0.0191 |
| 0.4775 | 8410 | 0.0185 |
| 0.4781 | 8420 | 0.0183 |
| 0.4786 | 8430 | 0.0181 |
| 0.4792 | 8440 | 0.0191 |
| 0.4798 | 8450 | 0.0196 |
| 0.4803 | 8460 | 0.0194 |
| 0.4809 | 8470 | 0.0188 |
| 0.4815 | 8480 | 0.019 |
| 0.4820 | 8490 | 0.0184 |
| 0.4826 | 8500 | 0.0192 |
| 0.4832 | 8510 | 0.0192 |
| 0.4837 | 8520 | 0.0191 |
| 0.4843 | 8530 | 0.0187 |
| 0.4849 | 8540 | 0.0192 |
| 0.4854 | 8550 | 0.019 |
| 0.4860 | 8560 | 0.0184 |
| 0.4866 | 8570 | 0.0186 |
| 0.4871 | 8580 | 0.0201 |
| 0.4877 | 8590 | 0.0189 |
| 0.4883 | 8600 | 0.0184 |
| 0.4888 | 8610 | 0.0194 |
| 0.4894 | 8620 | 0.0192 |
| 0.4900 | 8630 | 0.0188 |
| 0.4905 | 8640 | 0.0186 |
| 0.4911 | 8650 | 0.0193 |
| 0.4917 | 8660 | 0.0187 |
| 0.4923 | 8670 | 0.0189 |
| 0.4928 | 8680 | 0.019 |
| 0.4934 | 8690 | 0.0188 |
| 0.4940 | 8700 | 0.0194 |
| 0.4945 | 8710 | 0.0187 |
| 0.4951 | 8720 | 0.0183 |
| 0.4957 | 8730 | 0.0192 |
| 0.4962 | 8740 | 0.0183 |
| 0.4968 | 8750 | 0.0179 |
| 0.4974 | 8760 | 0.0184 |
| 0.4979 | 8770 | 0.0197 |
| 0.4985 | 8780 | 0.0191 |
| 0.4991 | 8790 | 0.0197 |
| 0.4996 | 8800 | 0.0206 |
| 0.5002 | 8810 | 0.0183 |
| 0.5008 | 8820 | 0.0186 |
| 0.5013 | 8830 | 0.0178 |
| 0.5019 | 8840 | 0.0185 |
| 0.5025 | 8850 | 0.0191 |
| 0.5030 | 8860 | 0.0189 |
| 0.5036 | 8870 | 0.0191 |
| 0.5042 | 8880 | 0.0186 |
| 0.5047 | 8890 | 0.0192 |
| 0.5053 | 8900 | 0.019 |
| 0.5059 | 8910 | 0.0194 |
| 0.5064 | 8920 | 0.0193 |
| 0.5070 | 8930 | 0.0184 |
| 0.5076 | 8940 | 0.0185 |
| 0.5081 | 8950 | 0.0189 |
| 0.5087 | 8960 | 0.0191 |
| 0.5093 | 8970 | 0.0177 |
| 0.5099 | 8980 | 0.0186 |
| 0.5104 | 8990 | 0.0195 |
| 0.5110 | 9000 | 0.0179 |
| 0.5116 | 9010 | 0.0177 |
| 0.5121 | 9020 | 0.0183 |
| 0.5127 | 9030 | 0.0191 |
| 0.5133 | 9040 | 0.0185 |
| 0.5138 | 9050 | 0.0196 |
| 0.5144 | 9060 | 0.019 |
| 0.5150 | 9070 | 0.0192 |
| 0.5155 | 9080 | 0.0195 |
| 0.5161 | 9090 | 0.0194 |
| 0.5167 | 9100 | 0.0191 |
| 0.5172 | 9110 | 0.0208 |
| 0.5178 | 9120 | 0.0192 |
| 0.5184 | 9130 | 0.0189 |
| 0.5189 | 9140 | 0.0179 |
| 0.5195 | 9150 | 0.0198 |
| 0.5201 | 9160 | 0.0185 |
| 0.5206 | 9170 | 0.0191 |
| 0.5212 | 9180 | 0.0185 |
| 0.5218 | 9190 | 0.0188 |
| 0.5223 | 9200 | 0.0191 |
| 0.5229 | 9210 | 0.0189 |
| 0.5235 | 9220 | 0.0186 |
| 0.5240 | 9230 | 0.0196 |
| 0.5246 | 9240 | 0.0193 |
| 0.5252 | 9250 | 0.0199 |
| 0.5257 | 9260 | 0.0199 |
| 0.5263 | 9270 | 0.0192 |
| 0.5269 | 9280 | 0.0187 |
| 0.5275 | 9290 | 0.0188 |
| 0.5280 | 9300 | 0.0181 |
| 0.5286 | 9310 | 0.0189 |
| 0.5292 | 9320 | 0.0193 |
| 0.5297 | 9330 | 0.0183 |
| 0.5303 | 9340 | 0.0194 |
| 0.5309 | 9350 | 0.017 |
| 0.5314 | 9360 | 0.0183 |
| 0.5320 | 9370 | 0.0192 |
| 0.5326 | 9380 | 0.0187 |
| 0.5331 | 9390 | 0.0184 |
| 0.5337 | 9400 | 0.0186 |
| 0.5343 | 9410 | 0.0191 |
| 0.5348 | 9420 | 0.0194 |
| 0.5354 | 9430 | 0.0187 |
| 0.5360 | 9440 | 0.0184 |
| 0.5365 | 9450 | 0.0193 |
| 0.5371 | 9460 | 0.0191 |
| 0.5377 | 9470 | 0.0188 |
| 0.5382 | 9480 | 0.0184 |
| 0.5388 | 9490 | 0.0184 |
| 0.5394 | 9500 | 0.0197 |
| 0.5399 | 9510 | 0.0183 |
| 0.5405 | 9520 | 0.0181 |
| 0.5411 | 9530 | 0.0188 |
| 0.5416 | 9540 | 0.0194 |
| 0.5422 | 9550 | 0.0186 |
| 0.5428 | 9560 | 0.0187 |
| 0.5433 | 9570 | 0.0187 |
| 0.5439 | 9580 | 0.0182 |
| 0.5445 | 9590 | 0.0188 |
| 0.5451 | 9600 | 0.019 |
| 0.5456 | 9610 | 0.0201 |
| 0.5462 | 9620 | 0.0191 |
| 0.5468 | 9630 | 0.0188 |
| 0.5473 | 9640 | 0.0187 |
| 0.5479 | 9650 | 0.0189 |
| 0.5485 | 9660 | 0.0193 |
| 0.5490 | 9670 | 0.0182 |
| 0.5496 | 9680 | 0.0193 |
| 0.5502 | 9690 | 0.0196 |
| 0.5507 | 9700 | 0.0184 |
| 0.5513 | 9710 | 0.0187 |
| 0.5519 | 9720 | 0.0191 |
| 0.5524 | 9730 | 0.0185 |
| 0.5530 | 9740 | 0.0189 |
| 0.5536 | 9750 | 0.0186 |
| 0.5541 | 9760 | 0.0194 |
| 0.5547 | 9770 | 0.0189 |
| 0.5553 | 9780 | 0.0187 |
| 0.5558 | 9790 | 0.0187 |
| 0.5564 | 9800 | 0.0185 |
| 0.5570 | 9810 | 0.0179 |
| 0.5575 | 9820 | 0.0197 |
| 0.5581 | 9830 | 0.0182 |
| 0.5587 | 9840 | 0.018 |
| 0.5592 | 9850 | 0.0187 |
| 0.5598 | 9860 | 0.0186 |
| 0.5604 | 9870 | 0.0188 |
| 0.5609 | 9880 | 0.0181 |
| 0.5615 | 9890 | 0.0186 |
| 0.5621 | 9900 | 0.0197 |
| 0.5627 | 9910 | 0.0191 |
| 0.5632 | 9920 | 0.019 |
| 0.5638 | 9930 | 0.019 |
| 0.5644 | 9940 | 0.019 |
| 0.5649 | 9950 | 0.0192 |
| 0.5655 | 9960 | 0.0177 |
| 0.5661 | 9970 | 0.0184 |
| 0.5666 | 9980 | 0.0186 |
| 0.5672 | 9990 | 0.0187 |
| 0.5678 | 10000 | 0.0181 |
| 0.5683 | 10010 | 0.0189 |
| 0.5689 | 10020 | 0.0194 |
| 0.5695 | 10030 | 0.0181 |
| 0.5700 | 10040 | 0.0184 |
| 0.5706 | 10050 | 0.0175 |
| 0.5712 | 10060 | 0.0181 |
| 0.5717 | 10070 | 0.0187 |
| 0.5723 | 10080 | 0.0194 |
| 0.5729 | 10090 | 0.0188 |
| 0.5734 | 10100 | 0.0185 |
| 0.5740 | 10110 | 0.019 |
| 0.5746 | 10120 | 0.0192 |
| 0.5751 | 10130 | 0.0192 |
| 0.5757 | 10140 | 0.0185 |
| 0.5763 | 10150 | 0.0178 |
| 0.5768 | 10160 | 0.0179 |
| 0.5774 | 10170 | 0.0194 |
| 0.5780 | 10180 | 0.0196 |
| 0.5785 | 10190 | 0.0183 |
| 0.5791 | 10200 | 0.0184 |
| 0.5797 | 10210 | 0.0184 |
| 0.5803 | 10220 | 0.0192 |
| 0.5808 | 10230 | 0.0193 |
| 0.5814 | 10240 | 0.0185 |
| 0.5820 | 10250 | 0.0192 |
| 0.5825 | 10260 | 0.0195 |
| 0.5831 | 10270 | 0.0192 |
| 0.5837 | 10280 | 0.0189 |
| 0.5842 | 10290 | 0.0183 |
| 0.5848 | 10300 | 0.019 |
| 0.5854 | 10310 | 0.0188 |
| 0.5859 | 10320 | 0.019 |
| 0.5865 | 10330 | 0.0192 |
| 0.5871 | 10340 | 0.0185 |
| 0.5876 | 10350 | 0.0193 |
| 0.5882 | 10360 | 0.0183 |
| 0.5888 | 10370 | 0.0196 |
| 0.5893 | 10380 | 0.0187 |
| 0.5899 | 10390 | 0.0185 |
| 0.5905 | 10400 | 0.0186 |
| 0.5910 | 10410 | 0.0182 |
| 0.5916 | 10420 | 0.0193 |
| 0.5922 | 10430 | 0.019 |
| 0.5927 | 10440 | 0.0186 |
| 0.5933 | 10450 | 0.0184 |
| 0.5939 | 10460 | 0.0192 |
| 0.5944 | 10470 | 0.0173 |
| 0.5950 | 10480 | 0.0184 |
| 0.5956 | 10490 | 0.0186 |
| 0.5962 | 10500 | 0.0176 |
| 0.5967 | 10510 | 0.0183 |
| 0.5973 | 10520 | 0.0183 |
| 0.5979 | 10530 | 0.0182 |
| 0.5984 | 10540 | 0.0191 |
| 0.5990 | 10550 | 0.0188 |
| 0.5996 | 10560 | 0.0191 |
| 0.6001 | 10570 | 0.0189 |
| 0.6007 | 10580 | 0.0183 |
| 0.6013 | 10590 | 0.0178 |
| 0.6018 | 10600 | 0.0175 |
| 0.6024 | 10610 | 0.0204 |
| 0.6030 | 10620 | 0.019 |
| 0.6035 | 10630 | 0.0189 |
| 0.6041 | 10640 | 0.0183 |
| 0.6047 | 10650 | 0.0188 |
| 0.6052 | 10660 | 0.0191 |
| 0.6058 | 10670 | 0.0193 |
| 0.6064 | 10680 | 0.0182 |
| 0.6069 | 10690 | 0.019 |
| 0.6075 | 10700 | 0.0183 |
| 0.6081 | 10710 | 0.0183 |
| 0.6086 | 10720 | 0.0186 |
| 0.6092 | 10730 | 0.0189 |
| 0.6098 | 10740 | 0.0175 |
| 0.6103 | 10750 | 0.0187 |
| 0.6109 | 10760 | 0.0186 |
| 0.6115 | 10770 | 0.0192 |
| 0.6120 | 10780 | 0.0186 |
| 0.6126 | 10790 | 0.0181 |
| 0.6132 | 10800 | 0.0188 |
| 0.6138 | 10810 | 0.0176 |
| 0.6143 | 10820 | 0.019 |
| 0.6149 | 10830 | 0.0176 |
| 0.6155 | 10840 | 0.0185 |
| 0.6160 | 10850 | 0.019 |
| 0.6166 | 10860 | 0.0185 |
| 0.6172 | 10870 | 0.019 |
| 0.6177 | 10880 | 0.018 |
| 0.6183 | 10890 | 0.0185 |
| 0.6189 | 10900 | 0.0185 |
| 0.6194 | 10910 | 0.0182 |
| 0.6200 | 10920 | 0.0185 |
| 0.6206 | 10930 | 0.0177 |
| 0.6211 | 10940 | 0.0186 |
| 0.6217 | 10950 | 0.0184 |
| 0.6223 | 10960 | 0.0187 |
| 0.6228 | 10970 | 0.018 |
| 0.6234 | 10980 | 0.0186 |
| 0.6240 | 10990 | 0.0184 |
| 0.6245 | 11000 | 0.0188 |
| 0.6251 | 11010 | 0.0187 |
| 0.6257 | 11020 | 0.0188 |
| 0.6262 | 11030 | 0.0189 |
| 0.6268 | 11040 | 0.019 |
| 0.6274 | 11050 | 0.0187 |
| 0.6279 | 11060 | 0.018 |
| 0.6285 | 11070 | 0.019 |
| 0.6291 | 11080 | 0.0185 |
| 0.6296 | 11090 | 0.0183 |
| 0.6302 | 11100 | 0.0184 |
| 0.6308 | 11110 | 0.0186 |
| 0.6314 | 11120 | 0.0187 |
| 0.6319 | 11130 | 0.0179 |
| 0.6325 | 11140 | 0.0183 |
| 0.6331 | 11150 | 0.0172 |
| 0.6336 | 11160 | 0.0181 |
| 0.6342 | 11170 | 0.0182 |
| 0.6348 | 11180 | 0.0194 |
| 0.6353 | 11190 | 0.018 |
| 0.6359 | 11200 | 0.0197 |
| 0.6365 | 11210 | 0.0181 |
| 0.6370 | 11220 | 0.0182 |
| 0.6376 | 11230 | 0.0187 |
| 0.6382 | 11240 | 0.019 |
| 0.6387 | 11250 | 0.0186 |
| 0.6393 | 11260 | 0.0181 |
| 0.6399 | 11270 | 0.0193 |
| 0.6404 | 11280 | 0.0193 |
| 0.6410 | 11290 | 0.0174 |
| 0.6416 | 11300 | 0.0184 |
| 0.6421 | 11310 | 0.0189 |
| 0.6427 | 11320 | 0.0182 |
| 0.6433 | 11330 | 0.0184 |
| 0.6438 | 11340 | 0.0181 |
| 0.6444 | 11350 | 0.0183 |
| 0.6450 | 11360 | 0.0191 |
| 0.6455 | 11370 | 0.018 |
| 0.6461 | 11380 | 0.0185 |
| 0.6467 | 11390 | 0.0189 |
| 0.6472 | 11400 | 0.0185 |
| 0.6478 | 11410 | 0.0183 |
| 0.6484 | 11420 | 0.0183 |
| 0.6490 | 11430 | 0.0186 |
| 0.6495 | 11440 | 0.0179 |
| 0.6501 | 11450 | 0.0174 |
| 0.6507 | 11460 | 0.0181 |
| 0.6512 | 11470 | 0.0175 |
| 0.6518 | 11480 | 0.0179 |
| 0.6524 | 11490 | 0.0183 |
| 0.6529 | 11500 | 0.0179 |
| 0.6535 | 11510 | 0.0184 |
| 0.6541 | 11520 | 0.0186 |
| 0.6546 | 11530 | 0.018 |
| 0.6552 | 11540 | 0.0183 |
| 0.6558 | 11550 | 0.0183 |
| 0.6563 | 11560 | 0.0185 |
| 0.6569 | 11570 | 0.0181 |
| 0.6575 | 11580 | 0.0181 |
| 0.6580 | 11590 | 0.0188 |
| 0.6586 | 11600 | 0.0188 |
| 0.6592 | 11610 | 0.0182 |
| 0.6597 | 11620 | 0.0188 |
| 0.6603 | 11630 | 0.0194 |
| 0.6609 | 11640 | 0.0185 |
| 0.6614 | 11650 | 0.0194 |
| 0.6620 | 11660 | 0.019 |
| 0.6626 | 11670 | 0.0186 |
| 0.6631 | 11680 | 0.0181 |
| 0.6637 | 11690 | 0.0181 |
| 0.6643 | 11700 | 0.0183 |
| 0.6648 | 11710 | 0.0187 |
| 0.6654 | 11720 | 0.0184 |
| 0.6660 | 11730 | 0.0183 |
| 0.6666 | 11740 | 0.0177 |
| 0.6671 | 11750 | 0.018 |
| 0.6677 | 11760 | 0.0182 |
| 0.6683 | 11770 | 0.0184 |
| 0.6688 | 11780 | 0.0185 |
| 0.6694 | 11790 | 0.0191 |
| 0.6700 | 11800 | 0.0187 |
| 0.6705 | 11810 | 0.018 |
| 0.6711 | 11820 | 0.0187 |
| 0.6717 | 11830 | 0.0178 |
| 0.6722 | 11840 | 0.0178 |
| 0.6728 | 11850 | 0.0192 |
| 0.6734 | 11860 | 0.0186 |
| 0.6739 | 11870 | 0.019 |
| 0.6745 | 11880 | 0.0188 |
| 0.6751 | 11890 | 0.0185 |
| 0.6756 | 11900 | 0.0188 |
| 0.6762 | 11910 | 0.0176 |
| 0.6768 | 11920 | 0.0186 |
| 0.6773 | 11930 | 0.0188 |
| 0.6779 | 11940 | 0.0183 |
| 0.6785 | 11950 | 0.02 |
| 0.6790 | 11960 | 0.0192 |
| 0.6796 | 11970 | 0.0184 |
| 0.6802 | 11980 | 0.0175 |
| 0.6807 | 11990 | 0.0179 |
| 0.6813 | 12000 | 0.0185 |
| 0.6819 | 12010 | 0.0185 |
| 0.6825 | 12020 | 0.019 |
| 0.6830 | 12030 | 0.02 |
| 0.6836 | 12040 | 0.018 |
| 0.6842 | 12050 | 0.0181 |
| 0.6847 | 12060 | 0.0169 |
| 0.6853 | 12070 | 0.018 |
| 0.6859 | 12080 | 0.0182 |
| 0.6864 | 12090 | 0.019 |
| 0.6870 | 12100 | 0.0182 |
| 0.6876 | 12110 | 0.0183 |
| 0.6881 | 12120 | 0.0175 |
| 0.6887 | 12130 | 0.0182 |
| 0.6893 | 12140 | 0.0196 |
| 0.6898 | 12150 | 0.0185 |
| 0.6904 | 12160 | 0.0179 |
| 0.6910 | 12170 | 0.0193 |
| 0.6915 | 12180 | 0.0177 |
| 0.6921 | 12190 | 0.0188 |
| 0.6927 | 12200 | 0.0197 |
| 0.6932 | 12210 | 0.0199 |
| 0.6938 | 12220 | 0.018 |
| 0.6944 | 12230 | 0.0171 |
| 0.6949 | 12240 | 0.0178 |
| 0.6955 | 12250 | 0.0197 |
| 0.6961 | 12260 | 0.0191 |
| 0.6966 | 12270 | 0.0175 |
| 0.6972 | 12280 | 0.0178 |
| 0.6978 | 12290 | 0.0191 |
| 0.6983 | 12300 | 0.0185 |
| 0.6989 | 12310 | 0.0194 |
| 0.6995 | 12320 | 0.0178 |
| 0.7001 | 12330 | 0.018 |
| 0.7006 | 12340 | 0.0179 |
| 0.7012 | 12350 | 0.0176 |
| 0.7018 | 12360 | 0.0188 |
| 0.7023 | 12370 | 0.0177 |
| 0.7029 | 12380 | 0.0179 |
| 0.7035 | 12390 | 0.0194 |
| 0.7040 | 12400 | 0.0181 |
| 0.7046 | 12410 | 0.018 |
| 0.7052 | 12420 | 0.0188 |
| 0.7057 | 12430 | 0.0184 |
| 0.7063 | 12440 | 0.0178 |
| 0.7069 | 12450 | 0.0175 |
| 0.7074 | 12460 | 0.0182 |
| 0.7080 | 12470 | 0.0189 |
| 0.7086 | 12480 | 0.0182 |
| 0.7091 | 12490 | 0.0193 |
| 0.7097 | 12500 | 0.0187 |
| 0.7103 | 12510 | 0.0179 |
| 0.7108 | 12520 | 0.0182 |
| 0.7114 | 12530 | 0.0194 |
| 0.7120 | 12540 | 0.018 |
| 0.7125 | 12550 | 0.0184 |
| 0.7131 | 12560 | 0.0176 |
| 0.7137 | 12570 | 0.0192 |
| 0.7142 | 12580 | 0.0186 |
| 0.7148 | 12590 | 0.0178 |
| 0.7154 | 12600 | 0.0196 |
| 0.7159 | 12610 | 0.0187 |
| 0.7165 | 12620 | 0.0183 |
| 0.7171 | 12630 | 0.0192 |
| 0.7177 | 12640 | 0.0192 |
| 0.7182 | 12650 | 0.0182 |
| 0.7188 | 12660 | 0.0192 |
| 0.7194 | 12670 | 0.0189 |
| 0.7199 | 12680 | 0.018 |
| 0.7205 | 12690 | 0.0182 |
| 0.7211 | 12700 | 0.0185 |
| 0.7216 | 12710 | 0.0187 |
| 0.7222 | 12720 | 0.0186 |
| 0.7228 | 12730 | 0.0185 |
| 0.7233 | 12740 | 0.0182 |
| 0.7239 | 12750 | 0.0182 |
| 0.7245 | 12760 | 0.0181 |
| 0.7250 | 12770 | 0.0191 |
| 0.7256 | 12780 | 0.0184 |
| 0.7262 | 12790 | 0.0185 |
| 0.7267 | 12800 | 0.0182 |
| 0.7273 | 12810 | 0.0187 |
| 0.7279 | 12820 | 0.0189 |
| 0.7284 | 12830 | 0.0183 |
| 0.7290 | 12840 | 0.0178 |
| 0.7296 | 12850 | 0.0178 |
| 0.7301 | 12860 | 0.0182 |
| 0.7307 | 12870 | 0.0186 |
| 0.7313 | 12880 | 0.0189 |
| 0.7318 | 12890 | 0.0186 |
| 0.7324 | 12900 | 0.0193 |
| 0.7330 | 12910 | 0.0185 |
| 0.7335 | 12920 | 0.0172 |
| 0.7341 | 12930 | 0.0177 |
| 0.7347 | 12940 | 0.0186 |
| 0.7353 | 12950 | 0.0179 |
| 0.7358 | 12960 | 0.0189 |
| 0.7364 | 12970 | 0.0185 |
| 0.7370 | 12980 | 0.0174 |
| 0.7375 | 12990 | 0.0181 |
| 0.7381 | 13000 | 0.0185 |
| 0.7387 | 13010 | 0.0184 |
| 0.7392 | 13020 | 0.0176 |
| 0.7398 | 13030 | 0.019 |
| 0.7404 | 13040 | 0.0184 |
| 0.7409 | 13050 | 0.0177 |
| 0.7415 | 13060 | 0.0175 |
| 0.7421 | 13070 | 0.0201 |
| 0.7426 | 13080 | 0.0195 |
| 0.7432 | 13090 | 0.0188 |
| 0.7438 | 13100 | 0.0192 |
| 0.7443 | 13110 | 0.0168 |
| 0.7449 | 13120 | 0.0176 |
| 0.7455 | 13130 | 0.0192 |
| 0.7460 | 13140 | 0.0188 |
| 0.7466 | 13150 | 0.0184 |
| 0.7472 | 13160 | 0.0173 |
| 0.7477 | 13170 | 0.0184 |
| 0.7483 | 13180 | 0.0195 |
| 0.7489 | 13190 | 0.0174 |
| 0.7494 | 13200 | 0.0177 |
| 0.7500 | 13210 | 0.0183 |
| 0.7506 | 13220 | 0.0179 |
| 0.7511 | 13230 | 0.0186 |
| 0.7517 | 13240 | 0.0172 |
| 0.7523 | 13250 | 0.0188 |
| 0.7529 | 13260 | 0.0194 |
| 0.7534 | 13270 | 0.0185 |
| 0.7540 | 13280 | 0.0172 |
| 0.7546 | 13290 | 0.0182 |
| 0.7551 | 13300 | 0.0174 |
| 0.7557 | 13310 | 0.0183 |
| 0.7563 | 13320 | 0.0176 |
| 0.7568 | 13330 | 0.0187 |
| 0.7574 | 13340 | 0.0191 |
| 0.7580 | 13350 | 0.0175 |
| 0.7585 | 13360 | 0.0184 |
| 0.7591 | 13370 | 0.0182 |
| 0.7597 | 13380 | 0.0179 |
| 0.7602 | 13390 | 0.0183 |
| 0.7608 | 13400 | 0.0185 |
| 0.7614 | 13410 | 0.0188 |
| 0.7619 | 13420 | 0.0178 |
| 0.7625 | 13430 | 0.0181 |
| 0.7631 | 13440 | 0.0176 |
| 0.7636 | 13450 | 0.0189 |
| 0.7642 | 13460 | 0.0186 |
| 0.7648 | 13470 | 0.0186 |
| 0.7653 | 13480 | 0.0186 |
| 0.7659 | 13490 | 0.0186 |
| 0.7665 | 13500 | 0.0186 |
| 0.7670 | 13510 | 0.0183 |
| 0.7676 | 13520 | 0.0184 |
| 0.7682 | 13530 | 0.0178 |
| 0.7688 | 13540 | 0.0185 |
| 0.7693 | 13550 | 0.0178 |
| 0.7699 | 13560 | 0.0171 |
| 0.7705 | 13570 | 0.018 |
| 0.7710 | 13580 | 0.0181 |
| 0.7716 | 13590 | 0.0182 |
| 0.7722 | 13600 | 0.0179 |
| 0.7727 | 13610 | 0.018 |
| 0.7733 | 13620 | 0.0191 |
| 0.7739 | 13630 | 0.0183 |
| 0.7744 | 13640 | 0.0181 |
| 0.7750 | 13650 | 0.0176 |
| 0.7756 | 13660 | 0.0195 |
| 0.7761 | 13670 | 0.0192 |
| 0.7767 | 13680 | 0.0169 |
| 0.7773 | 13690 | 0.018 |
| 0.7778 | 13700 | 0.0175 |
| 0.7784 | 13710 | 0.019 |
| 0.7790 | 13720 | 0.0184 |
| 0.7795 | 13730 | 0.0194 |
| 0.7801 | 13740 | 0.0183 |
| 0.7807 | 13750 | 0.0182 |
| 0.7812 | 13760 | 0.0174 |
| 0.7818 | 13770 | 0.0185 |
| 0.7824 | 13780 | 0.018 |
| 0.7829 | 13790 | 0.0178 |
| 0.7835 | 13800 | 0.0179 |
| 0.7841 | 13810 | 0.019 |
| 0.7846 | 13820 | 0.0174 |
| 0.7852 | 13830 | 0.0178 |
| 0.7858 | 13840 | 0.0184 |
| 0.7864 | 13850 | 0.0177 |
| 0.7869 | 13860 | 0.0186 |
| 0.7875 | 13870 | 0.0175 |
| 0.7881 | 13880 | 0.0183 |
| 0.7886 | 13890 | 0.0179 |
| 0.7892 | 13900 | 0.0197 |
| 0.7898 | 13910 | 0.0183 |
| 0.7903 | 13920 | 0.0172 |
| 0.7909 | 13930 | 0.019 |
| 0.7915 | 13940 | 0.0184 |
| 0.7920 | 13950 | 0.0187 |
| 0.7926 | 13960 | 0.0175 |
| 0.7932 | 13970 | 0.0175 |
| 0.7937 | 13980 | 0.0184 |
| 0.7943 | 13990 | 0.019 |
| 0.7949 | 14000 | 0.0183 |
| 0.7954 | 14010 | 0.018 |
| 0.7960 | 14020 | 0.0191 |
| 0.7966 | 14030 | 0.0186 |
| 0.7971 | 14040 | 0.0179 |
| 0.7977 | 14050 | 0.0176 |
| 0.7983 | 14060 | 0.0181 |
| 0.7988 | 14070 | 0.0187 |
| 0.7994 | 14080 | 0.0186 |
| 0.8000 | 14090 | 0.0176 |
| 0.8005 | 14100 | 0.0185 |
| 0.8011 | 14110 | 0.0184 |
| 0.8017 | 14120 | 0.0189 |
| 0.8022 | 14130 | 0.018 |
| 0.8028 | 14140 | 0.018 |
| 0.8034 | 14150 | 0.0165 |
| 0.8040 | 14160 | 0.0181 |
| 0.8045 | 14170 | 0.0188 |
| 0.8051 | 14180 | 0.0176 |
| 0.8057 | 14190 | 0.0173 |
| 0.8062 | 14200 | 0.0182 |
| 0.8068 | 14210 | 0.0182 |
| 0.8074 | 14220 | 0.0181 |
| 0.8079 | 14230 | 0.0177 |
| 0.8085 | 14240 | 0.0181 |
| 0.8091 | 14250 | 0.0192 |
| 0.8096 | 14260 | 0.0182 |
| 0.8102 | 14270 | 0.0192 |
| 0.8108 | 14280 | 0.0176 |
| 0.8113 | 14290 | 0.0177 |
| 0.8119 | 14300 | 0.0173 |
| 0.8125 | 14310 | 0.0179 |
| 0.8130 | 14320 | 0.0185 |
| 0.8136 | 14330 | 0.0174 |
| 0.8142 | 14340 | 0.018 |
| 0.8147 | 14350 | 0.0181 |
| 0.8153 | 14360 | 0.0183 |
| 0.8159 | 14370 | 0.0185 |
| 0.8164 | 14380 | 0.0183 |
| 0.8170 | 14390 | 0.0179 |
| 0.8176 | 14400 | 0.0181 |
| 0.8181 | 14410 | 0.0189 |
| 0.8187 | 14420 | 0.0191 |
| 0.8193 | 14430 | 0.0185 |
| 0.8198 | 14440 | 0.0179 |
| 0.8204 | 14450 | 0.0182 |
| 0.8210 | 14460 | 0.0177 |
| 0.8216 | 14470 | 0.0181 |
| 0.8221 | 14480 | 0.0181 |
| 0.8227 | 14490 | 0.0183 |
| 0.8233 | 14500 | 0.0176 |
| 0.8238 | 14510 | 0.0178 |
| 0.8244 | 14520 | 0.0177 |
| 0.8250 | 14530 | 0.0193 |
| 0.8255 | 14540 | 0.0179 |
| 0.8261 | 14550 | 0.0182 |
| 0.8267 | 14560 | 0.0192 |
| 0.8272 | 14570 | 0.0188 |
| 0.8278 | 14580 | 0.0188 |
| 0.8284 | 14590 | 0.0188 |
| 0.8289 | 14600 | 0.0187 |
| 0.8295 | 14610 | 0.0177 |
| 0.8301 | 14620 | 0.0178 |
| 0.8306 | 14630 | 0.0174 |
| 0.8312 | 14640 | 0.0178 |
| 0.8318 | 14650 | 0.0184 |
| 0.8323 | 14660 | 0.018 |
| 0.8329 | 14670 | 0.0177 |
| 0.8335 | 14680 | 0.019 |
| 0.8340 | 14690 | 0.0187 |
| 0.8346 | 14700 | 0.0176 |
| 0.8352 | 14710 | 0.0181 |
| 0.8357 | 14720 | 0.0183 |
| 0.8363 | 14730 | 0.0182 |
| 0.8369 | 14740 | 0.0188 |
| 0.8374 | 14750 | 0.0184 |
| 0.8380 | 14760 | 0.0177 |
| 0.8386 | 14770 | 0.0176 |
| 0.8392 | 14780 | 0.0179 |
| 0.8397 | 14790 | 0.0181 |
| 0.8403 | 14800 | 0.0185 |
| 0.8409 | 14810 | 0.0177 |
| 0.8414 | 14820 | 0.0184 |
| 0.8420 | 14830 | 0.0171 |
| 0.8426 | 14840 | 0.0186 |
| 0.8431 | 14850 | 0.0183 |
| 0.8437 | 14860 | 0.018 |
| 0.8443 | 14870 | 0.0192 |
| 0.8448 | 14880 | 0.0183 |
| 0.8454 | 14890 | 0.017 |
| 0.8460 | 14900 | 0.0175 |
| 0.8465 | 14910 | 0.0188 |
| 0.8471 | 14920 | 0.0168 |
| 0.8477 | 14930 | 0.0175 |
| 0.8482 | 14940 | 0.0165 |
| 0.8488 | 14950 | 0.0183 |
| 0.8494 | 14960 | 0.0184 |
| 0.8499 | 14970 | 0.0181 |
| 0.8505 | 14980 | 0.0184 |
| 0.8511 | 14990 | 0.0188 |
| 0.8516 | 15000 | 0.0183 |
| 0.8522 | 15010 | 0.0177 |
| 0.8528 | 15020 | 0.0185 |
| 0.8533 | 15030 | 0.0179 |
| 0.8539 | 15040 | 0.0171 |
| 0.8545 | 15050 | 0.0175 |
| 0.8551 | 15060 | 0.018 |
| 0.8556 | 15070 | 0.0181 |
| 0.8562 | 15080 | 0.019 |
| 0.8568 | 15090 | 0.0185 |
| 0.8573 | 15100 | 0.0185 |
| 0.8579 | 15110 | 0.0175 |
| 0.8585 | 15120 | 0.0178 |
| 0.8590 | 15130 | 0.0184 |
| 0.8596 | 15140 | 0.018 |
| 0.8602 | 15150 | 0.0188 |
| 0.8607 | 15160 | 0.0188 |
| 0.8613 | 15170 | 0.0179 |
| 0.8619 | 15180 | 0.0189 |
| 0.8624 | 15190 | 0.0182 |
| 0.8630 | 15200 | 0.0188 |
| 0.8636 | 15210 | 0.0168 |
| 0.8641 | 15220 | 0.0189 |
| 0.8647 | 15230 | 0.0182 |
| 0.8653 | 15240 | 0.0183 |
| 0.8658 | 15250 | 0.0188 |
| 0.8664 | 15260 | 0.0182 |
| 0.8670 | 15270 | 0.0176 |
| 0.8675 | 15280 | 0.0195 |
| 0.8681 | 15290 | 0.0185 |
| 0.8687 | 15300 | 0.0178 |
| 0.8692 | 15310 | 0.0174 |
| 0.8698 | 15320 | 0.0177 |
| 0.8704 | 15330 | 0.0182 |
| 0.8709 | 15340 | 0.018 |
| 0.8715 | 15350 | 0.018 |
| 0.8721 | 15360 | 0.0178 |
| 0.8727 | 15370 | 0.0186 |
| 0.8732 | 15380 | 0.0181 |
| 0.8738 | 15390 | 0.018 |
| 0.8744 | 15400 | 0.0175 |
| 0.8749 | 15410 | 0.019 |
| 0.8755 | 15420 | 0.0175 |
| 0.8761 | 15430 | 0.0184 |
| 0.8766 | 15440 | 0.0179 |
| 0.8772 | 15450 | 0.0181 |
| 0.8778 | 15460 | 0.0172 |
| 0.8783 | 15470 | 0.0183 |
| 0.8789 | 15480 | 0.0183 |
| 0.8795 | 15490 | 0.0177 |
| 0.8800 | 15500 | 0.0186 |
| 0.8806 | 15510 | 0.0183 |
| 0.8812 | 15520 | 0.0185 |
| 0.8817 | 15530 | 0.0187 |
| 0.8823 | 15540 | 0.0182 |
| 0.8829 | 15550 | 0.0174 |
| 0.8834 | 15560 | 0.0179 |
| 0.8840 | 15570 | 0.017 |
| 0.8846 | 15580 | 0.0194 |
| 0.8851 | 15590 | 0.0188 |
| 0.8857 | 15600 | 0.0183 |
| 0.8863 | 15610 | 0.0183 |
| 0.8868 | 15620 | 0.0176 |
| 0.8874 | 15630 | 0.0176 |
| 0.8880 | 15640 | 0.0172 |
| 0.8885 | 15650 | 0.017 |
| 0.8891 | 15660 | 0.0173 |
| 0.8897 | 15670 | 0.0173 |
| 0.8903 | 15680 | 0.0182 |
| 0.8908 | 15690 | 0.0171 |
| 0.8914 | 15700 | 0.0175 |
| 0.8920 | 15710 | 0.0198 |
| 0.8925 | 15720 | 0.0177 |
| 0.8931 | 15730 | 0.0176 |
| 0.8937 | 15740 | 0.0178 |
| 0.8942 | 15750 | 0.0181 |
| 0.8948 | 15760 | 0.0176 |
| 0.8954 | 15770 | 0.0173 |
| 0.8959 | 15780 | 0.0183 |
| 0.8965 | 15790 | 0.0183 |
| 0.8971 | 15800 | 0.0181 |
| 0.8976 | 15810 | 0.0178 |
| 0.8982 | 15820 | 0.0183 |
| 0.8988 | 15830 | 0.0168 |
| 0.8993 | 15840 | 0.0186 |
| 0.8999 | 15850 | 0.0179 |
| 0.9005 | 15860 | 0.0188 |
| 0.9010 | 15870 | 0.018 |
| 0.9016 | 15880 | 0.0187 |
| 0.9022 | 15890 | 0.0173 |
| 0.9027 | 15900 | 0.0177 |
| 0.9033 | 15910 | 0.0174 |
| 0.9039 | 15920 | 0.0177 |
| 0.9044 | 15930 | 0.0185 |
| 0.9050 | 15940 | 0.0187 |
| 0.9056 | 15950 | 0.0174 |
| 0.9061 | 15960 | 0.0179 |
| 0.9067 | 15970 | 0.0181 |
| 0.9073 | 15980 | 0.0185 |
| 0.9079 | 15990 | 0.0184 |
| 0.9084 | 16000 | 0.0177 |
| 0.9090 | 16010 | 0.018 |
| 0.9096 | 16020 | 0.0187 |
| 0.9101 | 16030 | 0.0185 |
| 0.9107 | 16040 | 0.0184 |
| 0.9113 | 16050 | 0.017 |
| 0.9118 | 16060 | 0.0178 |
| 0.9124 | 16070 | 0.0173 |
| 0.9130 | 16080 | 0.0172 |
| 0.9135 | 16090 | 0.0188 |
| 0.9141 | 16100 | 0.0191 |
| 0.9147 | 16110 | 0.0176 |
| 0.9152 | 16120 | 0.0179 |
| 0.9158 | 16130 | 0.0188 |
| 0.9164 | 16140 | 0.0175 |
| 0.9169 | 16150 | 0.0186 |
| 0.9175 | 16160 | 0.0182 |
| 0.9181 | 16170 | 0.0178 |
| 0.9186 | 16180 | 0.0176 |
| 0.9192 | 16190 | 0.0171 |
| 0.9198 | 16200 | 0.0187 |
| 0.9203 | 16210 | 0.0177 |
| 0.9209 | 16220 | 0.0183 |
| 0.9215 | 16230 | 0.0182 |
| 0.9220 | 16240 | 0.0188 |
| 0.9226 | 16250 | 0.0179 |
| 0.9232 | 16260 | 0.0188 |
| 0.9237 | 16270 | 0.0181 |
| 0.9243 | 16280 | 0.018 |
| 0.9249 | 16290 | 0.0172 |
| 0.9255 | 16300 | 0.0174 |
| 0.9260 | 16310 | 0.0176 |
| 0.9266 | 16320 | 0.0188 |
| 0.9272 | 16330 | 0.0187 |
| 0.9277 | 16340 | 0.0181 |
| 0.9283 | 16350 | 0.0183 |
| 0.9289 | 16360 | 0.0181 |
| 0.9294 | 16370 | 0.0175 |
| 0.9300 | 16380 | 0.0192 |
| 0.9306 | 16390 | 0.0178 |
| 0.9311 | 16400 | 0.0175 |
| 0.9317 | 16410 | 0.0173 |
| 0.9323 | 16420 | 0.0187 |
| 0.9328 | 16430 | 0.0178 |
| 0.9334 | 16440 | 0.0176 |
| 0.9340 | 16450 | 0.0184 |
| 0.9345 | 16460 | 0.019 |
| 0.9351 | 16470 | 0.0186 |
| 0.9357 | 16480 | 0.0177 |
| 0.9362 | 16490 | 0.0177 |
| 0.9368 | 16500 | 0.0177 |
| 0.9374 | 16510 | 0.0189 |
| 0.9379 | 16520 | 0.0182 |
| 0.9385 | 16530 | 0.0178 |
| 0.9391 | 16540 | 0.0174 |
| 0.9396 | 16550 | 0.0187 |
| 0.9402 | 16560 | 0.0184 |
| 0.9408 | 16570 | 0.0184 |
| 0.9414 | 16580 | 0.0177 |
| 0.9419 | 16590 | 0.0176 |
| 0.9425 | 16600 | 0.0179 |
| 0.9431 | 16610 | 0.0183 |
| 0.9436 | 16620 | 0.0178 |
| 0.9442 | 16630 | 0.0182 |
| 0.9448 | 16640 | 0.0178 |
| 0.9453 | 16650 | 0.0187 |
| 0.9459 | 16660 | 0.0186 |
| 0.9465 | 16670 | 0.0176 |
| 0.9470 | 16680 | 0.0194 |
| 0.9476 | 16690 | 0.018 |
| 0.9482 | 16700 | 0.0171 |
| 0.9487 | 16710 | 0.0186 |
| 0.9493 | 16720 | 0.0179 |
| 0.9499 | 16730 | 0.018 |
| 0.9504 | 16740 | 0.0181 |
| 0.9510 | 16750 | 0.0184 |
| 0.9516 | 16760 | 0.0186 |
| 0.9521 | 16770 | 0.0179 |
| 0.9527 | 16780 | 0.0179 |
| 0.9533 | 16790 | 0.0198 |
| 0.9538 | 16800 | 0.0191 |
| 0.9544 | 16810 | 0.0181 |
| 0.9550 | 16820 | 0.0173 |
| 0.9555 | 16830 | 0.0171 |
| 0.9561 | 16840 | 0.0177 |
| 0.9567 | 16850 | 0.0178 |
| 0.9572 | 16860 | 0.0176 |
| 0.9578 | 16870 | 0.0181 |
| 0.9584 | 16880 | 0.0175 |
| 0.9590 | 16890 | 0.0166 |
| 0.9595 | 16900 | 0.0189 |
| 0.9601 | 16910 | 0.0171 |
| 0.9607 | 16920 | 0.018 |
| 0.9612 | 16930 | 0.0179 |
| 0.9618 | 16940 | 0.0177 |
| 0.9624 | 16950 | 0.0168 |
| 0.9629 | 16960 | 0.0169 |
| 0.9635 | 16970 | 0.0184 |
| 0.9641 | 16980 | 0.0178 |
| 0.9646 | 16990 | 0.0185 |
| 0.9652 | 17000 | 0.0174 |
| 0.9658 | 17010 | 0.0186 |
| 0.9663 | 17020 | 0.0178 |
| 0.9669 | 17030 | 0.0179 |
| 0.9675 | 17040 | 0.0179 |
| 0.9680 | 17050 | 0.018 |
| 0.9686 | 17060 | 0.0169 |
| 0.9692 | 17070 | 0.0184 |
| 0.9697 | 17080 | 0.0182 |
| 0.9703 | 17090 | 0.0175 |
| 0.9709 | 17100 | 0.0165 |
| 0.9714 | 17110 | 0.0177 |
| 0.9720 | 17120 | 0.0173 |
| 0.9726 | 17130 | 0.0184 |
| 0.9731 | 17140 | 0.0158 |
| 0.9737 | 17150 | 0.0176 |
| 0.9743 | 17160 | 0.0183 |
| 0.9748 | 17170 | 0.0173 |
| 0.9754 | 17180 | 0.0186 |
| 0.9760 | 17190 | 0.019 |
| 0.9766 | 17200 | 0.0184 |
| 0.9771 | 17210 | 0.0179 |
| 0.9777 | 17220 | 0.018 |
| 0.9783 | 17230 | 0.018 |
| 0.9788 | 17240 | 0.0187 |
| 0.9794 | 17250 | 0.0176 |
| 0.9800 | 17260 | 0.0173 |
| 0.9805 | 17270 | 0.0187 |
| 0.9811 | 17280 | 0.0171 |
| 0.9817 | 17290 | 0.0171 |
| 0.9822 | 17300 | 0.0184 |
| 0.9828 | 17310 | 0.0177 |
| 0.9834 | 17320 | 0.0187 |
| 0.9839 | 17330 | 0.0183 |
| 0.9845 | 17340 | 0.0186 |
| 0.9851 | 17350 | 0.0179 |
| 0.9856 | 17360 | 0.0167 |
| 0.9862 | 17370 | 0.0182 |
| 0.9868 | 17380 | 0.0192 |
| 0.9873 | 17390 | 0.0186 |
| 0.9879 | 17400 | 0.0175 |
| 0.9885 | 17410 | 0.0183 |
| 0.9890 | 17420 | 0.018 |
| 0.9896 | 17430 | 0.0171 |
| 0.9902 | 17440 | 0.0183 |
| 0.9907 | 17450 | 0.0184 |
| 0.9913 | 17460 | 0.018 |
| 0.9919 | 17470 | 0.0177 |
| 0.9924 | 17480 | 0.018 |
| 0.9930 | 17490 | 0.0184 |
| 0.9936 | 17500 | 0.0176 |
| 0.9942 | 17510 | 0.0182 |
| 0.9947 | 17520 | 0.0181 |
| 0.9953 | 17530 | 0.0176 |
| 0.9959 | 17540 | 0.0173 |
| 0.9964 | 17550 | 0.0179 |
| 0.9970 | 17560 | 0.0181 |
| 0.9976 | 17570 | 0.0181 |
| 0.9981 | 17580 | 0.0183 |
| 0.9987 | 17590 | 0.0177 |
| 0.9993 | 17600 | 0.0178 |
| 0.9998 | 17610 | 0.0179 |
| 1.0004 | 17620 | 0.0181 |
| 1.0010 | 17630 | 0.0174 |
| 1.0015 | 17640 | 0.0172 |
| 1.0021 | 17650 | 0.0167 |
| 1.0027 | 17660 | 0.017 |
| 1.0032 | 17670 | 0.0172 |
| 1.0038 | 17680 | 0.0176 |
| 1.0044 | 17690 | 0.0171 |
| 1.0049 | 17700 | 0.017 |
| 1.0055 | 17710 | 0.017 |
| 1.0061 | 17720 | 0.0166 |
| 1.0066 | 17730 | 0.0179 |
| 1.0072 | 17740 | 0.0172 |
| 1.0078 | 17750 | 0.0181 |
| 1.0083 | 17760 | 0.0171 |
| 1.0089 | 17770 | 0.0161 |
| 1.0095 | 17780 | 0.0173 |
| 1.0100 | 17790 | 0.0179 |
| 1.0106 | 17800 | 0.0173 |
| 1.0112 | 17810 | 0.0165 |
| 1.0118 | 17820 | 0.0174 |
| 1.0123 | 17830 | 0.0163 |
| 1.0129 | 17840 | 0.0175 |
| 1.0135 | 17850 | 0.0175 |
| 1.0140 | 17860 | 0.0177 |
| 1.0146 | 17870 | 0.0167 |
| 1.0152 | 17880 | 0.0173 |
| 1.0157 | 17890 | 0.0166 |
| 1.0163 | 17900 | 0.0172 |
| 1.0169 | 17910 | 0.0165 |
| 1.0174 | 17920 | 0.0175 |
| 1.0180 | 17930 | 0.0167 |
| 1.0186 | 17940 | 0.0178 |
| 1.0191 | 17950 | 0.0168 |
| 1.0197 | 17960 | 0.0172 |
| 1.0203 | 17970 | 0.0173 |
| 1.0208 | 17980 | 0.016 |
| 1.0214 | 17990 | 0.0179 |
| 1.0220 | 18000 | 0.0177 |
| 1.0225 | 18010 | 0.0174 |
| 1.0231 | 18020 | 0.0168 |
| 1.0237 | 18030 | 0.0167 |
| 1.0242 | 18040 | 0.0169 |
| 1.0248 | 18050 | 0.0168 |
| 1.0254 | 18060 | 0.0172 |
| 1.0259 | 18070 | 0.017 |
| 1.0265 | 18080 | 0.0167 |
| 1.0271 | 18090 | 0.0177 |
| 1.0277 | 18100 | 0.0172 |
| 1.0282 | 18110 | 0.0171 |
| 1.0288 | 18120 | 0.0174 |
| 1.0294 | 18130 | 0.0166 |
| 1.0299 | 18140 | 0.0161 |
| 1.0305 | 18150 | 0.0161 |
| 1.0311 | 18160 | 0.0166 |
| 1.0316 | 18170 | 0.0174 |
| 1.0322 | 18180 | 0.017 |
| 1.0328 | 18190 | 0.017 |
| 1.0333 | 18200 | 0.0167 |
| 1.0339 | 18210 | 0.0173 |
| 1.0345 | 18220 | 0.0177 |
| 1.0350 | 18230 | 0.0158 |
| 1.0356 | 18240 | 0.0174 |
| 1.0362 | 18250 | 0.0167 |
| 1.0367 | 18260 | 0.017 |
| 1.0373 | 18270 | 0.0184 |
| 1.0379 | 18280 | 0.0168 |
| 1.0384 | 18290 | 0.0174 |
| 1.0390 | 18300 | 0.0164 |
| 1.0396 | 18310 | 0.0167 |
| 1.0401 | 18320 | 0.017 |
| 1.0407 | 18330 | 0.0176 |
| 1.0413 | 18340 | 0.0168 |
| 1.0418 | 18350 | 0.0172 |
| 1.0424 | 18360 | 0.0169 |
| 1.0430 | 18370 | 0.0168 |
| 1.0435 | 18380 | 0.0178 |
| 1.0441 | 18390 | 0.0174 |
| 1.0447 | 18400 | 0.0177 |
| 1.0453 | 18410 | 0.0154 |
| 1.0458 | 18420 | 0.0174 |
| 1.0464 | 18430 | 0.0165 |
| 1.0470 | 18440 | 0.0168 |
| 1.0475 | 18450 | 0.0175 |
| 1.0481 | 18460 | 0.0175 |
| 1.0487 | 18470 | 0.0161 |
| 1.0492 | 18480 | 0.0176 |
| 1.0498 | 18490 | 0.0168 |
| 1.0504 | 18500 | 0.0171 |
| 1.0509 | 18510 | 0.0166 |
| 1.0515 | 18520 | 0.016 |
| 1.0521 | 18530 | 0.0167 |
| 1.0526 | 18540 | 0.0171 |
| 1.0532 | 18550 | 0.017 |
| 1.0538 | 18560 | 0.0174 |
| 1.0543 | 18570 | 0.0165 |
| 1.0549 | 18580 | 0.0174 |
| 1.0555 | 18590 | 0.0165 |
| 1.0560 | 18600 | 0.0167 |
| 1.0566 | 18610 | 0.0158 |
| 1.0572 | 18620 | 0.0167 |
| 1.0577 | 18630 | 0.0161 |
| 1.0583 | 18640 | 0.0164 |
| 1.0589 | 18650 | 0.017 |
| 1.0594 | 18660 | 0.0168 |
| 1.0600 | 18670 | 0.0164 |
| 1.0606 | 18680 | 0.0166 |
| 1.0611 | 18690 | 0.0159 |
| 1.0617 | 18700 | 0.0174 |
| 1.0623 | 18710 | 0.0174 |
| 1.0629 | 18720 | 0.0179 |
| 1.0634 | 18730 | 0.0183 |
| 1.0640 | 18740 | 0.0173 |
| 1.0646 | 18750 | 0.0164 |
| 1.0651 | 18760 | 0.0169 |
| 1.0657 | 18770 | 0.0172 |
| 1.0663 | 18780 | 0.0166 |
| 1.0668 | 18790 | 0.0172 |
| 1.0674 | 18800 | 0.0166 |
| 1.0680 | 18810 | 0.0166 |
| 1.0685 | 18820 | 0.0166 |
| 1.0691 | 18830 | 0.0171 |
| 1.0697 | 18840 | 0.0177 |
| 1.0702 | 18850 | 0.0172 |
| 1.0708 | 18860 | 0.0163 |
| 1.0714 | 18870 | 0.0177 |
| 1.0719 | 18880 | 0.0168 |
| 1.0725 | 18890 | 0.0168 |
| 1.0731 | 18900 | 0.0163 |
| 1.0736 | 18910 | 0.0162 |
| 1.0742 | 18920 | 0.0172 |
| 1.0748 | 18930 | 0.0178 |
| 1.0753 | 18940 | 0.0171 |
| 1.0759 | 18950 | 0.0167 |
| 1.0765 | 18960 | 0.0168 |
| 1.0770 | 18970 | 0.0165 |
| 1.0776 | 18980 | 0.0168 |
| 1.0782 | 18990 | 0.0163 |
| 1.0787 | 19000 | 0.0171 |
| 1.0793 | 19010 | 0.0169 |
| 1.0799 | 19020 | 0.0166 |
| 1.0805 | 19030 | 0.0166 |
| 1.0810 | 19040 | 0.0175 |
| 1.0816 | 19050 | 0.0167 |
| 1.0822 | 19060 | 0.0166 |
| 1.0827 | 19070 | 0.017 |
| 1.0833 | 19080 | 0.017 |
| 1.0839 | 19090 | 0.0171 |
| 1.0844 | 19100 | 0.0171 |
| 1.0850 | 19110 | 0.0165 |
| 1.0856 | 19120 | 0.017 |
| 1.0861 | 19130 | 0.017 |
| 1.0867 | 19140 | 0.016 |
| 1.0873 | 19150 | 0.0165 |
| 1.0878 | 19160 | 0.0161 |
| 1.0884 | 19170 | 0.0176 |
| 1.0890 | 19180 | 0.0175 |
| 1.0895 | 19190 | 0.0171 |
| 1.0901 | 19200 | 0.0185 |
| 1.0907 | 19210 | 0.0168 |
| 1.0912 | 19220 | 0.0169 |
| 1.0918 | 19230 | 0.0164 |
| 1.0924 | 19240 | 0.0171 |
| 1.0929 | 19250 | 0.017 |
| 1.0935 | 19260 | 0.0166 |
| 1.0941 | 19270 | 0.0166 |
| 1.0946 | 19280 | 0.0175 |
| 1.0952 | 19290 | 0.0171 |
| 1.0958 | 19300 | 0.016 |
| 1.0963 | 19310 | 0.0168 |
| 1.0969 | 19320 | 0.0159 |
| 1.0975 | 19330 | 0.0165 |
| 1.0981 | 19340 | 0.0167 |
| 1.0986 | 19350 | 0.0173 |
| 1.0992 | 19360 | 0.0162 |
| 1.0998 | 19370 | 0.0177 |
| 1.1003 | 19380 | 0.0173 |
| 1.1009 | 19390 | 0.0167 |
| 1.1015 | 19400 | 0.017 |
| 1.1020 | 19410 | 0.0172 |
| 1.1026 | 19420 | 0.0164 |
| 1.1032 | 19430 | 0.017 |
| 1.1037 | 19440 | 0.0163 |
| 1.1043 | 19450 | 0.016 |
| 1.1049 | 19460 | 0.0169 |
| 1.1054 | 19470 | 0.0164 |
| 1.1060 | 19480 | 0.0169 |
| 1.1066 | 19490 | 0.0164 |
| 1.1071 | 19500 | 0.0171 |
| 1.1077 | 19510 | 0.0165 |
| 1.1083 | 19520 | 0.0167 |
| 1.1088 | 19530 | 0.0166 |
| 1.1094 | 19540 | 0.0174 |
| 1.1100 | 19550 | 0.0175 |
| 1.1105 | 19560 | 0.0164 |
| 1.1111 | 19570 | 0.0165 |
| 1.1117 | 19580 | 0.0166 |
| 1.1122 | 19590 | 0.0169 |
| 1.1128 | 19600 | 0.0176 |
| 1.1134 | 19610 | 0.0182 |
| 1.1139 | 19620 | 0.0171 |
| 1.1145 | 19630 | 0.0165 |
| 1.1151 | 19640 | 0.0174 |
| 1.1157 | 19650 | 0.0166 |
| 1.1162 | 19660 | 0.0163 |
| 1.1168 | 19670 | 0.0179 |
| 1.1174 | 19680 | 0.0165 |
| 1.1179 | 19690 | 0.0172 |
| 1.1185 | 19700 | 0.017 |
| 1.1191 | 19710 | 0.0166 |
| 1.1196 | 19720 | 0.0166 |
| 1.1202 | 19730 | 0.0173 |
| 1.1208 | 19740 | 0.016 |
| 1.1213 | 19750 | 0.0173 |
| 1.1219 | 19760 | 0.0161 |
| 1.1225 | 19770 | 0.0171 |
| 1.1230 | 19780 | 0.0167 |
| 1.1236 | 19790 | 0.0162 |
| 1.1242 | 19800 | 0.0173 |
| 1.1247 | 19810 | 0.0168 |
| 1.1253 | 19820 | 0.0174 |
| 1.1259 | 19830 | 0.0174 |
| 1.1264 | 19840 | 0.0175 |
| 1.1270 | 19850 | 0.017 |
| 1.1276 | 19860 | 0.0172 |
| 1.1281 | 19870 | 0.0168 |
| 1.1287 | 19880 | 0.0172 |
| 1.1293 | 19890 | 0.0163 |
| 1.1298 | 19900 | 0.0163 |
| 1.1304 | 19910 | 0.0159 |
| 1.1310 | 19920 | 0.0165 |
| 1.1316 | 19930 | 0.0165 |
| 1.1321 | 19940 | 0.0167 |
| 1.1327 | 19950 | 0.0168 |
| 1.1333 | 19960 | 0.0166 |
| 1.1338 | 19970 | 0.0165 |
| 1.1344 | 19980 | 0.0173 |
| 1.1350 | 19990 | 0.0172 |
| 1.1355 | 20000 | 0.0161 |
| 1.1361 | 20010 | 0.0164 |
| 1.1367 | 20020 | 0.0166 |
| 1.1372 | 20030 | 0.0172 |
| 1.1378 | 20040 | 0.0172 |
| 1.1384 | 20050 | 0.0156 |
| 1.1389 | 20060 | 0.0176 |
| 1.1395 | 20070 | 0.016 |
| 1.1401 | 20080 | 0.017 |
| 1.1406 | 20090 | 0.0166 |
| 1.1412 | 20100 | 0.0169 |
| 1.1418 | 20110 | 0.018 |
| 1.1423 | 20120 | 0.0168 |
| 1.1429 | 20130 | 0.0168 |
| 1.1435 | 20140 | 0.0174 |
| 1.1440 | 20150 | 0.0172 |
| 1.1446 | 20160 | 0.0167 |
| 1.1452 | 20170 | 0.0167 |
| 1.1457 | 20180 | 0.0168 |
| 1.1463 | 20190 | 0.0168 |
| 1.1469 | 20200 | 0.0164 |
| 1.1474 | 20210 | 0.0167 |
| 1.1480 | 20220 | 0.0174 |
| 1.1486 | 20230 | 0.0172 |
| 1.1492 | 20240 | 0.0178 |
| 1.1497 | 20250 | 0.0171 |
| 1.1503 | 20260 | 0.016 |
| 1.1509 | 20270 | 0.0172 |
| 1.1514 | 20280 | 0.0174 |
| 1.1520 | 20290 | 0.0164 |
| 1.1526 | 20300 | 0.0155 |
| 1.1531 | 20310 | 0.0176 |
| 1.1537 | 20320 | 0.016 |
| 1.1543 | 20330 | 0.0168 |
| 1.1548 | 20340 | 0.0163 |
| 1.1554 | 20350 | 0.0174 |
| 1.1560 | 20360 | 0.0166 |
| 1.1565 | 20370 | 0.0161 |
| 1.1571 | 20380 | 0.0163 |
| 1.1577 | 20390 | 0.0169 |
| 1.1582 | 20400 | 0.0166 |
| 1.1588 | 20410 | 0.0166 |
| 1.1594 | 20420 | 0.0168 |
| 1.1599 | 20430 | 0.0159 |
| 1.1605 | 20440 | 0.0165 |
| 1.1611 | 20450 | 0.0173 |
| 1.1616 | 20460 | 0.0168 |
| 1.1622 | 20470 | 0.018 |
| 1.1628 | 20480 | 0.0169 |
| 1.1633 | 20490 | 0.0166 |
| 1.1639 | 20500 | 0.0176 |
| 1.1645 | 20510 | 0.0167 |
| 1.1650 | 20520 | 0.0171 |
| 1.1656 | 20530 | 0.0173 |
| 1.1662 | 20540 | 0.0175 |
| 1.1668 | 20550 | 0.0165 |
| 1.1673 | 20560 | 0.0163 |
| 1.1679 | 20570 | 0.0165 |
| 1.1685 | 20580 | 0.0176 |
| 1.1690 | 20590 | 0.0167 |
| 1.1696 | 20600 | 0.017 |
| 1.1702 | 20610 | 0.0167 |
| 1.1707 | 20620 | 0.0163 |
| 1.1713 | 20630 | 0.017 |
| 1.1719 | 20640 | 0.0167 |
| 1.1724 | 20650 | 0.0168 |
| 1.1730 | 20660 | 0.0168 |
| 1.1736 | 20670 | 0.0172 |
| 1.1741 | 20680 | 0.0165 |
| 1.1747 | 20690 | 0.0166 |
| 1.1753 | 20700 | 0.017 |
| 1.1758 | 20710 | 0.0162 |
| 1.1764 | 20720 | 0.0166 |
| 1.1770 | 20730 | 0.0169 |
| 1.1775 | 20740 | 0.016 |
| 1.1781 | 20750 | 0.0162 |
| 1.1787 | 20760 | 0.0166 |
| 1.1792 | 20770 | 0.0169 |
| 1.1798 | 20780 | 0.0168 |
| 1.1804 | 20790 | 0.0163 |
| 1.1809 | 20800 | 0.0164 |
| 1.1815 | 20810 | 0.0164 |
| 1.1821 | 20820 | 0.0171 |
| 1.1826 | 20830 | 0.0167 |
| 1.1832 | 20840 | 0.0155 |
| 1.1838 | 20850 | 0.0163 |
| 1.1844 | 20860 | 0.0167 |
| 1.1849 | 20870 | 0.0166 |
| 1.1855 | 20880 | 0.0163 |
| 1.1861 | 20890 | 0.0158 |
| 1.1866 | 20900 | 0.017 |
| 1.1872 | 20910 | 0.0169 |
| 1.1878 | 20920 | 0.017 |
| 1.1883 | 20930 | 0.0164 |
| 1.1889 | 20940 | 0.0155 |
| 1.1895 | 20950 | 0.0177 |
| 1.1900 | 20960 | 0.0163 |
| 1.1906 | 20970 | 0.0175 |
| 1.1912 | 20980 | 0.0168 |
| 1.1917 | 20990 | 0.0171 |
| 1.1923 | 21000 | 0.0159 |
| 1.1929 | 21010 | 0.0167 |
| 1.1934 | 21020 | 0.0166 |
| 1.1940 | 21030 | 0.0169 |
| 1.1946 | 21040 | 0.0166 |
| 1.1951 | 21050 | 0.017 |
| 1.1957 | 21060 | 0.0169 |
| 1.1963 | 21070 | 0.0167 |
| 1.1968 | 21080 | 0.017 |
| 1.1974 | 21090 | 0.016 |
| 1.1980 | 21100 | 0.0157 |
| 1.1985 | 21110 | 0.016 |
| 1.1991 | 21120 | 0.017 |
| 1.1997 | 21130 | 0.0168 |
| 1.2002 | 21140 | 0.0161 |
| 1.2008 | 21150 | 0.0174 |
| 1.2014 | 21160 | 0.0169 |
| 1.2020 | 21170 | 0.0161 |
| 1.2025 | 21180 | 0.0174 |
| 1.2031 | 21190 | 0.0179 |
| 1.2037 | 21200 | 0.0173 |
| 1.2042 | 21210 | 0.0159 |
| 1.2048 | 21220 | 0.0172 |
| 1.2054 | 21230 | 0.017 |
| 1.2059 | 21240 | 0.0164 |
| 1.2065 | 21250 | 0.0171 |
| 1.2071 | 21260 | 0.0159 |
| 1.2076 | 21270 | 0.0175 |
| 1.2082 | 21280 | 0.0169 |
| 1.2088 | 21290 | 0.0175 |
| 1.2093 | 21300 | 0.017 |
| 1.2099 | 21310 | 0.0161 |
| 1.2105 | 21320 | 0.0171 |
| 1.2110 | 21330 | 0.0162 |
| 1.2116 | 21340 | 0.0165 |
| 1.2122 | 21350 | 0.0169 |
| 1.2127 | 21360 | 0.017 |
| 1.2133 | 21370 | 0.0168 |
| 1.2139 | 21380 | 0.0172 |
| 1.2144 | 21390 | 0.0169 |
| 1.2150 | 21400 | 0.0164 |
| 1.2156 | 21410 | 0.0174 |
| 1.2161 | 21420 | 0.0166 |
| 1.2167 | 21430 | 0.0164 |
| 1.2173 | 21440 | 0.0167 |
| 1.2179 | 21450 | 0.0167 |
| 1.2184 | 21460 | 0.0168 |
| 1.2190 | 21470 | 0.0171 |
| 1.2196 | 21480 | 0.0165 |
| 1.2201 | 21490 | 0.0166 |
| 1.2207 | 21500 | 0.0165 |
| 1.2213 | 21510 | 0.0168 |
| 1.2218 | 21520 | 0.0173 |
| 1.2224 | 21530 | 0.0172 |
| 1.2230 | 21540 | 0.0166 |
| 1.2235 | 21550 | 0.0162 |
| 1.2241 | 21560 | 0.0163 |
| 1.2247 | 21570 | 0.0169 |
| 1.2252 | 21580 | 0.0171 |
| 1.2258 | 21590 | 0.0161 |
| 1.2264 | 21600 | 0.017 |
| 1.2269 | 21610 | 0.0171 |
| 1.2275 | 21620 | 0.0174 |
| 1.2281 | 21630 | 0.0168 |
| 1.2286 | 21640 | 0.0169 |
| 1.2292 | 21650 | 0.0165 |
| 1.2298 | 21660 | 0.0164 |
| 1.2303 | 21670 | 0.0168 |
| 1.2309 | 21680 | 0.0162 |
| 1.2315 | 21690 | 0.0168 |
| 1.2320 | 21700 | 0.0164 |
| 1.2326 | 21710 | 0.0163 |
| 1.2332 | 21720 | 0.0167 |
| 1.2337 | 21730 | 0.0165 |
| 1.2343 | 21740 | 0.0168 |
| 1.2349 | 21750 | 0.0169 |
| 1.2355 | 21760 | 0.0162 |
| 1.2360 | 21770 | 0.0168 |
| 1.2366 | 21780 | 0.0159 |
| 1.2372 | 21790 | 0.0172 |
| 1.2377 | 21800 | 0.0165 |
| 1.2383 | 21810 | 0.0167 |
| 1.2389 | 21820 | 0.0168 |
| 1.2394 | 21830 | 0.0171 |
| 1.2400 | 21840 | 0.0157 |
| 1.2406 | 21850 | 0.0166 |
| 1.2411 | 21860 | 0.0169 |
| 1.2417 | 21870 | 0.0168 |
| 1.2423 | 21880 | 0.0172 |
| 1.2428 | 21890 | 0.0161 |
| 1.2434 | 21900 | 0.0172 |
| 1.2440 | 21910 | 0.017 |
| 1.2445 | 21920 | 0.0173 |
| 1.2451 | 21930 | 0.0162 |
| 1.2457 | 21940 | 0.0161 |
| 1.2462 | 21950 | 0.0173 |
| 1.2468 | 21960 | 0.0168 |
| 1.2474 | 21970 | 0.0168 |
| 1.2479 | 21980 | 0.0175 |
| 1.2485 | 21990 | 0.0171 |
| 1.2491 | 22000 | 0.0166 |
| 1.2496 | 22010 | 0.0163 |
| 1.2502 | 22020 | 0.0168 |
| 1.2508 | 22030 | 0.0178 |
| 1.2513 | 22040 | 0.017 |
| 1.2519 | 22050 | 0.0167 |
| 1.2525 | 22060 | 0.0165 |
| 1.2531 | 22070 | 0.0166 |
| 1.2536 | 22080 | 0.0166 |
| 1.2542 | 22090 | 0.0156 |
| 1.2548 | 22100 | 0.0176 |
| 1.2553 | 22110 | 0.0166 |
| 1.2559 | 22120 | 0.0168 |
| 1.2565 | 22130 | 0.0168 |
| 1.2570 | 22140 | 0.0177 |
| 1.2576 | 22150 | 0.0172 |
| 1.2582 | 22160 | 0.0164 |
| 1.2587 | 22170 | 0.017 |
| 1.2593 | 22180 | 0.0162 |
| 1.2599 | 22190 | 0.0158 |
| 1.2604 | 22200 | 0.0165 |
| 1.2610 | 22210 | 0.0172 |
| 1.2616 | 22220 | 0.0173 |
| 1.2621 | 22230 | 0.0164 |
| 1.2627 | 22240 | 0.0176 |
| 1.2633 | 22250 | 0.0165 |
| 1.2638 | 22260 | 0.0166 |
| 1.2644 | 22270 | 0.0171 |
| 1.2650 | 22280 | 0.016 |
| 1.2655 | 22290 | 0.0159 |
| 1.2661 | 22300 | 0.0172 |
| 1.2667 | 22310 | 0.0173 |
| 1.2672 | 22320 | 0.0171 |
| 1.2678 | 22330 | 0.0177 |
| 1.2684 | 22340 | 0.0167 |
| 1.2689 | 22350 | 0.0165 |
| 1.2695 | 22360 | 0.0165 |
| 1.2701 | 22370 | 0.0171 |
| 1.2707 | 22380 | 0.0163 |
| 1.2712 | 22390 | 0.0165 |
| 1.2718 | 22400 | 0.0173 |
| 1.2724 | 22410 | 0.0156 |
| 1.2729 | 22420 | 0.0172 |
| 1.2735 | 22430 | 0.0169 |
| 1.2741 | 22440 | 0.0176 |
| 1.2746 | 22450 | 0.0161 |
| 1.2752 | 22460 | 0.0156 |
| 1.2758 | 22470 | 0.0173 |
| 1.2763 | 22480 | 0.0172 |
| 1.2769 | 22490 | 0.0162 |
| 1.2775 | 22500 | 0.0161 |
| 1.2780 | 22510 | 0.0158 |
| 1.2786 | 22520 | 0.0174 |
| 1.2792 | 22530 | 0.016 |
| 1.2797 | 22540 | 0.0165 |
| 1.2803 | 22550 | 0.017 |
| 1.2809 | 22560 | 0.017 |
| 1.2814 | 22570 | 0.0162 |
| 1.2820 | 22580 | 0.0162 |
| 1.2826 | 22590 | 0.0176 |
| 1.2831 | 22600 | 0.017 |
| 1.2837 | 22610 | 0.0158 |
| 1.2843 | 22620 | 0.0168 |
| 1.2848 | 22630 | 0.0171 |
| 1.2854 | 22640 | 0.0169 |
| 1.2860 | 22650 | 0.0174 |
| 1.2865 | 22660 | 0.0163 |
| 1.2871 | 22670 | 0.0173 |
| 1.2877 | 22680 | 0.0174 |
| 1.2883 | 22690 | 0.0176 |
| 1.2888 | 22700 | 0.0169 |
| 1.2894 | 22710 | 0.0175 |
| 1.2900 | 22720 | 0.0167 |
| 1.2905 | 22730 | 0.017 |
| 1.2911 | 22740 | 0.0173 |
| 1.2917 | 22750 | 0.0155 |
| 1.2922 | 22760 | 0.0169 |
| 1.2928 | 22770 | 0.0166 |
| 1.2934 | 22780 | 0.0156 |
| 1.2939 | 22790 | 0.0171 |
| 1.2945 | 22800 | 0.017 |
| 1.2951 | 22810 | 0.0163 |
| 1.2956 | 22820 | 0.0173 |
| 1.2962 | 22830 | 0.0166 |
| 1.2968 | 22840 | 0.0167 |
| 1.2973 | 22850 | 0.017 |
| 1.2979 | 22860 | 0.0166 |
| 1.2985 | 22870 | 0.0159 |
| 1.2990 | 22880 | 0.0169 |
| 1.2996 | 22890 | 0.0166 |
| 1.3002 | 22900 | 0.016 |
| 1.3007 | 22910 | 0.0166 |
| 1.3013 | 22920 | 0.0167 |
| 1.3019 | 22930 | 0.0161 |
| 1.3024 | 22940 | 0.0172 |
| 1.3030 | 22950 | 0.0159 |
| 1.3036 | 22960 | 0.0167 |
| 1.3042 | 22970 | 0.0167 |
| 1.3047 | 22980 | 0.0165 |
| 1.3053 | 22990 | 0.017 |
| 1.3059 | 23000 | 0.0168 |
| 1.3064 | 23010 | 0.0176 |
| 1.3070 | 23020 | 0.0169 |
| 1.3076 | 23030 | 0.0165 |
| 1.3081 | 23040 | 0.0158 |
| 1.3087 | 23050 | 0.0164 |
| 1.3093 | 23060 | 0.017 |
| 1.3098 | 23070 | 0.0165 |
| 1.3104 | 23080 | 0.0167 |
| 1.3110 | 23090 | 0.0164 |
| 1.3115 | 23100 | 0.0157 |
| 1.3121 | 23110 | 0.0169 |
| 1.3127 | 23120 | 0.0168 |
| 1.3132 | 23130 | 0.0169 |
| 1.3138 | 23140 | 0.017 |
| 1.3144 | 23150 | 0.0172 |
| 1.3149 | 23160 | 0.0172 |
| 1.3155 | 23170 | 0.0166 |
| 1.3161 | 23180 | 0.0175 |
| 1.3166 | 23190 | 0.0172 |
| 1.3172 | 23200 | 0.0163 |
| 1.3178 | 23210 | 0.0163 |
| 1.3183 | 23220 | 0.0167 |
| 1.3189 | 23230 | 0.0165 |
| 1.3195 | 23240 | 0.0165 |
| 1.3200 | 23250 | 0.0161 |
| 1.3206 | 23260 | 0.016 |
| 1.3212 | 23270 | 0.016 |
| 1.3218 | 23280 | 0.0168 |
| 1.3223 | 23290 | 0.0171 |
| 1.3229 | 23300 | 0.0171 |
| 1.3235 | 23310 | 0.0163 |
| 1.3240 | 23320 | 0.0162 |
| 1.3246 | 23330 | 0.017 |
| 1.3252 | 23340 | 0.0174 |
| 1.3257 | 23350 | 0.0161 |
| 1.3263 | 23360 | 0.0171 |
| 1.3269 | 23370 | 0.016 |
| 1.3274 | 23380 | 0.0169 |
| 1.3280 | 23390 | 0.0154 |
| 1.3286 | 23400 | 0.0184 |
| 1.3291 | 23410 | 0.0164 |
| 1.3297 | 23420 | 0.0168 |
| 1.3303 | 23430 | 0.0167 |
| 1.3308 | 23440 | 0.0162 |
| 1.3314 | 23450 | 0.0155 |
| 1.3320 | 23460 | 0.0154 |
| 1.3325 | 23470 | 0.0165 |
| 1.3331 | 23480 | 0.0164 |
| 1.3337 | 23490 | 0.0157 |
| 1.3342 | 23500 | 0.0161 |
| 1.3348 | 23510 | 0.0167 |
| 1.3354 | 23520 | 0.017 |
| 1.3359 | 23530 | 0.0169 |
| 1.3365 | 23540 | 0.0167 |
| 1.3371 | 23550 | 0.0162 |
| 1.3376 | 23560 | 0.0162 |
| 1.3382 | 23570 | 0.0166 |
| 1.3388 | 23580 | 0.0166 |
| 1.3394 | 23590 | 0.0165 |
| 1.3399 | 23600 | 0.0162 |
| 1.3405 | 23610 | 0.0164 |
| 1.3411 | 23620 | 0.0173 |
| 1.3416 | 23630 | 0.0158 |
| 1.3422 | 23640 | 0.0163 |
| 1.3428 | 23650 | 0.0169 |
| 1.3433 | 23660 | 0.0168 |
| 1.3439 | 23670 | 0.0167 |
| 1.3445 | 23680 | 0.0168 |
| 1.3450 | 23690 | 0.0169 |
| 1.3456 | 23700 | 0.0153 |
| 1.3462 | 23710 | 0.0157 |
| 1.3467 | 23720 | 0.0171 |
| 1.3473 | 23730 | 0.0168 |
| 1.3479 | 23740 | 0.0161 |
| 1.3484 | 23750 | 0.0162 |
| 1.3490 | 23760 | 0.0166 |
| 1.3496 | 23770 | 0.017 |
| 1.3501 | 23780 | 0.0159 |
| 1.3507 | 23790 | 0.0169 |
| 1.3513 | 23800 | 0.0163 |
| 1.3518 | 23810 | 0.0166 |
| 1.3524 | 23820 | 0.0165 |
| 1.3530 | 23830 | 0.0157 |
| 1.3535 | 23840 | 0.0152 |
| 1.3541 | 23850 | 0.0161 |
| 1.3547 | 23860 | 0.0165 |
| 1.3552 | 23870 | 0.017 |
| 1.3558 | 23880 | 0.017 |
| 1.3564 | 23890 | 0.0162 |
| 1.3570 | 23900 | 0.0161 |
| 1.3575 | 23910 | 0.0173 |
| 1.3581 | 23920 | 0.0164 |
| 1.3587 | 23930 | 0.017 |
| 1.3592 | 23940 | 0.0173 |
| 1.3598 | 23950 | 0.0179 |
| 1.3604 | 23960 | 0.0166 |
| 1.3609 | 23970 | 0.0177 |
| 1.3615 | 23980 | 0.0163 |
| 1.3621 | 23990 | 0.0161 |
| 1.3626 | 24000 | 0.0172 |
| 1.3632 | 24010 | 0.0172 |
| 1.3638 | 24020 | 0.0163 |
| 1.3643 | 24030 | 0.0176 |
| 1.3649 | 24040 | 0.0159 |
| 1.3655 | 24050 | 0.0161 |
| 1.3660 | 24060 | 0.0168 |
| 1.3666 | 24070 | 0.0172 |
| 1.3672 | 24080 | 0.0172 |
| 1.3677 | 24090 | 0.0161 |
| 1.3683 | 24100 | 0.0171 |
| 1.3689 | 24110 | 0.0159 |
| 1.3694 | 24120 | 0.0168 |
| 1.3700 | 24130 | 0.0164 |
| 1.3706 | 24140 | 0.0166 |
| 1.3711 | 24150 | 0.0161 |
| 1.3717 | 24160 | 0.0154 |
| 1.3723 | 24170 | 0.0165 |
| 1.3728 | 24180 | 0.0168 |
| 1.3734 | 24190 | 0.0171 |
| 1.3740 | 24200 | 0.0166 |
| 1.3746 | 24210 | 0.0163 |
| 1.3751 | 24220 | 0.0166 |
| 1.3757 | 24230 | 0.0166 |
| 1.3763 | 24240 | 0.016 |
| 1.3768 | 24250 | 0.0166 |
| 1.3774 | 24260 | 0.0165 |
| 1.3780 | 24270 | 0.0161 |
| 1.3785 | 24280 | 0.0164 |
| 1.3791 | 24290 | 0.0155 |
| 1.3797 | 24300 | 0.0166 |
| 1.3802 | 24310 | 0.0158 |
| 1.3808 | 24320 | 0.0171 |
| 1.3814 | 24330 | 0.0177 |
| 1.3819 | 24340 | 0.0165 |
| 1.3825 | 24350 | 0.0169 |
| 1.3831 | 24360 | 0.0158 |
| 1.3836 | 24370 | 0.0167 |
| 1.3842 | 24380 | 0.0165 |
| 1.3848 | 24390 | 0.0161 |
| 1.3853 | 24400 | 0.0174 |
| 1.3859 | 24410 | 0.0175 |
| 1.3865 | 24420 | 0.0166 |
| 1.3870 | 24430 | 0.0167 |
| 1.3876 | 24440 | 0.0168 |
| 1.3882 | 24450 | 0.0164 |
| 1.3887 | 24460 | 0.016 |
| 1.3893 | 24470 | 0.0162 |
| 1.3899 | 24480 | 0.0168 |
| 1.3905 | 24490 | 0.0156 |
| 1.3910 | 24500 | 0.016 |
| 1.3916 | 24510 | 0.0161 |
| 1.3922 | 24520 | 0.0164 |
| 1.3927 | 24530 | 0.0171 |
| 1.3933 | 24540 | 0.0155 |
| 1.3939 | 24550 | 0.0168 |
| 1.3944 | 24560 | 0.0167 |
| 1.3950 | 24570 | 0.0158 |
| 1.3956 | 24580 | 0.0161 |
| 1.3961 | 24590 | 0.0163 |
| 1.3967 | 24600 | 0.0166 |
| 1.3973 | 24610 | 0.0168 |
| 1.3978 | 24620 | 0.0159 |
| 1.3984 | 24630 | 0.0172 |
| 1.3990 | 24640 | 0.0165 |
| 1.3995 | 24650 | 0.0158 |
| 1.4001 | 24660 | 0.0162 |
| 1.4007 | 24670 | 0.0159 |
| 1.4012 | 24680 | 0.0164 |
| 1.4018 | 24690 | 0.0165 |
| 1.4024 | 24700 | 0.0166 |
| 1.4029 | 24710 | 0.0163 |
| 1.4035 | 24720 | 0.0163 |
| 1.4041 | 24730 | 0.0167 |
| 1.4046 | 24740 | 0.0164 |
| 1.4052 | 24750 | 0.0171 |
| 1.4058 | 24760 | 0.0163 |
| 1.4063 | 24770 | 0.0161 |
| 1.4069 | 24780 | 0.0165 |
| 1.4075 | 24790 | 0.0155 |
| 1.4081 | 24800 | 0.0167 |
| 1.4086 | 24810 | 0.0173 |
| 1.4092 | 24820 | 0.0177 |
| 1.4098 | 24830 | 0.0168 |
| 1.4103 | 24840 | 0.0166 |
| 1.4109 | 24850 | 0.017 |
| 1.4115 | 24860 | 0.0161 |
| 1.4120 | 24870 | 0.0166 |
| 1.4126 | 24880 | 0.0165 |
| 1.4132 | 24890 | 0.0168 |
| 1.4137 | 24900 | 0.0163 |
| 1.4143 | 24910 | 0.016 |
| 1.4149 | 24920 | 0.0166 |
| 1.4154 | 24930 | 0.0169 |
| 1.4160 | 24940 | 0.0164 |
| 1.4166 | 24950 | 0.0164 |
| 1.4171 | 24960 | 0.0158 |
| 1.4177 | 24970 | 0.0164 |
| 1.4183 | 24980 | 0.0165 |
| 1.4188 | 24990 | 0.0161 |
| 1.4194 | 25000 | 0.0158 |
| 1.4200 | 25010 | 0.0165 |
| 1.4205 | 25020 | 0.0161 |
| 1.4211 | 25030 | 0.0152 |
| 1.4217 | 25040 | 0.0166 |
| 1.4222 | 25050 | 0.0163 |
| 1.4228 | 25060 | 0.0162 |
| 1.4234 | 25070 | 0.0155 |
| 1.4239 | 25080 | 0.0167 |
| 1.4245 | 25090 | 0.0163 |
| 1.4251 | 25100 | 0.0163 |
| 1.4257 | 25110 | 0.0163 |
| 1.4262 | 25120 | 0.0165 |
| 1.4268 | 25130 | 0.0171 |
| 1.4274 | 25140 | 0.0173 |
| 1.4279 | 25150 | 0.0165 |
| 1.4285 | 25160 | 0.0172 |
| 1.4291 | 25170 | 0.017 |
| 1.4296 | 25180 | 0.0166 |
| 1.4302 | 25190 | 0.0165 |
| 1.4308 | 25200 | 0.0157 |
| 1.4313 | 25210 | 0.0158 |
| 1.4319 | 25220 | 0.0162 |
| 1.4325 | 25230 | 0.016 |
| 1.4330 | 25240 | 0.0164 |
| 1.4336 | 25250 | 0.0175 |
| 1.4342 | 25260 | 0.0166 |
| 1.4347 | 25270 | 0.017 |
| 1.4353 | 25280 | 0.0153 |
| 1.4359 | 25290 | 0.0165 |
| 1.4364 | 25300 | 0.0161 |
| 1.4370 | 25310 | 0.0174 |
| 1.4376 | 25320 | 0.0161 |
| 1.4381 | 25330 | 0.0156 |
| 1.4387 | 25340 | 0.017 |
| 1.4393 | 25350 | 0.0159 |
| 1.4398 | 25360 | 0.0168 |
| 1.4404 | 25370 | 0.017 |
| 1.4410 | 25380 | 0.0169 |
| 1.4415 | 25390 | 0.0162 |
| 1.4421 | 25400 | 0.0169 |
| 1.4427 | 25410 | 0.0172 |
| 1.4433 | 25420 | 0.017 |
| 1.4438 | 25430 | 0.0171 |
| 1.4444 | 25440 | 0.0164 |
| 1.4450 | 25450 | 0.0175 |
| 1.4455 | 25460 | 0.0164 |
| 1.4461 | 25470 | 0.0161 |
| 1.4467 | 25480 | 0.0171 |
| 1.4472 | 25490 | 0.0156 |
| 1.4478 | 25500 | 0.0166 |
| 1.4484 | 25510 | 0.0172 |
| 1.4489 | 25520 | 0.0163 |
| 1.4495 | 25530 | 0.0168 |
| 1.4501 | 25540 | 0.0167 |
| 1.4506 | 25550 | 0.0171 |
| 1.4512 | 25560 | 0.0171 |
| 1.4518 | 25570 | 0.0169 |
| 1.4523 | 25580 | 0.0158 |
| 1.4529 | 25590 | 0.0168 |
| 1.4535 | 25600 | 0.0174 |
| 1.4540 | 25610 | 0.0165 |
| 1.4546 | 25620 | 0.0158 |
| 1.4552 | 25630 | 0.0163 |
| 1.4557 | 25640 | 0.0165 |
| 1.4563 | 25650 | 0.0173 |
| 1.4569 | 25660 | 0.0167 |
| 1.4574 | 25670 | 0.0171 |
| 1.4580 | 25680 | 0.0173 |
| 1.4586 | 25690 | 0.0163 |
| 1.4591 | 25700 | 0.0174 |
| 1.4597 | 25710 | 0.0174 |
| 1.4603 | 25720 | 0.0171 |
| 1.4609 | 25730 | 0.0159 |
| 1.4614 | 25740 | 0.0156 |
| 1.4620 | 25750 | 0.0166 |
| 1.4626 | 25760 | 0.0168 |
| 1.4631 | 25770 | 0.017 |
| 1.4637 | 25780 | 0.0165 |
| 1.4643 | 25790 | 0.0166 |
| 1.4648 | 25800 | 0.0163 |
| 1.4654 | 25810 | 0.0164 |
| 1.4660 | 25820 | 0.0171 |
| 1.4665 | 25830 | 0.0163 |
| 1.4671 | 25840 | 0.0164 |
| 1.4677 | 25850 | 0.0162 |
| 1.4682 | 25860 | 0.0167 |
| 1.4688 | 25870 | 0.0171 |
| 1.4694 | 25880 | 0.0149 |
| 1.4699 | 25890 | 0.0156 |
| 1.4705 | 25900 | 0.0158 |
| 1.4711 | 25910 | 0.0176 |
| 1.4716 | 25920 | 0.0163 |
| 1.4722 | 25930 | 0.0166 |
| 1.4728 | 25940 | 0.0163 |
| 1.4733 | 25950 | 0.0169 |
| 1.4739 | 25960 | 0.0164 |
| 1.4745 | 25970 | 0.0174 |
| 1.4750 | 25980 | 0.0159 |
| 1.4756 | 25990 | 0.0161 |
| 1.4762 | 26000 | 0.0161 |
| 1.4768 | 26010 | 0.0163 |
| 1.4773 | 26020 | 0.0161 |
| 1.4779 | 26030 | 0.0167 |
| 1.4785 | 26040 | 0.016 |
| 1.4790 | 26050 | 0.016 |
| 1.4796 | 26060 | 0.0169 |
| 1.4802 | 26070 | 0.016 |
| 1.4807 | 26080 | 0.0167 |
| 1.4813 | 26090 | 0.0171 |
| 1.4819 | 26100 | 0.017 |
| 1.4824 | 26110 | 0.017 |
| 1.4830 | 26120 | 0.0161 |
| 1.4836 | 26130 | 0.016 |
| 1.4841 | 26140 | 0.0167 |
| 1.4847 | 26150 | 0.0173 |
| 1.4853 | 26160 | 0.0167 |
| 1.4858 | 26170 | 0.0159 |
| 1.4864 | 26180 | 0.0164 |
| 1.4870 | 26190 | 0.016 |
| 1.4875 | 26200 | 0.0161 |
| 1.4881 | 26210 | 0.0148 |
| 1.4887 | 26220 | 0.016 |
| 1.4892 | 26230 | 0.0166 |
| 1.4898 | 26240 | 0.0164 |
| 1.4904 | 26250 | 0.0164 |
| 1.4909 | 26260 | 0.0165 |
| 1.4915 | 26270 | 0.0155 |
| 1.4921 | 26280 | 0.017 |
| 1.4926 | 26290 | 0.0165 |
| 1.4932 | 26300 | 0.0168 |
| 1.4938 | 26310 | 0.0168 |
| 1.4944 | 26320 | 0.0173 |
| 1.4949 | 26330 | 0.0161 |
| 1.4955 | 26340 | 0.0159 |
| 1.4961 | 26350 | 0.0169 |
| 1.4966 | 26360 | 0.017 |
| 1.4972 | 26370 | 0.0164 |
| 1.4978 | 26380 | 0.017 |
| 1.4983 | 26390 | 0.017 |
| 1.4989 | 26400 | 0.0158 |
| 1.4995 | 26410 | 0.0164 |
| 1.5000 | 26420 | 0.0159 |
| 1.5006 | 26430 | 0.0157 |
| 1.5012 | 26440 | 0.0166 |
| 1.5017 | 26450 | 0.0168 |
| 1.5023 | 26460 | 0.0161 |
| 1.5029 | 26470 | 0.0162 |
| 1.5034 | 26480 | 0.016 |
| 1.5040 | 26490 | 0.0158 |
| 1.5046 | 26500 | 0.0169 |
| 1.5051 | 26510 | 0.0165 |
| 1.5057 | 26520 | 0.0168 |
| 1.5063 | 26530 | 0.0167 |
| 1.5068 | 26540 | 0.0157 |
| 1.5074 | 26550 | 0.0168 |
| 1.5080 | 26560 | 0.0162 |
| 1.5085 | 26570 | 0.0166 |
| 1.5091 | 26580 | 0.0163 |
| 1.5097 | 26590 | 0.0155 |
| 1.5102 | 26600 | 0.0166 |
| 1.5108 | 26610 | 0.0171 |
| 1.5114 | 26620 | 0.0156 |
| 1.5120 | 26630 | 0.0163 |
| 1.5125 | 26640 | 0.0163 |
| 1.5131 | 26650 | 0.0161 |
| 1.5137 | 26660 | 0.0158 |
| 1.5142 | 26670 | 0.0159 |
| 1.5148 | 26680 | 0.0167 |
| 1.5154 | 26690 | 0.0172 |
| 1.5159 | 26700 | 0.0158 |
| 1.5165 | 26710 | 0.0173 |
| 1.5171 | 26720 | 0.0168 |
| 1.5176 | 26730 | 0.0168 |
| 1.5182 | 26740 | 0.0164 |
| 1.5188 | 26750 | 0.017 |
| 1.5193 | 26760 | 0.016 |
| 1.5199 | 26770 | 0.0161 |
| 1.5205 | 26780 | 0.0167 |
| 1.5210 | 26790 | 0.016 |
| 1.5216 | 26800 | 0.0165 |
| 1.5222 | 26810 | 0.016 |
| 1.5227 | 26820 | 0.017 |
| 1.5233 | 26830 | 0.0168 |
| 1.5239 | 26840 | 0.0168 |
| 1.5244 | 26850 | 0.0163 |
| 1.5250 | 26860 | 0.0168 |
| 1.5256 | 26870 | 0.0165 |
| 1.5261 | 26880 | 0.0158 |
| 1.5267 | 26890 | 0.016 |
| 1.5273 | 26900 | 0.0161 |
| 1.5278 | 26910 | 0.0163 |
| 1.5284 | 26920 | 0.0158 |
| 1.5290 | 26930 | 0.0165 |
| 1.5296 | 26940 | 0.0164 |
| 1.5301 | 26950 | 0.0174 |
| 1.5307 | 26960 | 0.0152 |
| 1.5313 | 26970 | 0.0155 |
| 1.5318 | 26980 | 0.0165 |
| 1.5324 | 26990 | 0.0161 |
| 1.5330 | 27000 | 0.0159 |
| 1.5335 | 27010 | 0.0169 |
| 1.5341 | 27020 | 0.016 |
| 1.5347 | 27030 | 0.0162 |
| 1.5352 | 27040 | 0.0155 |
| 1.5358 | 27050 | 0.0158 |
| 1.5364 | 27060 | 0.0164 |
| 1.5369 | 27070 | 0.0159 |
| 1.5375 | 27080 | 0.0162 |
| 1.5381 | 27090 | 0.0162 |
| 1.5386 | 27100 | 0.0162 |
| 1.5392 | 27110 | 0.0168 |
| 1.5398 | 27120 | 0.0158 |
| 1.5403 | 27130 | 0.0173 |
| 1.5409 | 27140 | 0.016 |
| 1.5415 | 27150 | 0.0181 |
| 1.5420 | 27160 | 0.0167 |
| 1.5426 | 27170 | 0.0165 |
| 1.5432 | 27180 | 0.0161 |
| 1.5437 | 27190 | 0.016 |
| 1.5443 | 27200 | 0.017 |
| 1.5449 | 27210 | 0.0165 |
| 1.5454 | 27220 | 0.0164 |
| 1.5460 | 27230 | 0.0171 |
| 1.5466 | 27240 | 0.0168 |
| 1.5472 | 27250 | 0.0161 |
| 1.5477 | 27260 | 0.017 |
| 1.5483 | 27270 | 0.0163 |
| 1.5489 | 27280 | 0.0173 |
| 1.5494 | 27290 | 0.0158 |
| 1.5500 | 27300 | 0.0169 |
| 1.5506 | 27310 | 0.0153 |
| 1.5511 | 27320 | 0.0166 |
| 1.5517 | 27330 | 0.0155 |
| 1.5523 | 27340 | 0.017 |
| 1.5528 | 27350 | 0.017 |
| 1.5534 | 27360 | 0.0159 |
| 1.5540 | 27370 | 0.0166 |
| 1.5545 | 27380 | 0.0156 |
| 1.5551 | 27390 | 0.0158 |
| 1.5557 | 27400 | 0.0164 |
| 1.5562 | 27410 | 0.0164 |
| 1.5568 | 27420 | 0.0159 |
| 1.5574 | 27430 | 0.0155 |
| 1.5579 | 27440 | 0.017 |
| 1.5585 | 27450 | 0.017 |
| 1.5591 | 27460 | 0.0166 |
| 1.5596 | 27470 | 0.0164 |
| 1.5602 | 27480 | 0.0172 |
| 1.5608 | 27490 | 0.0166 |
| 1.5613 | 27500 | 0.017 |
| 1.5619 | 27510 | 0.0167 |
| 1.5625 | 27520 | 0.0163 |
| 1.5631 | 27530 | 0.0164 |
| 1.5636 | 27540 | 0.0161 |
| 1.5642 | 27550 | 0.0167 |
| 1.5648 | 27560 | 0.0168 |
| 1.5653 | 27570 | 0.0165 |
| 1.5659 | 27580 | 0.0167 |
| 1.5665 | 27590 | 0.016 |
| 1.5670 | 27600 | 0.0158 |
| 1.5676 | 27610 | 0.0161 |
| 1.5682 | 27620 | 0.0164 |
| 1.5687 | 27630 | 0.0161 |
| 1.5693 | 27640 | 0.0173 |
| 1.5699 | 27650 | 0.0161 |
| 1.5704 | 27660 | 0.0177 |
| 1.5710 | 27670 | 0.0168 |
| 1.5716 | 27680 | 0.0167 |
| 1.5721 | 27690 | 0.0163 |
| 1.5727 | 27700 | 0.0161 |
| 1.5733 | 27710 | 0.016 |
| 1.5738 | 27720 | 0.016 |
| 1.5744 | 27730 | 0.0163 |
| 1.5750 | 27740 | 0.0171 |
| 1.5755 | 27750 | 0.0168 |
| 1.5761 | 27760 | 0.0159 |
| 1.5767 | 27770 | 0.0172 |
| 1.5772 | 27780 | 0.0166 |
| 1.5778 | 27790 | 0.0165 |
| 1.5784 | 27800 | 0.0168 |
| 1.5789 | 27810 | 0.0176 |
| 1.5795 | 27820 | 0.0177 |
| 1.5801 | 27830 | 0.016 |
| 1.5807 | 27840 | 0.0162 |
| 1.5812 | 27850 | 0.0162 |
| 1.5818 | 27860 | 0.0162 |
| 1.5824 | 27870 | 0.0164 |
| 1.5829 | 27880 | 0.0173 |
| 1.5835 | 27890 | 0.0168 |
| 1.5841 | 27900 | 0.0166 |
| 1.5846 | 27910 | 0.0162 |
| 1.5852 | 27920 | 0.0158 |
| 1.5858 | 27930 | 0.0154 |
| 1.5863 | 27940 | 0.016 |
| 1.5869 | 27950 | 0.0164 |
| 1.5875 | 27960 | 0.0166 |
| 1.5880 | 27970 | 0.0164 |
| 1.5886 | 27980 | 0.0163 |
| 1.5892 | 27990 | 0.0153 |
| 1.5897 | 28000 | 0.0163 |
| 1.5903 | 28010 | 0.0174 |
| 1.5909 | 28020 | 0.0163 |
| 1.5914 | 28030 | 0.0165 |
| 1.5920 | 28040 | 0.0172 |
| 1.5926 | 28050 | 0.0162 |
| 1.5931 | 28060 | 0.0159 |
| 1.5937 | 28070 | 0.0163 |
| 1.5943 | 28080 | 0.0152 |
| 1.5948 | 28090 | 0.0159 |
| 1.5954 | 28100 | 0.016 |
| 1.5960 | 28110 | 0.0165 |
| 1.5965 | 28120 | 0.0158 |
| 1.5971 | 28130 | 0.0167 |
| 1.5977 | 28140 | 0.0174 |
| 1.5983 | 28150 | 0.0171 |
| 1.5988 | 28160 | 0.0162 |
| 1.5994 | 28170 | 0.0171 |
| 1.6000 | 28180 | 0.0162 |
| 1.6005 | 28190 | 0.0157 |
| 1.6011 | 28200 | 0.0165 |
| 1.6017 | 28210 | 0.0165 |
| 1.6022 | 28220 | 0.0164 |
| 1.6028 | 28230 | 0.0166 |
| 1.6034 | 28240 | 0.0152 |
| 1.6039 | 28250 | 0.0172 |
| 1.6045 | 28260 | 0.0169 |
| 1.6051 | 28270 | 0.0164 |
| 1.6056 | 28280 | 0.0161 |
| 1.6062 | 28290 | 0.0155 |
| 1.6068 | 28300 | 0.0168 |
| 1.6073 | 28310 | 0.0164 |
| 1.6079 | 28320 | 0.0168 |
| 1.6085 | 28330 | 0.0152 |
| 1.6090 | 28340 | 0.0155 |
| 1.6096 | 28350 | 0.015 |
| 1.6102 | 28360 | 0.0166 |
| 1.6107 | 28370 | 0.0169 |
| 1.6113 | 28380 | 0.0149 |
| 1.6119 | 28390 | 0.0159 |
| 1.6124 | 28400 | 0.0168 |
| 1.6130 | 28410 | 0.0158 |
| 1.6136 | 28420 | 0.0172 |
| 1.6141 | 28430 | 0.0169 |
| 1.6147 | 28440 | 0.0164 |
| 1.6153 | 28450 | 0.0162 |
| 1.6159 | 28460 | 0.0169 |
| 1.6164 | 28470 | 0.0164 |
| 1.6170 | 28480 | 0.0155 |
| 1.6176 | 28490 | 0.0158 |
| 1.6181 | 28500 | 0.0156 |
| 1.6187 | 28510 | 0.0154 |
| 1.6193 | 28520 | 0.0162 |
| 1.6198 | 28530 | 0.0167 |
| 1.6204 | 28540 | 0.0172 |
| 1.6210 | 28550 | 0.017 |
| 1.6215 | 28560 | 0.0163 |
| 1.6221 | 28570 | 0.0162 |
| 1.6227 | 28580 | 0.0166 |
| 1.6232 | 28590 | 0.0154 |
| 1.6238 | 28600 | 0.0166 |
| 1.6244 | 28610 | 0.0157 |
| 1.6249 | 28620 | 0.0167 |
| 1.6255 | 28630 | 0.0168 |
| 1.6261 | 28640 | 0.0157 |
| 1.6266 | 28650 | 0.0165 |
| 1.6272 | 28660 | 0.0156 |
| 1.6278 | 28670 | 0.0162 |
| 1.6283 | 28680 | 0.0163 |
| 1.6289 | 28690 | 0.0165 |
| 1.6295 | 28700 | 0.0164 |
| 1.6300 | 28710 | 0.017 |
| 1.6306 | 28720 | 0.0167 |
| 1.6312 | 28730 | 0.0173 |
| 1.6317 | 28740 | 0.0167 |
| 1.6323 | 28750 | 0.0172 |
| 1.6329 | 28760 | 0.0154 |
| 1.6335 | 28770 | 0.0173 |
| 1.6340 | 28780 | 0.0166 |
| 1.6346 | 28790 | 0.0159 |
| 1.6352 | 28800 | 0.0153 |
| 1.6357 | 28810 | 0.0159 |
| 1.6363 | 28820 | 0.0161 |
| 1.6369 | 28830 | 0.0174 |
| 1.6374 | 28840 | 0.0164 |
| 1.6380 | 28850 | 0.0166 |
| 1.6386 | 28860 | 0.0164 |
| 1.6391 | 28870 | 0.016 |
| 1.6397 | 28880 | 0.0164 |
| 1.6403 | 28890 | 0.0165 |
| 1.6408 | 28900 | 0.0159 |
| 1.6414 | 28910 | 0.0159 |
| 1.6420 | 28920 | 0.0153 |
| 1.6425 | 28930 | 0.0159 |
| 1.6431 | 28940 | 0.0164 |
| 1.6437 | 28950 | 0.016 |
| 1.6442 | 28960 | 0.0156 |
| 1.6448 | 28970 | 0.016 |
| 1.6454 | 28980 | 0.016 |
| 1.6459 | 28990 | 0.0165 |
| 1.6465 | 29000 | 0.016 |
| 1.6471 | 29010 | 0.0163 |
| 1.6476 | 29020 | 0.0159 |
| 1.6482 | 29030 | 0.0157 |
| 1.6488 | 29040 | 0.017 |
| 1.6493 | 29050 | 0.016 |
| 1.6499 | 29060 | 0.017 |
| 1.6505 | 29070 | 0.0159 |
| 1.6511 | 29080 | 0.0159 |
| 1.6516 | 29090 | 0.0168 |
| 1.6522 | 29100 | 0.0168 |
| 1.6528 | 29110 | 0.0165 |
| 1.6533 | 29120 | 0.0162 |
| 1.6539 | 29130 | 0.017 |
| 1.6545 | 29140 | 0.0163 |
| 1.6550 | 29150 | 0.016 |
| 1.6556 | 29160 | 0.0163 |
| 1.6562 | 29170 | 0.0165 |
| 1.6567 | 29180 | 0.0164 |
| 1.6573 | 29190 | 0.0156 |
| 1.6579 | 29200 | 0.0162 |
| 1.6584 | 29210 | 0.0164 |
| 1.6590 | 29220 | 0.0153 |
| 1.6596 | 29230 | 0.0174 |
| 1.6601 | 29240 | 0.0171 |
| 1.6607 | 29250 | 0.0156 |
| 1.6613 | 29260 | 0.0163 |
| 1.6618 | 29270 | 0.0168 |
| 1.6624 | 29280 | 0.0161 |
| 1.6630 | 29290 | 0.0165 |
| 1.6635 | 29300 | 0.0165 |
| 1.6641 | 29310 | 0.0173 |
| 1.6647 | 29320 | 0.0164 |
| 1.6652 | 29330 | 0.0157 |
| 1.6658 | 29340 | 0.0154 |
| 1.6664 | 29350 | 0.0167 |
| 1.6670 | 29360 | 0.016 |
| 1.6675 | 29370 | 0.0169 |
| 1.6681 | 29380 | 0.0165 |
| 1.6687 | 29390 | 0.0167 |
| 1.6692 | 29400 | 0.0158 |
| 1.6698 | 29410 | 0.0159 |
| 1.6704 | 29420 | 0.0164 |
| 1.6709 | 29430 | 0.0166 |
| 1.6715 | 29440 | 0.017 |
| 1.6721 | 29450 | 0.0163 |
| 1.6726 | 29460 | 0.0165 |
| 1.6732 | 29470 | 0.0164 |
| 1.6738 | 29480 | 0.0159 |
| 1.6743 | 29490 | 0.0154 |
| 1.6749 | 29500 | 0.0161 |
| 1.6755 | 29510 | 0.0152 |
| 1.6760 | 29520 | 0.0157 |
| 1.6766 | 29530 | 0.0164 |
| 1.6772 | 29540 | 0.0156 |
| 1.6777 | 29550 | 0.0162 |
| 1.6783 | 29560 | 0.0166 |
| 1.6789 | 29570 | 0.0165 |
| 1.6794 | 29580 | 0.016 |
| 1.6800 | 29590 | 0.0162 |
| 1.6806 | 29600 | 0.0163 |
| 1.6811 | 29610 | 0.0157 |
| 1.6817 | 29620 | 0.0163 |
| 1.6823 | 29630 | 0.0163 |
| 1.6828 | 29640 | 0.0165 |
| 1.6834 | 29650 | 0.015 |
| 1.6840 | 29660 | 0.0164 |
| 1.6846 | 29670 | 0.0151 |
| 1.6851 | 29680 | 0.0164 |
| 1.6857 | 29690 | 0.0161 |
| 1.6863 | 29700 | 0.0162 |
| 1.6868 | 29710 | 0.016 |
| 1.6874 | 29720 | 0.0165 |
| 1.6880 | 29730 | 0.0159 |
| 1.6885 | 29740 | 0.0171 |
| 1.6891 | 29750 | 0.0157 |
| 1.6897 | 29760 | 0.0158 |
| 1.6902 | 29770 | 0.0162 |
| 1.6908 | 29780 | 0.0168 |
| 1.6914 | 29790 | 0.0165 |
| 1.6919 | 29800 | 0.0155 |
| 1.6925 | 29810 | 0.0163 |
| 1.6931 | 29820 | 0.0158 |
| 1.6936 | 29830 | 0.0163 |
| 1.6942 | 29840 | 0.0165 |
| 1.6948 | 29850 | 0.0155 |
| 1.6953 | 29860 | 0.0161 |
| 1.6959 | 29870 | 0.0163 |
| 1.6965 | 29880 | 0.0165 |
| 1.6970 | 29890 | 0.0159 |
| 1.6976 | 29900 | 0.0169 |
| 1.6982 | 29910 | 0.0154 |
| 1.6987 | 29920 | 0.0163 |
| 1.6993 | 29930 | 0.0161 |
| 1.6999 | 29940 | 0.0156 |
| 1.7004 | 29950 | 0.0157 |
| 1.7010 | 29960 | 0.016 |
| 1.7016 | 29970 | 0.0165 |
| 1.7022 | 29980 | 0.0161 |
| 1.7027 | 29990 | 0.0166 |
| 1.7033 | 30000 | 0.0152 |
| 1.7039 | 30010 | 0.0164 |
| 1.7044 | 30020 | 0.0156 |
| 1.7050 | 30030 | 0.0158 |
| 1.7056 | 30040 | 0.016 |
| 1.7061 | 30050 | 0.0154 |
| 1.7067 | 30060 | 0.0168 |
| 1.7073 | 30070 | 0.016 |
| 1.7078 | 30080 | 0.0163 |
| 1.7084 | 30090 | 0.0161 |
| 1.7090 | 30100 | 0.0159 |
| 1.7095 | 30110 | 0.0164 |
| 1.7101 | 30120 | 0.0159 |
| 1.7107 | 30130 | 0.0169 |
| 1.7112 | 30140 | 0.016 |
| 1.7118 | 30150 | 0.0157 |
| 1.7124 | 30160 | 0.016 |
| 1.7129 | 30170 | 0.016 |
| 1.7135 | 30180 | 0.0157 |
| 1.7141 | 30190 | 0.0151 |
| 1.7146 | 30200 | 0.0159 |
| 1.7152 | 30210 | 0.0158 |
| 1.7158 | 30220 | 0.0161 |
| 1.7163 | 30230 | 0.016 |
| 1.7169 | 30240 | 0.0172 |
| 1.7175 | 30250 | 0.0164 |
| 1.7180 | 30260 | 0.0164 |
| 1.7186 | 30270 | 0.0163 |
| 1.7192 | 30280 | 0.0159 |
| 1.7198 | 30290 | 0.0162 |
| 1.7203 | 30300 | 0.016 |
| 1.7209 | 30310 | 0.0168 |
| 1.7215 | 30320 | 0.0164 |
| 1.7220 | 30330 | 0.0169 |
| 1.7226 | 30340 | 0.0157 |
| 1.7232 | 30350 | 0.0163 |
| 1.7237 | 30360 | 0.0157 |
| 1.7243 | 30370 | 0.0165 |
| 1.7249 | 30380 | 0.0176 |
| 1.7254 | 30390 | 0.0161 |
| 1.7260 | 30400 | 0.0165 |
| 1.7266 | 30410 | 0.0152 |
| 1.7271 | 30420 | 0.0166 |
| 1.7277 | 30430 | 0.0158 |
| 1.7283 | 30440 | 0.0168 |
| 1.7288 | 30450 | 0.0162 |
| 1.7294 | 30460 | 0.0164 |
| 1.7300 | 30470 | 0.0165 |
| 1.7305 | 30480 | 0.0174 |
| 1.7311 | 30490 | 0.0167 |
| 1.7317 | 30500 | 0.0162 |
| 1.7322 | 30510 | 0.0157 |
| 1.7328 | 30520 | 0.0162 |
| 1.7334 | 30530 | 0.0163 |
| 1.7339 | 30540 | 0.0163 |
| 1.7345 | 30550 | 0.0167 |
| 1.7351 | 30560 | 0.0167 |
| 1.7356 | 30570 | 0.0158 |
| 1.7362 | 30580 | 0.0169 |
| 1.7368 | 30590 | 0.0168 |
| 1.7374 | 30600 | 0.0158 |
| 1.7379 | 30610 | 0.0161 |
| 1.7385 | 30620 | 0.0156 |
| 1.7391 | 30630 | 0.0161 |
| 1.7396 | 30640 | 0.0156 |
| 1.7402 | 30650 | 0.0155 |
| 1.7408 | 30660 | 0.016 |
| 1.7413 | 30670 | 0.0167 |
| 1.7419 | 30680 | 0.0165 |
| 1.7425 | 30690 | 0.0157 |
| 1.7430 | 30700 | 0.0157 |
| 1.7436 | 30710 | 0.0164 |
| 1.7442 | 30720 | 0.0167 |
| 1.7447 | 30730 | 0.0163 |
| 1.7453 | 30740 | 0.0162 |
| 1.7459 | 30750 | 0.0159 |
| 1.7464 | 30760 | 0.017 |
| 1.7470 | 30770 | 0.0159 |
| 1.7476 | 30780 | 0.016 |
| 1.7481 | 30790 | 0.0168 |
| 1.7487 | 30800 | 0.0162 |
| 1.7493 | 30810 | 0.016 |
| 1.7498 | 30820 | 0.0158 |
| 1.7504 | 30830 | 0.0163 |
| 1.7510 | 30840 | 0.0164 |
| 1.7515 | 30850 | 0.0159 |
| 1.7521 | 30860 | 0.0166 |
| 1.7527 | 30870 | 0.0161 |
| 1.7533 | 30880 | 0.0167 |
| 1.7538 | 30890 | 0.0156 |
| 1.7544 | 30900 | 0.0169 |
| 1.7550 | 30910 | 0.0154 |
| 1.7555 | 30920 | 0.0161 |
| 1.7561 | 30930 | 0.016 |
| 1.7567 | 30940 | 0.0158 |
| 1.7572 | 30950 | 0.0169 |
| 1.7578 | 30960 | 0.0151 |
| 1.7584 | 30970 | 0.0161 |
| 1.7589 | 30980 | 0.017 |
| 1.7595 | 30990 | 0.0166 |
| 1.7601 | 31000 | 0.017 |
| 1.7606 | 31010 | 0.0164 |
| 1.7612 | 31020 | 0.0161 |
| 1.7618 | 31030 | 0.0166 |
| 1.7623 | 31040 | 0.0154 |
| 1.7629 | 31050 | 0.0166 |
| 1.7635 | 31060 | 0.0159 |
| 1.7640 | 31070 | 0.0164 |
| 1.7646 | 31080 | 0.0156 |
| 1.7652 | 31090 | 0.016 |
| 1.7657 | 31100 | 0.0168 |
| 1.7663 | 31110 | 0.0164 |
| 1.7669 | 31120 | 0.0158 |
| 1.7674 | 31130 | 0.0165 |
| 1.7680 | 31140 | 0.015 |
| 1.7686 | 31150 | 0.0169 |
| 1.7691 | 31160 | 0.0154 |
| 1.7697 | 31170 | 0.0162 |
| 1.7703 | 31180 | 0.0171 |
| 1.7709 | 31190 | 0.0153 |
| 1.7714 | 31200 | 0.016 |
| 1.7720 | 31210 | 0.0154 |
| 1.7726 | 31220 | 0.0148 |
| 1.7731 | 31230 | 0.0165 |
| 1.7737 | 31240 | 0.0165 |
| 1.7743 | 31250 | 0.0163 |
| 1.7748 | 31260 | 0.0162 |
| 1.7754 | 31270 | 0.016 |
| 1.7760 | 31280 | 0.0164 |
| 1.7765 | 31290 | 0.0156 |
| 1.7771 | 31300 | 0.0149 |
| 1.7777 | 31310 | 0.0163 |
| 1.7782 | 31320 | 0.0166 |
| 1.7788 | 31330 | 0.0172 |
| 1.7794 | 31340 | 0.0156 |
| 1.7799 | 31350 | 0.0149 |
| 1.7805 | 31360 | 0.0164 |
| 1.7811 | 31370 | 0.0168 |
| 1.7816 | 31380 | 0.0144 |
| 1.7822 | 31390 | 0.0157 |
| 1.7828 | 31400 | 0.0169 |
| 1.7833 | 31410 | 0.0151 |
| 1.7839 | 31420 | 0.0164 |
| 1.7845 | 31430 | 0.016 |
| 1.7850 | 31440 | 0.0157 |
| 1.7856 | 31450 | 0.0165 |
| 1.7862 | 31460 | 0.0158 |
| 1.7867 | 31470 | 0.0159 |
| 1.7873 | 31480 | 0.0161 |
| 1.7879 | 31490 | 0.0166 |
| 1.7885 | 31500 | 0.0155 |
| 1.7890 | 31510 | 0.0159 |
| 1.7896 | 31520 | 0.0155 |
| 1.7902 | 31530 | 0.0163 |
| 1.7907 | 31540 | 0.0147 |
| 1.7913 | 31550 | 0.017 |
| 1.7919 | 31560 | 0.0164 |
| 1.7924 | 31570 | 0.0157 |
| 1.7930 | 31580 | 0.0159 |
| 1.7936 | 31590 | 0.0165 |
| 1.7941 | 31600 | 0.016 |
| 1.7947 | 31610 | 0.0161 |
| 1.7953 | 31620 | 0.0173 |
| 1.7958 | 31630 | 0.0161 |
| 1.7964 | 31640 | 0.0158 |
| 1.7970 | 31650 | 0.0157 |
| 1.7975 | 31660 | 0.0156 |
| 1.7981 | 31670 | 0.0157 |
| 1.7987 | 31680 | 0.0163 |
| 1.7992 | 31690 | 0.0167 |
| 1.7998 | 31700 | 0.0165 |
| 1.8004 | 31710 | 0.0166 |
| 1.8009 | 31720 | 0.0163 |
| 1.8015 | 31730 | 0.0156 |
| 1.8021 | 31740 | 0.0161 |
| 1.8026 | 31750 | 0.0164 |
| 1.8032 | 31760 | 0.0153 |
| 1.8038 | 31770 | 0.0166 |
| 1.8043 | 31780 | 0.0161 |
| 1.8049 | 31790 | 0.0167 |
| 1.8055 | 31800 | 0.0166 |
| 1.8061 | 31810 | 0.0165 |
| 1.8066 | 31820 | 0.016 |
| 1.8072 | 31830 | 0.0154 |
| 1.8078 | 31840 | 0.0167 |
| 1.8083 | 31850 | 0.0154 |
| 1.8089 | 31860 | 0.0166 |
| 1.8095 | 31870 | 0.0151 |
| 1.8100 | 31880 | 0.0162 |
| 1.8106 | 31890 | 0.0161 |
| 1.8112 | 31900 | 0.0163 |
| 1.8117 | 31910 | 0.0159 |
| 1.8123 | 31920 | 0.0166 |
| 1.8129 | 31930 | 0.0156 |
| 1.8134 | 31940 | 0.0158 |
| 1.8140 | 31950 | 0.0159 |
| 1.8146 | 31960 | 0.0159 |
| 1.8151 | 31970 | 0.0152 |
| 1.8157 | 31980 | 0.0161 |
| 1.8163 | 31990 | 0.0157 |
| 1.8168 | 32000 | 0.0154 |
| 1.8174 | 32010 | 0.0162 |
| 1.8180 | 32020 | 0.0154 |
| 1.8185 | 32030 | 0.0157 |
| 1.8191 | 32040 | 0.016 |
| 1.8197 | 32050 | 0.0156 |
| 1.8202 | 32060 | 0.0157 |
| 1.8208 | 32070 | 0.0157 |
| 1.8214 | 32080 | 0.0161 |
| 1.8219 | 32090 | 0.0163 |
| 1.8225 | 32100 | 0.0153 |
| 1.8231 | 32110 | 0.0153 |
| 1.8237 | 32120 | 0.0161 |
| 1.8242 | 32130 | 0.0152 |
| 1.8248 | 32140 | 0.016 |
| 1.8254 | 32150 | 0.016 |
| 1.8259 | 32160 | 0.0167 |
| 1.8265 | 32170 | 0.0154 |
| 1.8271 | 32180 | 0.0166 |
| 1.8276 | 32190 | 0.015 |
| 1.8282 | 32200 | 0.0163 |
| 1.8288 | 32210 | 0.0159 |
| 1.8293 | 32220 | 0.0153 |
| 1.8299 | 32230 | 0.015 |
| 1.8305 | 32240 | 0.0159 |
| 1.8310 | 32250 | 0.0159 |
| 1.8316 | 32260 | 0.0163 |
| 1.8322 | 32270 | 0.0157 |
| 1.8327 | 32280 | 0.0165 |
| 1.8333 | 32290 | 0.0158 |
| 1.8339 | 32300 | 0.0164 |
| 1.8344 | 32310 | 0.0159 |
| 1.8350 | 32320 | 0.0154 |
| 1.8356 | 32330 | 0.0159 |
| 1.8361 | 32340 | 0.0156 |
| 1.8367 | 32350 | 0.0162 |
| 1.8373 | 32360 | 0.0159 |
| 1.8378 | 32370 | 0.0164 |
| 1.8384 | 32380 | 0.016 |
| 1.8390 | 32390 | 0.0166 |
| 1.8396 | 32400 | 0.0151 |
| 1.8401 | 32410 | 0.0161 |
| 1.8407 | 32420 | 0.0158 |
| 1.8413 | 32430 | 0.0169 |
| 1.8418 | 32440 | 0.0162 |
| 1.8424 | 32450 | 0.016 |
| 1.8430 | 32460 | 0.0162 |
| 1.8435 | 32470 | 0.0159 |
| 1.8441 | 32480 | 0.0163 |
| 1.8447 | 32490 | 0.0153 |
| 1.8452 | 32500 | 0.017 |
| 1.8458 | 32510 | 0.0163 |
| 1.8464 | 32520 | 0.016 |
| 1.8469 | 32530 | 0.017 |
| 1.8475 | 32540 | 0.0151 |
| 1.8481 | 32550 | 0.0159 |
| 1.8486 | 32560 | 0.0163 |
| 1.8492 | 32570 | 0.015 |
| 1.8498 | 32580 | 0.0163 |
| 1.8503 | 32590 | 0.0155 |
| 1.8509 | 32600 | 0.0168 |
| 1.8515 | 32610 | 0.0149 |
| 1.8520 | 32620 | 0.0168 |
| 1.8526 | 32630 | 0.0163 |
| 1.8532 | 32640 | 0.0158 |
| 1.8537 | 32650 | 0.0161 |
| 1.8543 | 32660 | 0.0157 |
| 1.8549 | 32670 | 0.0151 |
| 1.8554 | 32680 | 0.0153 |
| 1.8560 | 32690 | 0.0162 |
| 1.8566 | 32700 | 0.0173 |
| 1.8572 | 32710 | 0.0159 |
| 1.8577 | 32720 | 0.0167 |
| 1.8583 | 32730 | 0.0166 |
| 1.8589 | 32740 | 0.0162 |
| 1.8594 | 32750 | 0.0162 |
| 1.8600 | 32760 | 0.016 |
| 1.8606 | 32770 | 0.016 |
| 1.8611 | 32780 | 0.0159 |
| 1.8617 | 32790 | 0.0161 |
| 1.8623 | 32800 | 0.0154 |
| 1.8628 | 32810 | 0.0164 |
| 1.8634 | 32820 | 0.0164 |
| 1.8640 | 32830 | 0.0156 |
| 1.8645 | 32840 | 0.016 |
| 1.8651 | 32850 | 0.0163 |
| 1.8657 | 32860 | 0.0157 |
| 1.8662 | 32870 | 0.0158 |
| 1.8668 | 32880 | 0.0155 |
| 1.8674 | 32890 | 0.0152 |
| 1.8679 | 32900 | 0.0163 |
| 1.8685 | 32910 | 0.0166 |
| 1.8691 | 32920 | 0.0163 |
| 1.8696 | 32930 | 0.0149 |
| 1.8702 | 32940 | 0.0159 |
| 1.8708 | 32950 | 0.0164 |
| 1.8713 | 32960 | 0.0167 |
| 1.8719 | 32970 | 0.0166 |
| 1.8725 | 32980 | 0.0162 |
| 1.8730 | 32990 | 0.0158 |
| 1.8736 | 33000 | 0.0158 |
| 1.8742 | 33010 | 0.0156 |
| 1.8748 | 33020 | 0.0173 |
| 1.8753 | 33030 | 0.0169 |
| 1.8759 | 33040 | 0.016 |
| 1.8765 | 33050 | 0.0156 |
| 1.8770 | 33060 | 0.0148 |
| 1.8776 | 33070 | 0.0162 |
| 1.8782 | 33080 | 0.0171 |
| 1.8787 | 33090 | 0.0151 |
| 1.8793 | 33100 | 0.0161 |
| 1.8799 | 33110 | 0.016 |
| 1.8804 | 33120 | 0.0168 |
| 1.8810 | 33130 | 0.0159 |
| 1.8816 | 33140 | 0.0156 |
| 1.8821 | 33150 | 0.0155 |
| 1.8827 | 33160 | 0.0155 |
| 1.8833 | 33170 | 0.0162 |
| 1.8838 | 33180 | 0.0163 |
| 1.8844 | 33190 | 0.0151 |
| 1.8850 | 33200 | 0.0161 |
| 1.8855 | 33210 | 0.0148 |
| 1.8861 | 33220 | 0.0163 |
| 1.8867 | 33230 | 0.0159 |
| 1.8872 | 33240 | 0.0165 |
| 1.8878 | 33250 | 0.0154 |
| 1.8884 | 33260 | 0.0155 |
| 1.8889 | 33270 | 0.0153 |
| 1.8895 | 33280 | 0.0159 |
| 1.8901 | 33290 | 0.0161 |
| 1.8906 | 33300 | 0.0159 |
| 1.8912 | 33310 | 0.0159 |
| 1.8918 | 33320 | 0.015 |
| 1.8924 | 33330 | 0.0159 |
| 1.8929 | 33340 | 0.0163 |
| 1.8935 | 33350 | 0.0156 |
| 1.8941 | 33360 | 0.0153 |
| 1.8946 | 33370 | 0.0163 |
| 1.8952 | 33380 | 0.0159 |
| 1.8958 | 33390 | 0.0168 |
| 1.8963 | 33400 | 0.016 |
| 1.8969 | 33410 | 0.0162 |
| 1.8975 | 33420 | 0.0157 |
| 1.8980 | 33430 | 0.0172 |
| 1.8986 | 33440 | 0.0155 |
| 1.8992 | 33450 | 0.0151 |
| 1.8997 | 33460 | 0.0152 |
| 1.9003 | 33470 | 0.0162 |
| 1.9009 | 33480 | 0.0154 |
| 1.9014 | 33490 | 0.0163 |
| 1.9020 | 33500 | 0.0157 |
| 1.9026 | 33510 | 0.0159 |
| 1.9031 | 33520 | 0.0168 |
| 1.9037 | 33530 | 0.0153 |
| 1.9043 | 33540 | 0.0156 |
| 1.9048 | 33550 | 0.0162 |
| 1.9054 | 33560 | 0.0161 |
| 1.9060 | 33570 | 0.0158 |
| 1.9065 | 33580 | 0.0169 |
| 1.9071 | 33590 | 0.0158 |
| 1.9077 | 33600 | 0.0168 |
| 1.9082 | 33610 | 0.0157 |
| 1.9088 | 33620 | 0.0155 |
| 1.9094 | 33630 | 0.0159 |
| 1.9100 | 33640 | 0.016 |
| 1.9105 | 33650 | 0.0158 |
| 1.9111 | 33660 | 0.0155 |
| 1.9117 | 33670 | 0.0166 |
| 1.9122 | 33680 | 0.0159 |
| 1.9128 | 33690 | 0.0168 |
| 1.9134 | 33700 | 0.0166 |
| 1.9139 | 33710 | 0.0163 |
| 1.9145 | 33720 | 0.0154 |
| 1.9151 | 33730 | 0.0165 |
| 1.9156 | 33740 | 0.0162 |
| 1.9162 | 33750 | 0.0157 |
| 1.9168 | 33760 | 0.0153 |
| 1.9173 | 33770 | 0.0159 |
| 1.9179 | 33780 | 0.0155 |
| 1.9185 | 33790 | 0.0164 |
| 1.9190 | 33800 | 0.0166 |
| 1.9196 | 33810 | 0.0156 |
| 1.9202 | 33820 | 0.0158 |
| 1.9207 | 33830 | 0.0163 |
| 1.9213 | 33840 | 0.0158 |
| 1.9219 | 33850 | 0.0167 |
| 1.9224 | 33860 | 0.0159 |
| 1.9230 | 33870 | 0.0158 |
| 1.9236 | 33880 | 0.0157 |
| 1.9241 | 33890 | 0.0156 |
| 1.9247 | 33900 | 0.0162 |
| 1.9253 | 33910 | 0.0151 |
| 1.9259 | 33920 | 0.0157 |
| 1.9264 | 33930 | 0.0166 |
| 1.9270 | 33940 | 0.015 |
| 1.9276 | 33950 | 0.0158 |
| 1.9281 | 33960 | 0.017 |
| 1.9287 | 33970 | 0.0155 |
| 1.9293 | 33980 | 0.0151 |
| 1.9298 | 33990 | 0.016 |
| 1.9304 | 34000 | 0.0163 |
| 1.9310 | 34010 | 0.0163 |
| 1.9315 | 34020 | 0.0167 |
| 1.9321 | 34030 | 0.0152 |
| 1.9327 | 34040 | 0.0161 |
| 1.9332 | 34050 | 0.0156 |
| 1.9338 | 34060 | 0.0167 |
| 1.9344 | 34070 | 0.0154 |
| 1.9349 | 34080 | 0.0147 |
| 1.9355 | 34090 | 0.0155 |
| 1.9361 | 34100 | 0.0171 |
| 1.9366 | 34110 | 0.0156 |
| 1.9372 | 34120 | 0.0153 |
| 1.9378 | 34130 | 0.0156 |
| 1.9383 | 34140 | 0.0144 |
| 1.9389 | 34150 | 0.0163 |
| 1.9395 | 34160 | 0.0168 |
| 1.9400 | 34170 | 0.0157 |
| 1.9406 | 34180 | 0.0166 |
| 1.9412 | 34190 | 0.0164 |
| 1.9417 | 34200 | 0.0145 |
| 1.9423 | 34210 | 0.0167 |
| 1.9429 | 34220 | 0.016 |
| 1.9435 | 34230 | 0.0161 |
| 1.9440 | 34240 | 0.0163 |
| 1.9446 | 34250 | 0.0165 |
| 1.9452 | 34260 | 0.0166 |
| 1.9457 | 34270 | 0.0159 |
| 1.9463 | 34280 | 0.0154 |
| 1.9469 | 34290 | 0.016 |
| 1.9474 | 34300 | 0.015 |
| 1.9480 | 34310 | 0.0152 |
| 1.9486 | 34320 | 0.016 |
| 1.9491 | 34330 | 0.0162 |
| 1.9497 | 34340 | 0.0152 |
| 1.9503 | 34350 | 0.0154 |
| 1.9508 | 34360 | 0.0169 |
| 1.9514 | 34370 | 0.0161 |
| 1.9520 | 34380 | 0.0158 |
| 1.9525 | 34390 | 0.0153 |
| 1.9531 | 34400 | 0.0157 |
| 1.9537 | 34410 | 0.0162 |
| 1.9542 | 34420 | 0.0159 |
| 1.9548 | 34430 | 0.0163 |
| 1.9554 | 34440 | 0.0156 |
| 1.9559 | 34450 | 0.016 |
| 1.9565 | 34460 | 0.0159 |
| 1.9571 | 34470 | 0.0157 |
| 1.9576 | 34480 | 0.0155 |
| 1.9582 | 34490 | 0.0167 |
| 1.9588 | 34500 | 0.0157 |
| 1.9593 | 34510 | 0.0155 |
| 1.9599 | 34520 | 0.0155 |
| 1.9605 | 34530 | 0.0158 |
| 1.9611 | 34540 | 0.0157 |
| 1.9616 | 34550 | 0.0162 |
| 1.9622 | 34560 | 0.0153 |
| 1.9628 | 34570 | 0.0151 |
| 1.9633 | 34580 | 0.0162 |
| 1.9639 | 34590 | 0.0152 |
| 1.9645 | 34600 | 0.0164 |
| 1.9650 | 34610 | 0.0154 |
| 1.9656 | 34620 | 0.0164 |
| 1.9662 | 34630 | 0.0167 |
| 1.9667 | 34640 | 0.016 |
| 1.9673 | 34650 | 0.0154 |
| 1.9679 | 34660 | 0.0159 |
| 1.9684 | 34670 | 0.0151 |
| 1.9690 | 34680 | 0.0156 |
| 1.9696 | 34690 | 0.0153 |
| 1.9701 | 34700 | 0.0153 |
| 1.9707 | 34710 | 0.0154 |
| 1.9713 | 34720 | 0.0168 |
| 1.9718 | 34730 | 0.0162 |
| 1.9724 | 34740 | 0.0154 |
| 1.9730 | 34750 | 0.0165 |
| 1.9735 | 34760 | 0.0163 |
| 1.9741 | 34770 | 0.0162 |
| 1.9747 | 34780 | 0.0162 |
| 1.9752 | 34790 | 0.0155 |
| 1.9758 | 34800 | 0.0164 |
| 1.9764 | 34810 | 0.0153 |
| 1.9769 | 34820 | 0.0155 |
| 1.9775 | 34830 | 0.0165 |
| 1.9781 | 34840 | 0.0168 |
| 1.9787 | 34850 | 0.0161 |
| 1.9792 | 34860 | 0.0155 |
| 1.9798 | 34870 | 0.0159 |
| 1.9804 | 34880 | 0.0148 |
| 1.9809 | 34890 | 0.0155 |
| 1.9815 | 34900 | 0.0165 |
| 1.9821 | 34910 | 0.016 |
| 1.9826 | 34920 | 0.0164 |
| 1.9832 | 34930 | 0.0156 |
| 1.9838 | 34940 | 0.016 |
| 1.9843 | 34950 | 0.0155 |
| 1.9849 | 34960 | 0.0157 |
| 1.9855 | 34970 | 0.0161 |
| 1.9860 | 34980 | 0.0159 |
| 1.9866 | 34990 | 0.0156 |
| 1.9872 | 35000 | 0.0166 |
| 1.9877 | 35010 | 0.0154 |
| 1.9883 | 35020 | 0.0156 |
| 1.9889 | 35030 | 0.0155 |
| 1.9894 | 35040 | 0.0168 |
| 1.9900 | 35050 | 0.0163 |
| 1.9906 | 35060 | 0.0159 |
| 1.9911 | 35070 | 0.0164 |
| 1.9917 | 35080 | 0.0161 |
| 1.9923 | 35090 | 0.0158 |
| 1.9928 | 35100 | 0.0157 |
| 1.9934 | 35110 | 0.0159 |
| 1.9940 | 35120 | 0.016 |
| 1.9945 | 35130 | 0.0153 |
| 1.9951 | 35140 | 0.0158 |
| 1.9957 | 35150 | 0.0162 |
| 1.9963 | 35160 | 0.0169 |
| 1.9968 | 35170 | 0.0155 |
| 1.9974 | 35180 | 0.0159 |
| 1.9980 | 35190 | 0.0152 |
| 1.9985 | 35200 | 0.0151 |
| 1.9991 | 35210 | 0.0151 |
| 1.9997 | 35220 | 0.0158 |
| 2.0002 | 35230 | 0.0151 |
| 2.0008 | 35240 | 0.0145 |
| 2.0014 | 35250 | 0.0143 |
| 2.0019 | 35260 | 0.015 |
| 2.0025 | 35270 | 0.0147 |
| 2.0031 | 35280 | 0.0154 |
| 2.0036 | 35290 | 0.0148 |
| 2.0042 | 35300 | 0.0148 |
</details>
### Framework Versions
- Python: 3.12.0
- Sentence Transformers: 3.4.0.dev0
- Transformers: 4.46.3
- PyTorch: 2.5.1+cu121
- Accelerate: 1.1.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
<!--
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|
{"base_model": "Tochka-AI/ruRoPEBert-e5-base-512", "library_name": "sentence-transformers", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:2465773", "loss:ContrastiveLoss"], "widget": [{"source_sentence": "Вообще-то, цветущая весна здесь началась ещё в начале февраля, но мне не хотелось дразнить людей теплом слишком задолго до окончания зимы. А вот сейчас - самое время!\nВсе фотографии сделаны мной в городском парке или недалеко от города. Проводить здесь зимние месяцы - одно удовольствие! Очень комфортная погода, много зелени, а сейчас и цветов - любуйтесь сами :3\n<PERSON>. Ну ведь красавица!\nЯ уже почти месяц жду, когда зацветёт всё дерево, но создаётся впечатление, будто снизу раскрывшиеся бутоны кто-то обрывает - ещё вчера был, а сегодня нет. Возможно, они просто опадают, сегодня я как раз подобрала пару штучек.\nА этот кустарник цвёл всю зиму. Таких жёлтых кустарников тут несколько видов, есть и те, которые вкусно пахнут.\nА тут я специально ради вас нашла название \\- Красный Хеномелес! Или просто японская айва. Я как-то привыкла к белым и розовым цветочкам на деревьях, а такой насыщенный красный приятно удивил.\nА это цветёт <PERSON>, мне в комментариях подсказали =)\nИнтересно, что до сих пор осталось немножко осенних листьев (а в декабре я успела собрать очень красочный гербарий). Такой вот осенне-весенний контраст.\nЭто жасмин. Кажется. Ну, я краем уха слышала, как тётенька-экскурсовод рассказывала.\nА вот это точно знаю - цикломены лесные. Одни из самых ранних цветочков, застилают лесные поляны - очень мило, с начала февраля цветут.\nПодснежники цвели вместе с цикломенами в феврале.\nСкоро у нас распустится <PERSON>. Жду-жду *_*\nНу и, конечно же, <PERSON>! Если встать в правильном месте и поймать нужный ветер, можно услышать прекрасный аромат =)\nЕсли у вас возникает мысль \"А не махнуть ли мне в Сочи\" - решайтесь! Здесь не только пустынные пляжи созерцательно море, но и очень приятно гулять среди зелени и цветов.", "sentences": ["Урал радует разнообразием. В лёгкой пешей прогулке между деревеньками и дачами можно увидеть...\nВыработанный мраморный карьер.\nКак после хорошего дождя тропинки становятся белыми.\nКарьер, как это часто бывает, заполнен водой и служит местом отдыха\nС определённых ракурсов вода приобретает очень красивый оттенок.\nНа такой почве очень хорошо растёт облепиха.\nА дорога под ногами блестит и переливается бликами от солнца, но этого я вам не покажу.\nВ нескольких дестяках метров от карьера есть небольшой водопад.\nМаленький, но симпатичный.\nИ даже ущелье! Или каньон?\nНеподалёку располагается ни много ни мало памятник природы - базальтовые скалы.\nПохоже на поджаренную щепку, правда? Только гораааздо больше. И скала.\nВы уже заметили по теням, какой тут стройный хвойный лес?\nА ещё тут растут люпины, прямо как в Зеландиях и Исландиях. [Помимо них ещё красиво цветёт Иван-чай в окрестностях.]\nНу и завершим прогулку красивым закатом, хотя они в любом месте красивы - за это люблю свою планету.\nКонечно, ещё есть река, поля, грибы, лесные озёра и что-то, до чего мы ещё не прогулялись. Доехать можно хоть на электричке - 47 или 49 км. В окрестных деревнях тоже есть на что посмотреть.\nКрасота ближе, чем вы думаете ;)", "Может люди работают до поздна? Приходят в 22, что им теперь дела домашние не делать? Явно косяк со слышимостью. Вам мешают - сразу пара долбоебов. У нас раньше такая херня была - внизу соседи слышали как мы ходим и даже как компьютерное кресло двигается. Тоже истерики устраивали, мой к ним содил поговорить, позвонил мне попросил походить. Да, говорит слышно сильно. А знаете в чем прикол? В том что я не топаю. Я хожу тихо. Тут именно вибрация. Поменяли кресло - специально когда покупали катали чтобы вообще не было слышно, постелили ковёр. Вроде перестали на нас жаловаться. С той квартиры мы съехали, сейчас там живет мама молодого человека моего. Так на нее они гонят за то что стиралку ночью включает! На кухне. Стиралка ее из тех что идёт на взлёт. При нас не жаловались. Но видимо доебаться надо. Может и вы такой? Может вам надо доебаться?", "Позиция нажатого элемента ListView и её получение в запущенном активити\nВсем добрый день.\nПосле нажатия на элемент списка listview, нужно передать его позицию (номер элемента списка) в другое активити. Использую position, которая передается в onListItemClick. Но в другое активити почему то передается null.\nПомогите разобраться))\n[code]\n public class MainActivity extends ListActivity {\n @Override\n protected void onListItemClick(ListView l, View v, int position, long id) {\n super.onListItemClick(l, v, position, id);\n Intent intent = new Intent(getApplicationContext(), AfterClickActivity.class);\n intent.putExtra(\"position\",position);\n startActivity(intent);\n }\n @Override\n protected void onCreate(Bundle savedInstanceState) {\n Integer[] array = new Integer[100];\n for (int i = 0; i <100 ; i++) {\n array[i] = i;\n }\n super.onCreate(savedInstanceState);\n ArrayAdapter adapter = new ArrayAdapter(getApplicationContext(),R.layout.list_item,R.id.tvText,array);\n setListAdapter(adapter);\n[/code]\n}\n[code]\n public class AfterClickActivity extends ActionBarActivity {\n @Override\n protected void onCreate(Bundle savedInstanceState) {\n super.onCreate(savedInstanceState);\n setContentView(R.layout.activity_after_click);\n String position = getIntent().getStringExtra(\"position\");\n TextView view = (TextView)findViewById(R.id.tvView1);\n view.setText(String.valueOf(position));\n }\n[/code]"]}, {"source_sentence": "Здравствуйте,попал в не приятную ситуацию. В октябре на МКАД произошло мелкое ДТП я на грузовой машине легонько притер <PERSON> сам виноват,не соблюдал дистанцию. За рулём девушка,поговорили оформили евро протокол и разъехались. Протокол в страховую компанию надо было предоставить в течение пяти дней. Грузовая машина оформлена на предприятие где я работаю. Через день с механиком поехали в страховую, а там висит объявление что прием заявлений и протоколов временно приостановлен. Сколько это \"временно \"продлилось. Не известно. Сегодня пришло письмо из Росгосстраха ,о том что я вовремя не предоставил соответствующие документы и выплата по ремонту 18800к возлагается на меня. В страховую больше не приезжал. Как быть?", "sentences": ["Кстати ole72ole я писал уже выше насчёт книги \"Пластичность мозга\", там в самой первой главе говорится о женщине которая всё время падала и о методах которыми её смогли восстановить. Хорошо бы показать эту книгу его родственникам, чтобы в свою очередь они прочитали и рассказали о методах лечащему врачу. Вот начало этой главы, мне кажется или у вашего сопалатника похожая ситуация только чуть всё иначе? Могу скинуть если напишешь свою почту эту книгу, т.к. на пикабу я не нашёл Личных сообщений, а в свободном доступе (бесплатно) книгу очень тяжело найти.\n<PERSON> постоянно кажется, что она падает. И из-за этого непреходящего ощущения она\nдействительно не может устоять на ногах.\nКогда <PERSON>, не имея точки опоры, поднимается с места, в первые мгновения она выглядит так,\nсловно стоит на краю пропасти, в которую вот-вот упадет. Сначала у нее начинает дрожать и\nсклоняться на одну сторону голова, и <PERSON> вытягивает руки, пытаясь зафиксировать свое положение.\nПосле этого ее тело принимается хаотично двигаться вперед и назад, и тогда <PERSON> напоминает\nчеловека, идущего по туго натянутому канату в тот ужасный момент, когда канатоходец начинает\nтерять равновесие. При этом вы видите, что <PERSON>, на самом деле, стоит на твердой, устойчивой\nповерхности (на полу, на земле), широко расставив ноги. Создается впечатление, что она боится не столько падения, сколько того, что ее толкнут.", "Семья из Екатеринбурга более 10 лет пытается добиться восстановления вентиляции в квартире\n<PERSON>:\nМесто действия: мск, дом 24 этажа. Я на 9-м. Стал замечать, что нет тяги в вентиляции. Пришел в диспетчерскую УК (благо два шага от подъезда), написал заявление на проверку/прочистку.\nФабула:\nВозвращаюсь часов в 8 вечера, в холле подъезда стихийный митинг из человек 7, что мол УК совсем охерели и портят имущество. Вслушиваясь в монолог истерящей дамочки проясняю обстоятельства: разбит холодильник.\nДалее подробности от УК.\nУК приняв заявку, отправляет работника на крышу проверить вентканал, (короб в квартире ппц толстый: где то 120*50 см по внешним габаритам, и расположен там, где многие ввиду нестандартной планировки, хотели бы установить холодильник) решили прочистить канал привязав обычную 16кг гирю советского производства к тросу начали проход шахты. На высоте примерно 12-11 этажа нашли \"засор\" и начали прочистку, постепенно увеличивая высоту подъёма грузика... Уж не знаю на какую высоту подняли этот грузик, но он прошел \"засор\" в виде холодильника до середины морозилки.\nКак итог за самовольную врезку в вент канал \"пострадавшей\" был влуплен штраф и предписание устранить изменения.\nЕдинственное что частично оправдывает \"пострадавшую\" - она сделала обвод вокруг врезанного холодильника из венттрубы 110мм*50мм квадратного сечения, что было явно недостаточно для тяги в таком стояке.", "Космическое производство кристаллов для лазеров\nКлючевой вопрос космического производства стоит так. Что в космосе можно делать такого, чтобы оно 1) отбивало стоимость вывода на орбиту и возвращения на Землю груза и 2) оно было массовым, чтобы рынок был достаточно большим?\nКогда начинают говорить о 3D-печати живой ткани - то, по моим впечатлениям, говорят об этом от отчаяния. Да, с научной точки зрения это очень интересно. Но до того, чтобы что-то такое напечатанное пересаживать живому человеку - мягко говоря ну вот совсем очень далеко.\nДа, можно печатать ткани нескольких органов на одной площадке, связывать их друг с другом, организовывать обмен веществ, и затем смотреть как какое-нибудь новое лекарство влияет на \"экосистему\" тканей (а не на каждый вид ткани в отдельности). Но это все штучный товар, бизнес на этом не построить.\nЧто-то более близкое к реальной экономике сделала летом корпорация Redwire.\nRedwire вырастила на орбите на борту МКС двухграммовый кристалл дигидрофосфата калия (их используют для создания мощных лазеров) и даже продала его - Университету штата Огайо. Компания заявляет, что это первая в истории сделка с товарами, произведенными в космосе. Вот этот кристалл:\nКристалл, выращенный Redwire на МКС\nПервые попытки выращивания кристаллов на орбите были предприняты в 1970-х. На фото слева - \"земные\" кристаллы, а справа - кристаллы, выращенные в космосе в условиях микрогравитации (см. фото ниже).\nПрофессор <PERSON> приобрел образец, выращенный Redwire за 4000 долларов, что эквивалентно 2 миллионам долларов за килограмм.\nСейчас сотрудники университетского центра электронной микроскопии и анализа изучают образец - режут его на тонкие сегменты и изучат его свойства.\nRedwire ожидает, что анализ покажет, что выращивание кристалла в условиях микрогравитации привело к меньшему количеству дефектов и получению более качественного продукта.\nhttps://t.me/IngeniumNotes/622"]}, {"source_sentence": "Феодосия, 25 декабря. Крыминформ. В Феодосии 67-летний <PERSON> объявил голодовку и грозится покончить жизнь самоубийством в связи с тем, что суд не удовлетворил иск о подтверждении факта его постоянного проживания на территории Крыма, что необходимо для приобретения российского гражданства. Он объявил об этом сегодня, выйдя к зданию городского суда с табличкой \"Голодовка\".\n\"Суд 22 декабря вынес решение, которым не подтверждает факт моего постоянного проживания в Феодосии. Я прописан в Украине, но живу в городе по одному и тому же адресу с 2004 года, работал на заводе, принимаю активное участие в жизни города. Пенсию мне перестали выплачивать, жить не на что, деньги занимаю у соседей\", - сообщил <PERSON>. По его словам, на суде давали показания его соседи, а сам он представил подтверждающие документы.\nСегодня, как сообщил мужчина, он получил ответ председателя суда <PERSON>, в котором ему предлагают обратиться в апелляционный суд. Однако, по словам <PERSON>, у него нет денег даже на оплату госпошлины. \"Мне рекомендовали обратиться в апелляционный суд, но денег на это у меня нет вообще\", - сказал он.", "sentences": ["17-18 января исполнилось два года эпохальному событию: кухонная утварь (кастрюли, дуршлаги и прочее) напрочь вошла в украинскую политику, а понятие «кастрюлеголовые» плотно закрепилось за майданствующими особями незалежной.\nНапомним, 16 января 2014 года Верховная Рада Украины приняла (а 17 января президент <PERSON> подписал) пакет законов, которые должны были помочь в борьбе с массовыми беспорядками. Как-то: запрещалось участие в демонстрациях с использованием средств, затрудняющих идентификацию личности, присутствие на митинге с открытым огнем, пиротехникой, оружием, газовыми баллончиками, взрывчаткой и т. п. средствами, а также в камуфляже, масках и касках. Отметим, что практически все принятые тогда законы имели прямые аналоги в законодательстве стран ЕС и США. Но Совет Европы дал им предварительную собственную оценку, по которой многие предлагаемые нормы почему-то не соответствовали Европейской конвенции по правам человека, рекомендовав парламенту либо отменить, либо приостановить их действие в течение значительного периода времени.\nАдепты «революции гыдности» сразу же окрестили принятые акты «Законами о диктатуре» и массово вышли на Майдан с кастрюлями на головах. К слову, 9 из 12-ти законов таки были отменены Верховной Радой 28 января 2014 года. Но, увы, кухонная утварь плотно сдавила черепа украинских патриотов: многие до сих пор кастрюли так и не сняли.\n«Нешуточная дата – день кастрюлеголовых. Есть и те, кто, спустя два года, ничего так и не понял. Поздравляют друг дружку, вспоминают, кто ведро нацепил, кто – дуршлаг, кто – кастрюлю с цветочками. Они и по сей день не сняли с головы кастрюли. Им только кажется, что они их сняли. А на самом деле кастрюли приросли и пустили корни», - пишет блогер <PERSON>.\nКак сложилась судьба этих кастрюлеголовых идиотов – известно всем. Скачущие холопы стали «незалежнее всех незалежных» вместе взятых: отныне их судьба им не принадлежит. А как еще объяснить тот факт, что внутренние дела развалившейся Украины решают <PERSON> и <PERSON>, при этом даже не информируя «козаков гаврылюков» о результатах переговоров? И вправду, кастрюлеголовая Украина теперь самая независимая страна в мире: от нее в мире ничего не зависит!\n«Эй, мыздобулы! А ни у кого из вас не чешется ганьба через то, что ваши дела решают <PERSON> (не президент, но, по вашим словам, командовавший снайперами на майдауне) и <PERSON> (тоже не президент, но кормившая вас печеньками на майдауне)? Причем, ваших олигофренов-хунтозавров даже не пригласили послушать, про что они там тёрли! Ну, что шнобаками хлюпаете?\nХрюкните что-либо по этому поводу! СУГС, например», - обращается к майданным деятелям блогер и журналист <PERSON>.\nНо в ответ почему-то никто не хрюкает. Возможно, в сдавленных кастрюлями черепах шевелятся остатки разума? И этот разум говорит следующее (снова устами <PERSON>):\n1.Убили 50 тысяч человек.\n2\\. Изгнали из домов 3 млн. человек.\n3\\. Разрушили Донбасс.\n4\\. Прос*али Крым и Донбасс.\n5\\. Поссорили народ Украины.\n6\\. Сделали половину населения внутренними эмигрантами, а другую - подлыми стукачами и тиранами.\n7\\. Уронили гривну в три раза.\n8\\. Уничтожили остатки промышленности.\n9\\. Оставили людей без работы и заработка.\n10\\. Посчитали гов*нные коммунальные услуги в гов*нных хрущёбах как в «пяти звездах».\n11\\. Сделали Украину колонией анонимных кредиторов.\n12\\. Сделали армию карательной, а солдат и офицеров - карателями.\n13\\. Сделали украинский паспорт тревожным сигналом для погранцов всех планет солнечной системы.\n14\\. Установили тиранию потомков фашистских недобитков.\n15\\. Превратили прессу в яму со змеями.\n16\\. Превратили общество в стадо тупых, но агрессивных захребетников.\n17\\. <PERSON> предали и искусили предать других свою историю, своих предков, свою Родину.\n18\\. Сделали презренного буржуя своим президентом.\nПодробнее: http://antifashist.com/item/den-kastryulegolovyh-oni-zdobuly...", "Всех приветствую.\nПервый сшитый мной лонгер.\nНу или длинный кошелек.\nНикогда их не делал, решил попробовать.\nОказалось довольно таки удобная штука.\nХвалюсь тем что получилось.\nСемь отделений для карт, три больших кармана под купюры и документы.\nНебольшой карман для монет или других мелких предметов закрывающийся на качественную молнию.\nКожа: \"растишка\" итальянская 1,5 - 1,6 мм.\nПодклад: овечка тонкая.\nПробойник: ромб 4 мм.\nНитка: Галасес 0,5 мм.\nТорцы обработаны при помощи краски <PERSON> и наждачки.\nМолния тоже какая то иностранная (не Китай, без вирусов)).\nКак Вам?", "нет, не в России, это в риге. не получится пока, дом давно сдали в эксплуатацию, ворота наверняка закрыты, круг далеко от ворот. фоткать особо нечего там, просто как большой круглый люк с двумя металлическими кольцами на стыке. на всякий <PERSON>. у меня есть топоплан чтоб иметь представление. круг 4,5м (пунктир на плане), двор от бордюра до стены 7х8м. всё включая круг уложено брусчаткой. возможно что в кругу, это имитация брусчатки. механизма не видел. все уже было смонтировано когда я мерял."]}, {"source_sentence": "<PERSON> не проходить, а пропускать других людей - предоставлять проходы.\nПо правилам у каждого человека должен быть свой индивидуальный способ прохода - проездной, банковская карта, социальная карта и т.д. То есть нельзя купить проездной на две поездки и двум людям использовать его для одновременного прохода. По факту часто закрывают глаза, если по одному лимитному проездному проходят пара-тройка человек (друзья, семья), но не стоит это воспринимать как должное и причитать, если нарвётесь на штраф.", "sentences": ["Господа юристы, пожалуйста, прокомментируйте. Просто очень много противоречивой информации. Хотя бы те моменты которые я чаще всего слышу.\nСейчас очень популярны настроения: вводите ЧП или ЧС.\nИ тогда заживем:\n1\\. Всем предприятиям по закону оплатят все зарплаты сотрудников которые в \"самоизоляции\".\n2\\. Всем простят кредиты. Ну ок, не простят, но можно не платить.\n3\\. Обязательств по договорам например с управляющей компанией нет. За ЖКХ можно не платить.\n4\\. Всем кто не работает должны денег по закону. Я серьезно, и такое часто встречал.\nНе будет ли:\n1\\. Любой работодатель сможет увольнять работников по трудовому договору. Ссылаются на раздел \"форс-мажор\"\n2\\. Не получится ли так. У меня договор с со страховой компанией, наступил страховой случай, компания не заплатила на законных основаниях ссылаясь на ЧП/ЧС?\n<PERSON> от греха тег <PERSON>.", "Веб в нынешнем его состоянии - это как раз клоака, где всем пофиг как, главное чтоб работало и стоило $0.01, а лучше еще дешевле. Как оно там работает, будет ли способно к развитию и поддержке - всем насрать, ибо через год все стопиццот фреймворков по десять раз сменят мажорные версии и API, половина из них и вовсе помрет, зато родится еще столько же новых, вчерашние джуниоры уйдут в менеджмент и прочие \"побегушки\" а на их место вновь придут люди с нулевыми знаниями, старый код даже пытаться разбирать никто не станет - его просто выкинут и напишут заново. Это как с китайскими товарами: мммаксимум дешево, мммаксимум дешево и мммаксимум дешево, при этом полностью неремотнопригодно.", "Когда в меде учился,то на 5-м курсе была психиатрия -цикл.\nВ общем на одном занятии повели нас по палатам и \"раздали \" больных. Типа опросите,опишите жизнь,анамнез и поставьте предварительный диагноз .\nИтак, мне попалась девушка. На вид лет 17. Начинает рассказывать про жизнь,что у нее она тяжёлая была. Всю жизнь в спорте с детства,что родила в 15 лет по залету. Какой вид спорта? Она мне начинает рассказывать подробно про лёгкую атлетику ,затем ,что занималась спортивной гимнастикой . И говорила НАСТОЛЬКО убедительно ,что я начал верить. Но затем ее понесло и начала говорить,что на Олимпиаде 2008 года выступала ,заняла бронзу по бегу . Я на этом моменте прихерел и начал подозревать подвох ( 17 лет, олимпиада, ребенок в 15....WTF?)\nДля убедительности она херанулась с разбега в шпагат.\nВ итоге оказалось,что ей 21,ребенка никакого нет,никаким спортом она не занималась. Была простой студенткой колледжа поваров и свихнулась. Шизофрения.\nС тех пор я уверен,что много среди нас не совсем адекватных. Просто мы их не замечаем."]}, {"source_sentence": "Решил своей девушке сделать подарок ко дню рождения. А именно - котенка (давно хотела завести). Нашел девушку, которая отдает маленького милого котенка в добрые руки. Созвонились, договорились о встрече, хозяйка сказала, что как только вернется домой с магазина - можно приехать и забрать пушистого зверька. Через час поступил звонок, своей девушке сказал, что надо по делам рабочим сгонять. В итоге девушка оказалась непорядочной, когда приехал, она сообщила, что котенка уже забрали 10 минут назад, мол, приехали раньше меня. Но это полбеды.\nВечером следующего дня я понял, что записать в девушку в телефоне как \"Катя(Котёнок)\" было очень глупой идеей...", "sentences": ["Дело было году в 2010-м. Время моего беззаботного студенчества и прочих ништяков 19-тилетнего возраста. Дело было на зимних каникулах, в период святок. Я вернулась в полупустое общежитие от родителей, через пару дней приехала моя близкая подруга, которая предложила мне в одну из ночей вызвать чей-нибудь дух - святки же. Надо сказать, в ту пору мы подобным оккультизмом увлекались в силу юного возраста и гуманитарных направлений в институте. Я как умела пробовала гадать на таро (сейчас понимаю, как это по прошествии лет выглядит), подруга собирала всякий оккультный славянский фольклор и неплохо в этом разбиралась. Но ближе к делу. Решено было вызвать дух. Но чей? Выбор по ряду причин пал на легендарную для отечественной рок-музыки личность - <PERSON>.\nСеанс решили проводить с иглой на нити, кругом с буквами и свечой, которая однажды была зажжена от благодатного огня. Свечу нам предоставила преподаватель по психологии(!), которая неоднократно вещала о ее магических свойствах. В назначенный день, начитавшись всяких оккультных историй и насмотревшись соответствующих фильмов мы наготовили сладких пироженок (в подобной литературе часто говорится, что сладкое после таких сеансов помогает прийти в себя).\nНе веря особо в успех, мы выключили свет, закрыли окна, зажгли свечу, подвесили иглу и начали вещать \"Призываем дух <PERSON>!\"\nДо какого-то момента мы вещали все ту же фразу \"Мы призываем...\" и уже готовы были прекратить, так как игла висела неподвижно, и каких-то других признаков духа не было, как вдруг из ниоткуда появился мотылек, мотылек, КАРЛ! (январь-месяц на дворе) и начал наворачивать круги вокруг нас.\nСтало не просто страшно. Стало до кирпичей страшно!!! Но отступать было некуда, подруга решила начать распрашивать дух. Дрожащим голосом, с явными признаками паники, она начала спрашивать его про <PERSON> и ее смерть (кто не знает, она известный рок-музыкант, погибшая при странных обстоятельствах и состоявшая с <PERSON> в отношениях долгое время). И вот тут...я не знаю... то ли у меня удачно дрожали руки, то ли сквозняк, но игла начала качаться, выдавая вполне себе осмысленные ответы из букв в круге.\nТак продолжалось какое-то время, наша паника нарастала, как вдруг мотылек сел прямо на пламя свечи и моментально сгорел... Подруга посчитала это знаком ухода \"духа\", и мы порешили, что все закончилось.\nСовпадение или нет - сладкого и правда хотелось до безумия. Мы включили свет, достали приготовленные сладости и решили послушать музыку. Естественно запустили в плейлист всю папку с названием \"ГрОб\". Казалось бы, все закончилось, но последним звоночком стало то, что в рандомном плейлисте первой песней была \"Отряд не заметил потери бойца\", которая начинается строкой \"Глупый мотылек догорал на свечке\".\nНадо ли говорить, что мы ночью спали в другой комнате? И духов больше никогда вызвать не пытались.\nСейчас, вспоминая данный случай, единственную крипоту в моей жизни, наверное, я искренне хочу верить, что это не более чем стечение обстоятельств. Если бы не мотылек, это им бы и было с вероятностью 146%. Подобными вещами больше не увлекаюсь. Повзрослела и из гуманитария превратилась в инженера.", "Чтобы факт, признанный наукой назвать \"дурацким стереотипом\"? Ну да, как по мне, требуется осведомлённость повыше обывательской. И ещё раз. С вами никто не спорит. Бывает всяко, в том числе так, как вы написали. Вариантов столько же, сколько живых людей. А паттерны, усвоенные в семье ребёнком, присущи большому количеству взрослых.\nИ речь изначально шла о модели семьи и вариантах поведения, связанных с ней. То есть \"мальчик копирует отношение папы к маме\". Это не значит, что его ждёт такая же судьба, у него будет такой же характер, или он тоже разведётся в 27. Но по умолчанию, пока не наживёт своего опыта (а некоторые и дальше), мальчик будет опираться на то, что видел в семье. Ваш пример с эмпатией - верен. Только в этом контексте ничего не доказывает.", "на работе выключили свет. Диспетчер сидит в своей программе, делает табеля. бесперебойник периодически пищит.\n\\- д не пищи ты!!\n\\- пиик пиик пиик\n\\- д знаю я что света нет, не ори!!\n\\- пиик пиик пиик\n\\- ну сейчас сейчас, доделаю и выключу\n\\- пиик пиик пиик\n\\- какой же ты глупый!!!\n\\- пиик пиик пиик\n\\- как же ты достал, ну сейчас, еще 10 человек осталось и выключаю\n\\- пиииииииииик...........\nкомпьютер гаснет.....пауза......злая тётенька диспетчер осознает что произошло\n\\- он что вырубился????? так я же ничего не сохранила!!!! какого черта??? мне же теперь все заново делать!!!!! что за компьютер глупый такой!!!\nпочти собралась сисадмину звонить )))) но не стала)))\nЗа компьютер обидно, он же из последних сил терпел, кричал, а его глупым...)"]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,009 |
wesleymorris/longformer-content-global
|
wesleymorris
|
text-classification
|
[
"transformers",
"pytorch",
"longformer",
"text-classification",
"macroeconomics",
"automated summary evaluation",
"content",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-05-10T11:57:38Z |
2024-07-03T15:27:48+00:00
| 175 | 0 |
---
language:
- en
license: apache-2.0
metrics:
- mse
tags:
- macroeconomics
- automated summary evaluation
- content
thumbnail: url to a thumbnail used in social sharing
---
# Content Model
This is a longformer model with a regression head designed to predict the Content score of a summary.
By default, longformers assign global attention to only the classification token with a sliding attention window which moves across the rest of the test.
This model, however, is trained to assign global attention to the entire summary with a reduced sliding window.
When performing inference using this model, you should make sure to assign a custom global attention mask as follows:
```
def inference(summary, source, model):
combined = summary + tokenizer.sep_token + source
context = tokenizer(combined)
sep_index = context['input_ids'].index(2)
context['global_attention_mask'] = [1]*(sep_index + 1) + [0]*(len(context['input_ids'])-(sep_index + 1))
inputs = {}
for key in context:
inputs[key] = torch.tensor([context[key]])
return float(model(**inputs)['logits'][0][0])
```
## Corpus
It was trained on a corpus of 4,233 summaries of 101 sources compiled by Botarleanu et al. (2022).
The summaries were graded by expert raters on 6 criteria: Details, Main Point, Cohesion, Paraphrasing, Objective Language, and Language Beyond the Text.
A principle component analyis was used to reduce the dimensionality of the outcome variables to two.
* **Content** includes Details, Main Point, Paraphrasing and Cohesion
* **Wording** includes Objective Language, and Language Beyond the Text
## Score
This model predicts the Content score. The model to predict the Wording score can be found [here](https://huggingface.co/tiedaar/longformer-wording-global).
The following diagram illustrates the model architecture:
When providing input to the model, the summary and the source should be concatenated using the seperator token \</s>.
All summaries for 15 sources were withheld from the training set to use for testing.
This allows the model to have access to both the summary and the source to provide more accurate scores. The model reported an R2 of 0.82 on the test set of summaries.
## Contact
This model was developed by LEAR Lab at Vanderbilt University.
For questions or comments about this model, please contact [[email protected]]([email protected]).
---
| null |
Non_BioNLP
|
# Content Model
This is a longformer model with a regression head designed to predict the Content score of a summary.
By default, longformers assign global attention to only the classification token with a sliding attention window which moves across the rest of the test.
This model, however, is trained to assign global attention to the entire summary with a reduced sliding window.
When performing inference using this model, you should make sure to assign a custom global attention mask as follows:
```
def inference(summary, source, model):
combined = summary + tokenizer.sep_token + source
context = tokenizer(combined)
sep_index = context['input_ids'].index(2)
context['global_attention_mask'] = [1]*(sep_index + 1) + [0]*(len(context['input_ids'])-(sep_index + 1))
inputs = {}
for key in context:
inputs[key] = torch.tensor([context[key]])
return float(model(**inputs)['logits'][0][0])
```
## Corpus
It was trained on a corpus of 4,233 summaries of 101 sources compiled by Botarleanu et al. (2022).
The summaries were graded by expert raters on 6 criteria: Details, Main Point, Cohesion, Paraphrasing, Objective Language, and Language Beyond the Text.
A principle component analyis was used to reduce the dimensionality of the outcome variables to two.
* **Content** includes Details, Main Point, Paraphrasing and Cohesion
* **Wording** includes Objective Language, and Language Beyond the Text
## Score
This model predicts the Content score. The model to predict the Wording score can be found [here](https://huggingface.co/tiedaar/longformer-wording-global).
The following diagram illustrates the model architecture:
When providing input to the model, the summary and the source should be concatenated using the seperator token \</s>.
All summaries for 15 sources were withheld from the training set to use for testing.
This allows the model to have access to both the summary and the source to provide more accurate scores. The model reported an R2 of 0.82 on the test set of summaries.
## Contact
This model was developed by LEAR Lab at Vanderbilt University.
For questions or comments about this model, please contact [[email protected]]([email protected]).
---
|
{"language": ["en"], "license": "apache-2.0", "metrics": ["mse"], "tags": ["macroeconomics", "automated summary evaluation", "content"], "thumbnail": "url to a thumbnail used in social sharing"}
|
task
|
[
"PARAPHRASING"
] | 42,010 |
Amir13/xlm-roberta-base-ontonotesv5
|
Amir13
|
token-classification
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:Amir13/ontonotes5-persian",
"arxiv:2302.09611",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-14T14:12:21Z |
2023-03-16T21:05:59+00:00
| 19 | 0 |
---
datasets: Amir13/ontonotes5-persian
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-ontonotesv5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-ontonotesv5
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ontonotes5-persian](https://huggingface.co/datasets/Amir13/ontonotes5-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Precision: 0.8336
- Recall: 0.8360
- F1: 0.8348
- Accuracy: 0.9753
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1145 | 1.0 | 2310 | 0.1174 | 0.7717 | 0.7950 | 0.7832 | 0.9697 |
| 0.0793 | 2.0 | 4620 | 0.1084 | 0.8129 | 0.8108 | 0.8118 | 0.9729 |
| 0.0627 | 3.0 | 6930 | 0.1078 | 0.8227 | 0.8102 | 0.8164 | 0.9735 |
| 0.047 | 4.0 | 9240 | 0.1132 | 0.8105 | 0.8223 | 0.8164 | 0.9731 |
| 0.0347 | 5.0 | 11550 | 0.1190 | 0.8185 | 0.8315 | 0.8250 | 0.9742 |
| 0.0274 | 6.0 | 13860 | 0.1282 | 0.8088 | 0.8387 | 0.8235 | 0.9734 |
| 0.0202 | 7.0 | 16170 | 0.1329 | 0.8219 | 0.8354 | 0.8286 | 0.9745 |
| 0.0167 | 8.0 | 18480 | 0.1423 | 0.8147 | 0.8376 | 0.8260 | 0.9742 |
| 0.0134 | 9.0 | 20790 | 0.1520 | 0.8259 | 0.8308 | 0.8284 | 0.9745 |
| 0.0097 | 10.0 | 23100 | 0.1627 | 0.8226 | 0.8377 | 0.8300 | 0.9745 |
| 0.0084 | 11.0 | 25410 | 0.1693 | 0.8336 | 0.8360 | 0.8348 | 0.9753 |
| 0.0066 | 12.0 | 27720 | 0.1744 | 0.8317 | 0.8359 | 0.8338 | 0.9751 |
| 0.0053 | 13.0 | 30030 | 0.1764 | 0.8247 | 0.8409 | 0.8327 | 0.9750 |
| 0.004 | 14.0 | 32340 | 0.1797 | 0.8280 | 0.8378 | 0.8328 | 0.9751 |
| 0.004 | 15.0 | 34650 | 0.1809 | 0.8310 | 0.8382 | 0.8346 | 0.9754 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
## Citation
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-ontonotesv5
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [ontonotes5-persian](https://huggingface.co/datasets/Amir13/ontonotes5-persian) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Precision: 0.8336
- Recall: 0.8360
- F1: 0.8348
- Accuracy: 0.9753
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1145 | 1.0 | 2310 | 0.1174 | 0.7717 | 0.7950 | 0.7832 | 0.9697 |
| 0.0793 | 2.0 | 4620 | 0.1084 | 0.8129 | 0.8108 | 0.8118 | 0.9729 |
| 0.0627 | 3.0 | 6930 | 0.1078 | 0.8227 | 0.8102 | 0.8164 | 0.9735 |
| 0.047 | 4.0 | 9240 | 0.1132 | 0.8105 | 0.8223 | 0.8164 | 0.9731 |
| 0.0347 | 5.0 | 11550 | 0.1190 | 0.8185 | 0.8315 | 0.8250 | 0.9742 |
| 0.0274 | 6.0 | 13860 | 0.1282 | 0.8088 | 0.8387 | 0.8235 | 0.9734 |
| 0.0202 | 7.0 | 16170 | 0.1329 | 0.8219 | 0.8354 | 0.8286 | 0.9745 |
| 0.0167 | 8.0 | 18480 | 0.1423 | 0.8147 | 0.8376 | 0.8260 | 0.9742 |
| 0.0134 | 9.0 | 20790 | 0.1520 | 0.8259 | 0.8308 | 0.8284 | 0.9745 |
| 0.0097 | 10.0 | 23100 | 0.1627 | 0.8226 | 0.8377 | 0.8300 | 0.9745 |
| 0.0084 | 11.0 | 25410 | 0.1693 | 0.8336 | 0.8360 | 0.8348 | 0.9753 |
| 0.0066 | 12.0 | 27720 | 0.1744 | 0.8317 | 0.8359 | 0.8338 | 0.9751 |
| 0.0053 | 13.0 | 30030 | 0.1764 | 0.8247 | 0.8409 | 0.8327 | 0.9750 |
| 0.004 | 14.0 | 32340 | 0.1797 | 0.8280 | 0.8378 | 0.8328 | 0.9751 |
| 0.004 | 15.0 | 34650 | 0.1809 | 0.8310 | 0.8382 | 0.8346 | 0.9754 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
## Citation
If you used the datasets and models in this repository, please cite it.
```bibtex
@misc{https://doi.org/10.48550/arxiv.2302.09611,
doi = {10.48550/ARXIV.2302.09611},
url = {https://arxiv.org/abs/2302.09611},
author = {Sartipi, Amir and Fatemi, Afsaneh},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English},
publisher = {arXiv},
year = {2023},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
{"datasets": "Amir13/ontonotes5-persian", "license": "mit", "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "xlm-roberta-base-ontonotesv5", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 42,011 |
TransferGraph/JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-ag_news
|
TransferGraph
|
text-classification
|
[
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:ag_news",
"base_model:JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset",
"base_model:adapter:JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset",
"model-index",
"region:us"
] | 2024-02-27T23:13:52Z |
2024-02-28T00:54:26+00:00
| 0 | 0 |
---
base_model: JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset
datasets:
- ag_news
library_name: peft
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-ag_news
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- type: accuracy
value: 0.25
name: accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-ag_news
This model is a fine-tuned version of [JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset](https://huggingface.co/JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset) on the ag_news dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.25
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.25 | None | 0 |
| 0.25 | 1.3863 | 0 |
| 0.25 | 1.3863 | 1 |
| 0.25 | 1.3863 | 2 |
| 0.25 | 1.3863 | 3 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-ag_news
This model is a fine-tuned version of [JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset](https://huggingface.co/JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset) on the ag_news dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.25
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.25 | None | 0 |
| 0.25 | 1.3863 | 0 |
| 0.25 | 1.3863 | 1 |
| 0.25 | 1.3863 | 2 |
| 0.25 | 1.3863 | 3 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2
|
{"base_model": "JNK789/distilbert-base-uncased-finetuned-tweets-emoji-dataset", "datasets": ["ag_news"], "library_name": "peft", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "JNK789_distilbert-base-uncased-finetuned-tweets-emoji-dataset-finetuned-lora-ag_news", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ag_news", "type": "ag_news", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.25, "name": "accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,012 |
zbigi/bart-base-summarization-medical_on_cnn-45
|
zbigi
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:adapter:facebook/bart-base",
"license:apache-2.0",
"region:us"
] | 2024-07-25T13:12:43Z |
2024-07-25T14:56:49+00:00
| 1 | 0 |
---
base_model: facebook/bart-base
library_name: peft
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: bart-base-summarization-medical_on_cnn-45
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-summarization-medical_on_cnn-45
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3819
- Rouge1: 0.2543
- Rouge2: 0.0961
- Rougel: 0.2009
- Rougelsum: 0.2262
- Gen Len: 18.613
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 45
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.6997 | 1.0 | 1250 | 3.3755 | 0.2507 | 0.0879 | 0.1956 | 0.2199 | 19.0 |
| 2.5999 | 2.0 | 2500 | 3.3728 | 0.2478 | 0.0901 | 0.1965 | 0.22 | 18.974 |
| 2.5783 | 3.0 | 3750 | 3.3670 | 0.2532 | 0.0929 | 0.199 | 0.2243 | 18.781 |
| 2.5498 | 4.0 | 5000 | 3.3812 | 0.2541 | 0.0947 | 0.2005 | 0.2253 | 18.681 |
| 2.5358 | 5.0 | 6250 | 3.3795 | 0.254 | 0.095 | 0.2006 | 0.2252 | 18.677 |
| 2.5296 | 6.0 | 7500 | 3.3819 | 0.2543 | 0.0961 | 0.2009 | 0.2262 | 18.613 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
| null |
BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-summarization-medical_on_cnn-45
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3819
- Rouge1: 0.2543
- Rouge2: 0.0961
- Rougel: 0.2009
- Rougelsum: 0.2262
- Gen Len: 18.613
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 45
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.6997 | 1.0 | 1250 | 3.3755 | 0.2507 | 0.0879 | 0.1956 | 0.2199 | 19.0 |
| 2.5999 | 2.0 | 2500 | 3.3728 | 0.2478 | 0.0901 | 0.1965 | 0.22 | 18.974 |
| 2.5783 | 3.0 | 3750 | 3.3670 | 0.2532 | 0.0929 | 0.199 | 0.2243 | 18.781 |
| 2.5498 | 4.0 | 5000 | 3.3812 | 0.2541 | 0.0947 | 0.2005 | 0.2253 | 18.681 |
| 2.5358 | 5.0 | 6250 | 3.3795 | 0.254 | 0.095 | 0.2006 | 0.2252 | 18.677 |
| 2.5296 | 6.0 | 7500 | 3.3819 | 0.2543 | 0.0961 | 0.2009 | 0.2262 | 18.613 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
{"base_model": "facebook/bart-base", "library_name": "peft", "license": "apache-2.0", "metrics": ["rouge"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-summarization-medical_on_cnn-45", "results": []}]}
|
task
|
[
"SUMMARIZATION"
] | 42,014 |
mrapacz/interlinear-pl-greta-emb-sum-normalized-ob
|
mrapacz
|
text2text-generation
|
[
"transformers",
"pytorch",
"morph-t5-sum",
"text2text-generation",
"pl",
"dataset:mrapacz/greek-interlinear-translations",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-02-08T12:27:08Z |
2025-02-21T21:31:04+00:00
| 13 | 0 |
---
base_model:
- GreTa
datasets:
- mrapacz/greek-interlinear-translations
language:
- pl
library_name: transformers
license: cc-by-sa-4.0
metrics:
- bleu
---
# Model Card for Ancient Greek to Polish Interlinear Translation Model
This model performs interlinear translation from Ancient Greek to Polish, maintaining word-level alignment between source and target texts.
You can find the source code used for training this and other models trained as part of this project in the [GitHub repository](https://github.com/mrapacz/loreslm-interlinear-translation).
## Model Details
### Model Description
- **Developed By:** Maciej Rapacz, AGH University of Kraków
- **Model Type:** MorphT5SumForConditionalGeneration
- **Base Model:** GreTa
- **Tokenizer:** GreTa
- **Language(s):** Ancient Greek (source) → Polish (target)
- **License:** CC BY-NC-SA 4.0
- **Tag Set:** OB (Oblubienica)
- **Text Preprocessing:** Normalized
- **Morphological Encoding:** emb-sum
### Model Performance
- **BLEU Score:** 32.92
- **SemScore:** 0.87
### Model Sources
- **Repository:** https://github.com/mrapacz/loreslm-interlinear-translation
- **Paper:** https://aclanthology.org/2025.loreslm-1.11/
## Usage Example
> **Note**: This model uses a modification of T5-family models that includes dedicated embedding layers for encoding morphological information. To load these models, install the [morpht5](https://github.com/mrapacz/loreslm-interlinear-translation/blob/master/morpht5/README.md) package:
> ```bash
> pip install morpht5
> ```
```python
>>> from morpht5 import MorphT5SumForConditionalGeneration, MorphT5Tokenizer
>>> text = ['λεγει', 'αυτω', 'ο', 'ιησους', 'εγειρε', 'αρον', 'τον', 'κραβαττον', 'σου', 'και', 'περιπατει']
>>> tags = ['vi Pres Act 3 Sg', 'pp Dat Sg m', 't_ Nom Sg m', 'n_ Nom Sg m', 'vm Pres Act 2 Sg', 'vm Aor Act 2 Sg', 't_ Acc Sg m', 'n_ Acc Sg m', 'pp 2 Gen Sg', 'Conj', 'vm Pres Act 2 Sg']
>>> tokenizer = MorphT5Tokenizer.from_pretrained("mrapacz/interlinear-pl-greta-emb-sum-normalized-ob")
>>> inputs = tokenizer(
text=text,
morph_tags=tags,
return_tensors="pt"
)
>>> model = MorphT5SumForConditionalGeneration.from_pretrained("mrapacz/interlinear-pl-greta-emb-sum-normalized-ob")
>>> outputs = model.generate(
**inputs,
max_new_tokens=100,
early_stopping=True,
)
>>> decoded = tokenizer.decode(outputs[0], skip_special_tokens=True, keep_block_separator=True)
>>> decoded = decoded.replace(tokenizer.target_block_separator_token, " | ")
>>> decoded
'mówi | mu | - | jezus | wstawaj | we | - | mat | swoj | i | chod'
```
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{rapacz-smywinski-pohl-2025-low,
title = "Low-Resource Interlinear Translation: Morphology-Enhanced Neural Models for {A}ncient {G}reek",
author = "Rapacz, Maciej and
Smywi{\'n}ski-Pohl, Aleksander",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the First Workshop on Language Models for Low-Resource Languages",
month = jan,
year = "2025",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.loreslm-1.11/",
pages = "145--165",
abstract = "Contemporary machine translation systems prioritize fluent, natural-sounding output with flexible word ordering. In contrast, interlinear translation maintains the source text`s syntactic structure by aligning target language words directly beneath their source counterparts. Despite its importance in classical scholarship, automated approaches to interlinear translation remain understudied. We evaluated neural interlinear translation from Ancient Greek to English and Polish using four transformer-based models: two Ancient Greek-specialized (GreTa and PhilTa) and two general-purpose multilingual models (mT5-base and mT5-large). Our approach introduces novel morphological embedding layers and evaluates text preprocessing and tag set selection across 144 experimental configurations using a word-aligned parallel corpus of the Greek New Testament. Results show that morphological features through dedicated embedding layers significantly enhance translation quality, improving BLEU scores by 35{\%} (44.67 {\textrightarrow} 60.40) for English and 38{\%} (42.92 {\textrightarrow} 59.33) for Polish compared to baseline models. PhilTa achieves state-of-the-art performance for English, while mT5-large does so for Polish. Notably, PhilTa maintains stable performance using only 10{\%} of training data. Our findings challenge the assumption that modern neural architectures cannot benefit from explicit morphological annotations. While preprocessing strategies and tag set selection show minimal impact, the substantial gains from morphological embeddings demonstrate their value in low-resource scenarios."
}
```
| null |
Non_BioNLP
|
# Model Card for Ancient Greek to Polish Interlinear Translation Model
This model performs interlinear translation from Ancient Greek to Polish, maintaining word-level alignment between source and target texts.
You can find the source code used for training this and other models trained as part of this project in the [GitHub repository](https://github.com/mrapacz/loreslm-interlinear-translation).
## Model Details
### Model Description
- **Developed By:** Maciej Rapacz, AGH University of Kraków
- **Model Type:** MorphT5SumForConditionalGeneration
- **Base Model:** GreTa
- **Tokenizer:** GreTa
- **Language(s):** Ancient Greek (source) → Polish (target)
- **License:** CC BY-NC-SA 4.0
- **Tag Set:** OB (Oblubienica)
- **Text Preprocessing:** Normalized
- **Morphological Encoding:** emb-sum
### Model Performance
- **BLEU Score:** 32.92
- **SemScore:** 0.87
### Model Sources
- **Repository:** https://github.com/mrapacz/loreslm-interlinear-translation
- **Paper:** https://aclanthology.org/2025.loreslm-1.11/
## Usage Example
> **Note**: This model uses a modification of T5-family models that includes dedicated embedding layers for encoding morphological information. To load these models, install the [morpht5](https://github.com/mrapacz/loreslm-interlinear-translation/blob/master/morpht5/README.md) package:
> ```bash
> pip install morpht5
> ```
```python
>>> from morpht5 import MorphT5SumForConditionalGeneration, MorphT5Tokenizer
>>> text = ['λεγει', 'αυτω', 'ο', 'ιησους', 'εγειρε', 'αρον', 'τον', 'κραβαττον', 'σου', 'και', 'περιπατει']
>>> tags = ['vi Pres Act 3 Sg', 'pp Dat Sg m', 't_ Nom Sg m', 'n_ Nom Sg m', 'vm Pres Act 2 Sg', 'vm Aor Act 2 Sg', 't_ Acc Sg m', 'n_ Acc Sg m', 'pp 2 Gen Sg', 'Conj', 'vm Pres Act 2 Sg']
>>> tokenizer = MorphT5Tokenizer.from_pretrained("mrapacz/interlinear-pl-greta-emb-sum-normalized-ob")
>>> inputs = tokenizer(
text=text,
morph_tags=tags,
return_tensors="pt"
)
>>> model = MorphT5SumForConditionalGeneration.from_pretrained("mrapacz/interlinear-pl-greta-emb-sum-normalized-ob")
>>> outputs = model.generate(
**inputs,
max_new_tokens=100,
early_stopping=True,
)
>>> decoded = tokenizer.decode(outputs[0], skip_special_tokens=True, keep_block_separator=True)
>>> decoded = decoded.replace(tokenizer.target_block_separator_token, " | ")
>>> decoded
'mówi | mu | - | jezus | wstawaj | we | - | mat | swoj | i | chod'
```
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{rapacz-smywinski-pohl-2025-low,
title = "Low-Resource Interlinear Translation: Morphology-Enhanced Neural Models for {A}ncient {G}reek",
author = "Rapacz, Maciej and
Smywi{\'n}ski-Pohl, Aleksander",
editor = "Hettiarachchi, Hansi and
Ranasinghe, Tharindu and
Rayson, Paul and
Mitkov, Ruslan and
Gaber, Mohamed and
Premasiri, Damith and
Tan, Fiona Anting and
Uyangodage, Lasitha",
booktitle = "Proceedings of the First Workshop on Language Models for Low-Resource Languages",
month = jan,
year = "2025",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.loreslm-1.11/",
pages = "145--165",
abstract = "Contemporary machine translation systems prioritize fluent, natural-sounding output with flexible word ordering. In contrast, interlinear translation maintains the source text`s syntactic structure by aligning target language words directly beneath their source counterparts. Despite its importance in classical scholarship, automated approaches to interlinear translation remain understudied. We evaluated neural interlinear translation from Ancient Greek to English and Polish using four transformer-based models: two Ancient Greek-specialized (GreTa and PhilTa) and two general-purpose multilingual models (mT5-base and mT5-large). Our approach introduces novel morphological embedding layers and evaluates text preprocessing and tag set selection across 144 experimental configurations using a word-aligned parallel corpus of the Greek New Testament. Results show that morphological features through dedicated embedding layers significantly enhance translation quality, improving BLEU scores by 35{\%} (44.67 {\textrightarrow} 60.40) for English and 38{\%} (42.92 {\textrightarrow} 59.33) for Polish compared to baseline models. PhilTa achieves state-of-the-art performance for English, while mT5-large does so for Polish. Notably, PhilTa maintains stable performance using only 10{\%} of training data. Our findings challenge the assumption that modern neural architectures cannot benefit from explicit morphological annotations. While preprocessing strategies and tag set selection show minimal impact, the substantial gains from morphological embeddings demonstrate their value in low-resource scenarios."
}
```
|
{"base_model": ["GreTa"], "datasets": ["mrapacz/greek-interlinear-translations"], "language": ["pl"], "library_name": "transformers", "license": "cc-by-sa-4.0", "metrics": ["bleu"]}
|
task
|
[
"TRANSLATION"
] | 42,015 |
Helsinki-NLP/opus-mt-tc-base-uk-fi
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"marian",
"text2text-generation",
"translation",
"opus-mt-tc",
"fi",
"uk",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-24T06:20:21Z |
2023-10-10T21:38:06+00:00
| 13 | 0 |
---
language:
- fi
- uk
license: cc-by-4.0
tags:
- translation
- opus-mt-tc
model-index:
- name: opus-mt-tc-base-uk-fi
results:
- task:
type: translation
name: Translation ukr-fin
dataset:
name: flores101-devtest
type: flores_101
args: ukr fin devtest
metrics:
- type: bleu
value: 19.6
name: BLEU
---
# opus-mt-tc-base-uk-fi
Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-17
* source language(s): ukr
* target language(s): fin
* model: transformer-align
* data: opusTCv20210807+pft+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip)
* more information released models: [OPUS-MT ukr-fin README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-fin/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Африка є колискою людства.",
"Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять."
]
model_name = "pytorch-models/opus-mt-tc-base-uk-fi"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Afrikka on ihmiskunnan kehto.
# Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-fi")
print(pipe("Африка є колискою людства."))
# expected output: Afrikka on ihmiskunnan kehto.
```
## Benchmarks
* test set translations: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt)
* test set scores: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ukr-fin | flores101-devtest | 0.54827 | 19.6 | 1012 | 18781 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 09:10:42 EET 2022
* port machine: LM0-400-22516.local
| null |
Non_BioNLP
|
# opus-mt-tc-base-uk-fi
Neural machine translation model for translating from Ukrainian (uk) to Finnish (fi).
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
```
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
```
## Model info
* Release: 2022-03-17
* source language(s): ukr
* target language(s): fin
* model: transformer-align
* data: opusTCv20210807+pft+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.zip)
* more information released models: [OPUS-MT ukr-fin README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-fin/README.md)
## Usage
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Африка є колискою людства.",
"Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять."
]
model_name = "pytorch-models/opus-mt-tc-base-uk-fi"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
# expected output:
# Afrikka on ihmiskunnan kehto.
# Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen.
```
You can also use OPUS-MT models with the transformers pipelines, for example:
```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-uk-fi")
print(pipe("Африка є колискою людства."))
# expected output: Afrikka on ihmiskunnan kehto.
```
## Benchmarks
* test set translations: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.test.txt)
* test set scores: [opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-fin/opusTCv20210807+pft+pbt_transformer-align_2022-03-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)
| langpair | testset | chr-F | BLEU | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| ukr-fin | flores101-devtest | 0.54827 | 19.6 | 1012 | 18781 |
## Acknowledgements
The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
## Model conversion info
* transformers version: 4.16.2
* OPUS-MT git hash: 1bdabf7
* port time: Thu Mar 24 09:10:42 EET 2022
* port machine: LM0-400-22516.local
|
{"language": ["fi", "uk"], "license": "cc-by-4.0", "tags": ["translation", "opus-mt-tc"], "model-index": [{"name": "opus-mt-tc-base-uk-fi", "results": [{"task": {"type": "translation", "name": "Translation ukr-fin"}, "dataset": {"name": "flores101-devtest", "type": "flores_101", "args": "ukr fin devtest"}, "metrics": [{"type": "bleu", "value": 19.6, "name": "BLEU"}]}]}]}
|
task
|
[
"TRANSLATION"
] | 42,016 |
Helsinki-NLP/opus-mt-sv-crs
|
Helsinki-NLP
|
translation
|
[
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"sv",
"crs",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04Z |
2023-08-16T12:04:55+00:00
| 34 | 0 |
---
license: apache-2.0
tags:
- translation
---
### opus-mt-sv-crs
* source languages: sv
* target languages: crs
* OPUS readme: [sv-crs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-crs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.crs | 32.4 | 0.512 |
| null |
Non_BioNLP
|
### opus-mt-sv-crs
* source languages: sv
* target languages: crs
* OPUS readme: [sv-crs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-crs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.crs | 32.4 | 0.512 |
|
{"license": "apache-2.0", "tags": ["translation"]}
|
task
|
[
"TRANSLATION"
] | 42,017 |
sssingh/distilbert-base-uncased-emotion-finetuned
|
sssingh
|
text-classification
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-07-10T09:17:12Z |
2022-07-16T08:15:11+00:00
| 116 | 0 |
---
datasets:
- emotion
license: apache-2.0
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-emotion-finetuned
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: f1
value: 0.9350215566385567
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-emotion-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1518
- Acc: 0.935
- F1: 0.9350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:------:|
| 0.1734 | 1.0 | 250 | 0.1624 | 0.928 | 0.9279 |
| 0.1187 | 2.0 | 500 | 0.1518 | 0.935 | 0.9350 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-emotion-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1518
- Acc: 0.935
- F1: 0.9350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:------:|
| 0.1734 | 1.0 | 250 | 0.1624 | 0.928 | 0.9279 |
| 0.1187 | 2.0 | 500 | 0.1518 | 0.935 | 0.9350 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
{"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-emotion-finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "f1", "value": 0.9350215566385567, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,018 |
antoinelouis/camembert-L2
|
antoinelouis
|
feature-extraction
|
[
"transformers",
"pytorch",
"safetensors",
"camembert",
"feature-extraction",
"fr",
"license:mit",
"text-embeddings-inference",
"region:us"
] | 2023-10-10T13:41:38Z |
2024-05-02T08:27:08+00:00
| 27 | 0 |
---
language:
- fr
library_name: transformers
license: mit
pipeline_tag: feature-extraction
inference: false
---
# CamemBERT-L2
This model is a pruned version of the pre-trained [CamemBERT](https://huggingface.co/camembert-base) checkpoint, obtained by [dropping the top-layers](https://doi.org/10.48550/arXiv.2004.03844) from the original model.
## Usage
You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search. For tasks such as text generation, you should look at autoregressive models like [BelGPT-2](https://huggingface.co/antoinelouis/belgpt2).
You can use this model directly with a pipeline for [masked language modeling](https://huggingface.co/tasks/fill-mask):
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='antoinelouis/camembert-L2')
unmasker("Bonjour, je suis un [MASK] modèle.")
```
You can also use this model to [extract the features](https://huggingface.co/tasks/feature-extraction) of a given text:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/camembert-L2')
model = AutoModel.from_pretrained('antoinelouis/camembert-L2')
text = "Remplacez-moi par le texte de votre choix."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Variations
CamemBERT has originally been released in base (110M) and large (335M) variations. The following checkpoints prune the base variation by dropping the top 2, 4, 6, 8, and 10 pretrained encoding layers, respectively.
| Model | #Params | Size | Pruning |
|--------------------------------------------------------------------|:-------:|:-----:|:-------:|
| [CamemBERT-base](https://huggingface.co/camembert-base) | 110.6M | 445MB | - |
| | | | |
| [CamemBERT-L10](https://huggingface.co/antoinelouis/camembert-L10) | 96.4M | 386MB | -13% |
| [CamemBERT-L8](https://huggingface.co/antoinelouis/camembert-L8) | 82.3M | 329MB | -26% |
| [CamemBERT-L6](https://huggingface.co/antoinelouis/camembert-L6) | 68.1M | 272MB | -38% |
| [CamemBERT-L4](https://huggingface.co/antoinelouis/camembert-L4) | 53.9M | 216MB | -51% |
| **CamemBERT-L2** | 39.7M | 159MB | -64% |
| null |
Non_BioNLP
|
# CamemBERT-L2
This model is a pruned version of the pre-trained [CamemBERT](https://huggingface.co/camembert-base) checkpoint, obtained by [dropping the top-layers](https://doi.org/10.48550/arXiv.2004.03844) from the original model.
## Usage
You can use the raw model for masked language modeling (MLM), but it's mostly intended to be fine-tuned on a downstream task, especially one that uses the whole sentence to make decisions such as text classification, extractive question answering, or semantic search. For tasks such as text generation, you should look at autoregressive models like [BelGPT-2](https://huggingface.co/antoinelouis/belgpt2).
You can use this model directly with a pipeline for [masked language modeling](https://huggingface.co/tasks/fill-mask):
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='antoinelouis/camembert-L2')
unmasker("Bonjour, je suis un [MASK] modèle.")
```
You can also use this model to [extract the features](https://huggingface.co/tasks/feature-extraction) of a given text:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/camembert-L2')
model = AutoModel.from_pretrained('antoinelouis/camembert-L2')
text = "Remplacez-moi par le texte de votre choix."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Variations
CamemBERT has originally been released in base (110M) and large (335M) variations. The following checkpoints prune the base variation by dropping the top 2, 4, 6, 8, and 10 pretrained encoding layers, respectively.
| Model | #Params | Size | Pruning |
|--------------------------------------------------------------------|:-------:|:-----:|:-------:|
| [CamemBERT-base](https://huggingface.co/camembert-base) | 110.6M | 445MB | - |
| | | | |
| [CamemBERT-L10](https://huggingface.co/antoinelouis/camembert-L10) | 96.4M | 386MB | -13% |
| [CamemBERT-L8](https://huggingface.co/antoinelouis/camembert-L8) | 82.3M | 329MB | -26% |
| [CamemBERT-L6](https://huggingface.co/antoinelouis/camembert-L6) | 68.1M | 272MB | -38% |
| [CamemBERT-L4](https://huggingface.co/antoinelouis/camembert-L4) | 53.9M | 216MB | -51% |
| **CamemBERT-L2** | 39.7M | 159MB | -64% |
|
{"language": ["fr"], "library_name": "transformers", "license": "mit", "pipeline_tag": "feature-extraction", "inference": false}
|
task
|
[
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING"
] | 42,019 |
dq158/morbius
|
dq158
|
text2text-generation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-02T01:54:31Z |
2023-10-30T04:38:38+00:00
| 150 | 0 |
---
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: morbius
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# morbius
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3311
- Bleu: 0.0490
- Precisions: [0.12658339197748064, 0.058000714881448825, 0.031020853918560506, 0.0276665140764477]
- Brevity Penalty: 0.9781
- Length Ratio: 0.9783
- Translation Length: 45472
- Reference Length: 46479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| 2.6085 | 1.0 | 2630 | 2.3793 | 0.0398 | [0.11484440108136675, 0.05086452177719413, 0.022402389588222743, 0.019262093750807972] | 1.0 | 1.0585 | 49197 | 46479 |
| 2.5537 | 2.0 | 5260 | 2.3538 | 0.0451 | [0.12435074854873206, 0.053338059789672695, 0.02736549165120594, 0.024163621427155037] | 0.9858 | 0.9859 | 45822 | 46479 |
| 2.427 | 3.0 | 7890 | 2.3412 | 0.0478 | [0.12566410537870473, 0.05610922151130985, 0.029971974257836827, 0.026891236083357122] | 0.9798 | 0.9800 | 45550 | 46479 |
| 2.3716 | 4.0 | 10520 | 2.3347 | 0.0487 | [0.12663965838169275, 0.0574505431946487, 0.030477866031926728, 0.027230821761893922] | 0.9823 | 0.9825 | 45665 | 46479 |
| 2.3494 | 5.0 | 13150 | 2.3311 | 0.0490 | [0.12658339197748064, 0.058000714881448825, 0.031020853918560506, 0.0276665140764477] | 0.9781 | 0.9783 | 45472 | 46479 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# morbius
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3311
- Bleu: 0.0490
- Precisions: [0.12658339197748064, 0.058000714881448825, 0.031020853918560506, 0.0276665140764477]
- Brevity Penalty: 0.9781
- Length Ratio: 0.9783
- Translation Length: 45472
- Reference Length: 46479
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| 2.6085 | 1.0 | 2630 | 2.3793 | 0.0398 | [0.11484440108136675, 0.05086452177719413, 0.022402389588222743, 0.019262093750807972] | 1.0 | 1.0585 | 49197 | 46479 |
| 2.5537 | 2.0 | 5260 | 2.3538 | 0.0451 | [0.12435074854873206, 0.053338059789672695, 0.02736549165120594, 0.024163621427155037] | 0.9858 | 0.9859 | 45822 | 46479 |
| 2.427 | 3.0 | 7890 | 2.3412 | 0.0478 | [0.12566410537870473, 0.05610922151130985, 0.029971974257836827, 0.026891236083357122] | 0.9798 | 0.9800 | 45550 | 46479 |
| 2.3716 | 4.0 | 10520 | 2.3347 | 0.0487 | [0.12663965838169275, 0.0574505431946487, 0.030477866031926728, 0.027230821761893922] | 0.9823 | 0.9825 | 45665 | 46479 |
| 2.3494 | 5.0 | 13150 | 2.3311 | 0.0490 | [0.12658339197748064, 0.058000714881448825, 0.031020853918560506, 0.0276665140764477] | 0.9781 | 0.9783 | 45472 | 46479 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
{"metrics": ["bleu"], "tags": ["generated_from_trainer"], "model-index": [{"name": "morbius", "results": []}]}
|
task
|
[
"TRANSLATION"
] | 42,020 |
YakovElm/Hyperledger15SetFitModel_balance_ratio_2
|
YakovElm
|
text-classification
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2023-06-01T15:36:03Z |
2023-06-01T15:36:37+00:00
| 10 | 0 |
---
license: apache-2.0
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
---
# YakovElm/Hyperledger15SetFitModel_balance_ratio_2
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("YakovElm/Hyperledger15SetFitModel_balance_ratio_2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
| null |
Non_BioNLP
|
# YakovElm/Hyperledger15SetFitModel_balance_ratio_2
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("YakovElm/Hyperledger15SetFitModel_balance_ratio_2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
{"license": "apache-2.0", "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification"]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,021 |
srikarvar/fine_tuned_model_17
|
srikarvar
|
sentence-similarity
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2752",
"loss:OnlineContrastiveLoss",
"arxiv:1908.10084",
"base_model:intfloat/multilingual-e5-small",
"base_model:finetune:intfloat/multilingual-e5-small",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-16T14:06:46Z |
2024-09-16T14:07:08+00:00
| 5 | 0 |
---
base_model: intfloat/multilingual-e5-small
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2752
- loss:OnlineContrastiveLoss
widget:
- source_sentence: Would you want to be President?
sentences:
- Can you help me with my homework?
- How to bake cookies?
- Why do you want to be to president?
- source_sentence: Velocity of sound waves in the atmosphere
sentences:
- What is the speed of sound in air?
- What is the best/most memorable thing you've ever eaten and why?
- The `safe` option in the `to_spreadsheet` method controls whether a safe conversion
or not is needed for certain plant attributes to store the data in a SpreadsheetTable
or Row.
- source_sentence: Number of countries in the European Union
sentences:
- How many countries are in the European Union?
- Who painted the Sistine Chapel ceiling?
- The RecipeManager class is used to manage the downloading and extraction of recipes.
- source_sentence: Official currency of the USA
sentences:
- What is purpose of life?
- Files inside ZIP archives are accessed and yielded sequentially using iter_zip().
- What is the currency of the United States?
- source_sentence: Who wrote the book "1984"?
sentences:
- What is the speed of light?
- How to set up a home gym?
- Who wrote the book "To Kill a Mockingbird"?
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-small
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: pair class dev
type: pair-class-dev
metrics:
- type: cosine_accuracy
value: 0.9456521739130435
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8053532838821411
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9554896142433236
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8053532838821411
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.92
name: Cosine Precision
- type: cosine_recall
value: 0.9938271604938271
name: Cosine Recall
- type: cosine_ap
value: 0.970102365862799
name: Cosine Ap
- type: dot_accuracy
value: 0.9456521739130435
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.8053532838821411
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9554896142433236
name: Dot F1
- type: dot_f1_threshold
value: 0.8053532838821411
name: Dot F1 Threshold
- type: dot_precision
value: 0.92
name: Dot Precision
- type: dot_recall
value: 0.9938271604938271
name: Dot Recall
- type: dot_ap
value: 0.970102365862799
name: Dot Ap
- type: manhattan_accuracy
value: 0.9456521739130435
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 9.787351608276367
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.9554896142433236
name: Manhattan F1
- type: manhattan_f1_threshold
value: 9.787351608276367
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.92
name: Manhattan Precision
- type: manhattan_recall
value: 0.9938271604938271
name: Manhattan Recall
- type: manhattan_ap
value: 0.9698493258522533
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9456521739130435
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.6239285469055176
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9554896142433236
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.6239285469055176
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.92
name: Euclidean Precision
- type: euclidean_recall
value: 0.9938271604938271
name: Euclidean Recall
- type: euclidean_ap
value: 0.970102365862799
name: Euclidean Ap
- type: max_accuracy
value: 0.9456521739130435
name: Max Accuracy
- type: max_accuracy_threshold
value: 9.787351608276367
name: Max Accuracy Threshold
- type: max_f1
value: 0.9554896142433236
name: Max F1
- type: max_f1_threshold
value: 9.787351608276367
name: Max F1 Threshold
- type: max_precision
value: 0.92
name: Max Precision
- type: max_recall
value: 0.9938271604938271
name: Max Recall
- type: max_ap
value: 0.970102365862799
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: pair class test
type: pair-class-test
metrics:
- type: cosine_accuracy
value: 0.9456521739130435
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8053532838821411
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9554896142433236
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8053532838821411
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.92
name: Cosine Precision
- type: cosine_recall
value: 0.9938271604938271
name: Cosine Recall
- type: cosine_ap
value: 0.970102365862799
name: Cosine Ap
- type: dot_accuracy
value: 0.9456521739130435
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 0.8053532838821411
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.9554896142433236
name: Dot F1
- type: dot_f1_threshold
value: 0.8053532838821411
name: Dot F1 Threshold
- type: dot_precision
value: 0.92
name: Dot Precision
- type: dot_recall
value: 0.9938271604938271
name: Dot Recall
- type: dot_ap
value: 0.970102365862799
name: Dot Ap
- type: manhattan_accuracy
value: 0.9456521739130435
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 9.787351608276367
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.9554896142433236
name: Manhattan F1
- type: manhattan_f1_threshold
value: 9.787351608276367
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.92
name: Manhattan Precision
- type: manhattan_recall
value: 0.9938271604938271
name: Manhattan Recall
- type: manhattan_ap
value: 0.9698493258522533
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.9456521739130435
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 0.6239285469055176
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.9554896142433236
name: Euclidean F1
- type: euclidean_f1_threshold
value: 0.6239285469055176
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.92
name: Euclidean Precision
- type: euclidean_recall
value: 0.9938271604938271
name: Euclidean Recall
- type: euclidean_ap
value: 0.970102365862799
name: Euclidean Ap
- type: max_accuracy
value: 0.9456521739130435
name: Max Accuracy
- type: max_accuracy_threshold
value: 9.787351608276367
name: Max Accuracy Threshold
- type: max_f1
value: 0.9554896142433236
name: Max F1
- type: max_f1_threshold
value: 9.787351608276367
name: Max F1 Threshold
- type: max_precision
value: 0.92
name: Max Precision
- type: max_recall
value: 0.9938271604938271
name: Max Recall
- type: max_ap
value: 0.970102365862799
name: Max Ap
---
# SentenceTransformer based on intfloat/multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("srikarvar/fine_tuned_model_17")
# Run inference
sentences = [
'Who wrote the book "1984"?',
'Who wrote the book "To Kill a Mockingbird"?',
'What is the speed of light?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `pair-class-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9457 |
| cosine_accuracy_threshold | 0.8054 |
| cosine_f1 | 0.9555 |
| cosine_f1_threshold | 0.8054 |
| cosine_precision | 0.92 |
| cosine_recall | 0.9938 |
| cosine_ap | 0.9701 |
| dot_accuracy | 0.9457 |
| dot_accuracy_threshold | 0.8054 |
| dot_f1 | 0.9555 |
| dot_f1_threshold | 0.8054 |
| dot_precision | 0.92 |
| dot_recall | 0.9938 |
| dot_ap | 0.9701 |
| manhattan_accuracy | 0.9457 |
| manhattan_accuracy_threshold | 9.7874 |
| manhattan_f1 | 0.9555 |
| manhattan_f1_threshold | 9.7874 |
| manhattan_precision | 0.92 |
| manhattan_recall | 0.9938 |
| manhattan_ap | 0.9698 |
| euclidean_accuracy | 0.9457 |
| euclidean_accuracy_threshold | 0.6239 |
| euclidean_f1 | 0.9555 |
| euclidean_f1_threshold | 0.6239 |
| euclidean_precision | 0.92 |
| euclidean_recall | 0.9938 |
| euclidean_ap | 0.9701 |
| max_accuracy | 0.9457 |
| max_accuracy_threshold | 9.7874 |
| max_f1 | 0.9555 |
| max_f1_threshold | 9.7874 |
| max_precision | 0.92 |
| max_recall | 0.9938 |
| **max_ap** | **0.9701** |
#### Binary Classification
* Dataset: `pair-class-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9457 |
| cosine_accuracy_threshold | 0.8054 |
| cosine_f1 | 0.9555 |
| cosine_f1_threshold | 0.8054 |
| cosine_precision | 0.92 |
| cosine_recall | 0.9938 |
| cosine_ap | 0.9701 |
| dot_accuracy | 0.9457 |
| dot_accuracy_threshold | 0.8054 |
| dot_f1 | 0.9555 |
| dot_f1_threshold | 0.8054 |
| dot_precision | 0.92 |
| dot_recall | 0.9938 |
| dot_ap | 0.9701 |
| manhattan_accuracy | 0.9457 |
| manhattan_accuracy_threshold | 9.7874 |
| manhattan_f1 | 0.9555 |
| manhattan_f1_threshold | 9.7874 |
| manhattan_precision | 0.92 |
| manhattan_recall | 0.9938 |
| manhattan_ap | 0.9698 |
| euclidean_accuracy | 0.9457 |
| euclidean_accuracy_threshold | 0.6239 |
| euclidean_f1 | 0.9555 |
| euclidean_f1_threshold | 0.6239 |
| euclidean_precision | 0.92 |
| euclidean_recall | 0.9938 |
| euclidean_ap | 0.9701 |
| max_accuracy | 0.9457 |
| max_accuracy_threshold | 9.7874 |
| max_f1 | 0.9555 |
| max_f1_threshold | 9.7874 |
| max_precision | 0.92 |
| max_recall | 0.9938 |
| **max_ap** | **0.9701** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,752 training samples
* Columns: <code>sentence2</code>, <code>label</code>, and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence2 | label | sentence1 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | int | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 10.14 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~49.00%</li><li>1: ~51.00%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.77 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| sentence2 | label | sentence1 |
|:---------------------------------------------------|:---------------|:--------------------------------------------------|
| <code>What are the ingredients of pizza?</code> | <code>1</code> | <code>What are the ingredients of a pizza?</code> |
| <code>What are the ingredients of a burger?</code> | <code>0</code> | <code>What are the ingredients of a pizza?</code> |
| <code>How is photosynthesis carried out?</code> | <code>1</code> | <code>How does photosynthesis work?</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 276 evaluation samples
* Columns: <code>sentence2</code>, <code>label</code>, and <code>sentence1</code>
* Approximate statistics based on the first 276 samples:
| | sentence2 | label | sentence1 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | int | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.34 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>0: ~41.30%</li><li>1: ~58.70%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.56 tokens</li><li>max: 87 tokens</li></ul> |
* Samples:
| sentence2 | label | sentence1 |
|:---------------------------------------------------------------------------------------------------------------------------|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How is AI used to enhance cybersecurity?</code> | <code>0</code> | <code>What are the challenges of AI in cybersecurity?</code> |
| <code>The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version.</code> | <code>1</code> | <code>You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation.</code> |
| <code>Name the capital city of Italy</code> | <code>1</code> | <code>What is the capital of Italy?</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap |
|:----------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:|
| 0 | 0 | - | - | 0.7876 | - |
| 0.2326 | 10 | 1.5405 | - | - | - |
| 0.4651 | 20 | 1.0389 | - | - | - |
| 0.6977 | 30 | 1.2755 | - | - | - |
| 0.9302 | 40 | 0.7024 | - | - | - |
| 1.0 | 43 | - | 0.9673 | 0.9133 | - |
| 1.1512 | 50 | 0.7527 | - | - | - |
| 1.3837 | 60 | 0.6684 | - | - | - |
| 1.6163 | 70 | 0.7612 | - | - | - |
| 1.8488 | 80 | 0.7265 | - | - | - |
| 2.0116 | 87 | - | 0.4647 | 0.9534 | - |
| 2.0698 | 90 | 0.2986 | - | - | - |
| 2.3023 | 100 | 0.1964 | - | - | - |
| 2.5349 | 110 | 0.5834 | - | - | - |
| 2.7674 | 120 | 0.4893 | - | - | - |
| 3.0 | 130 | 0.1254 | 0.3544 | 0.9670 | - |
| 3.2209 | 140 | 0.278 | - | - | - |
| 3.4535 | 150 | 0.1805 | - | - | - |
| 3.6860 | 160 | 0.4525 | - | - | - |
| 3.9186 | 170 | 0.1885 | - | - | - |
| **3.9651** | **172** | **-** | **0.3396** | **0.9701** | **0.9701** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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*Clearly define terms in order to be accessible across audiences.*
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| null |
Non_BioNLP
|
# SentenceTransformer based on intfloat/multilingual-e5-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision fd1525a9fd15316a2d503bf26ab031a61d056e98 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("srikarvar/fine_tuned_model_17")
# Run inference
sentences = [
'Who wrote the book "1984"?',
'Who wrote the book "To Kill a Mockingbird"?',
'What is the speed of light?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `pair-class-dev`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9457 |
| cosine_accuracy_threshold | 0.8054 |
| cosine_f1 | 0.9555 |
| cosine_f1_threshold | 0.8054 |
| cosine_precision | 0.92 |
| cosine_recall | 0.9938 |
| cosine_ap | 0.9701 |
| dot_accuracy | 0.9457 |
| dot_accuracy_threshold | 0.8054 |
| dot_f1 | 0.9555 |
| dot_f1_threshold | 0.8054 |
| dot_precision | 0.92 |
| dot_recall | 0.9938 |
| dot_ap | 0.9701 |
| manhattan_accuracy | 0.9457 |
| manhattan_accuracy_threshold | 9.7874 |
| manhattan_f1 | 0.9555 |
| manhattan_f1_threshold | 9.7874 |
| manhattan_precision | 0.92 |
| manhattan_recall | 0.9938 |
| manhattan_ap | 0.9698 |
| euclidean_accuracy | 0.9457 |
| euclidean_accuracy_threshold | 0.6239 |
| euclidean_f1 | 0.9555 |
| euclidean_f1_threshold | 0.6239 |
| euclidean_precision | 0.92 |
| euclidean_recall | 0.9938 |
| euclidean_ap | 0.9701 |
| max_accuracy | 0.9457 |
| max_accuracy_threshold | 9.7874 |
| max_f1 | 0.9555 |
| max_f1_threshold | 9.7874 |
| max_precision | 0.92 |
| max_recall | 0.9938 |
| **max_ap** | **0.9701** |
#### Binary Classification
* Dataset: `pair-class-test`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.9457 |
| cosine_accuracy_threshold | 0.8054 |
| cosine_f1 | 0.9555 |
| cosine_f1_threshold | 0.8054 |
| cosine_precision | 0.92 |
| cosine_recall | 0.9938 |
| cosine_ap | 0.9701 |
| dot_accuracy | 0.9457 |
| dot_accuracy_threshold | 0.8054 |
| dot_f1 | 0.9555 |
| dot_f1_threshold | 0.8054 |
| dot_precision | 0.92 |
| dot_recall | 0.9938 |
| dot_ap | 0.9701 |
| manhattan_accuracy | 0.9457 |
| manhattan_accuracy_threshold | 9.7874 |
| manhattan_f1 | 0.9555 |
| manhattan_f1_threshold | 9.7874 |
| manhattan_precision | 0.92 |
| manhattan_recall | 0.9938 |
| manhattan_ap | 0.9698 |
| euclidean_accuracy | 0.9457 |
| euclidean_accuracy_threshold | 0.6239 |
| euclidean_f1 | 0.9555 |
| euclidean_f1_threshold | 0.6239 |
| euclidean_precision | 0.92 |
| euclidean_recall | 0.9938 |
| euclidean_ap | 0.9701 |
| max_accuracy | 0.9457 |
| max_accuracy_threshold | 9.7874 |
| max_f1 | 0.9555 |
| max_f1_threshold | 9.7874 |
| max_precision | 0.92 |
| max_recall | 0.9938 |
| **max_ap** | **0.9701** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,752 training samples
* Columns: <code>sentence2</code>, <code>label</code>, and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence2 | label | sentence1 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | int | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 10.14 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>0: ~49.00%</li><li>1: ~51.00%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.77 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| sentence2 | label | sentence1 |
|:---------------------------------------------------|:---------------|:--------------------------------------------------|
| <code>What are the ingredients of pizza?</code> | <code>1</code> | <code>What are the ingredients of a pizza?</code> |
| <code>What are the ingredients of a burger?</code> | <code>0</code> | <code>What are the ingredients of a pizza?</code> |
| <code>How is photosynthesis carried out?</code> | <code>1</code> | <code>How does photosynthesis work?</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 276 evaluation samples
* Columns: <code>sentence2</code>, <code>label</code>, and <code>sentence1</code>
* Approximate statistics based on the first 276 samples:
| | sentence2 | label | sentence1 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | int | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.34 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>0: ~41.30%</li><li>1: ~58.70%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.56 tokens</li><li>max: 87 tokens</li></ul> |
* Samples:
| sentence2 | label | sentence1 |
|:---------------------------------------------------------------------------------------------------------------------------|:---------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How is AI used to enhance cybersecurity?</code> | <code>0</code> | <code>What are the challenges of AI in cybersecurity?</code> |
| <code>The SYSTEM log documentation can be accessed by clicking on the link which will take you to the main version.</code> | <code>1</code> | <code>You can find the SYSTEM log documentation on the main version. Click on the provided link to redirect to the main version of the documentation.</code> |
| <code>Name the capital city of Italy</code> | <code>1</code> | <code>What is the capital of Italy?</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | pair-class-dev_max_ap | pair-class-test_max_ap |
|:----------:|:-------:|:-------------:|:----------:|:---------------------:|:----------------------:|
| 0 | 0 | - | - | 0.7876 | - |
| 0.2326 | 10 | 1.5405 | - | - | - |
| 0.4651 | 20 | 1.0389 | - | - | - |
| 0.6977 | 30 | 1.2755 | - | - | - |
| 0.9302 | 40 | 0.7024 | - | - | - |
| 1.0 | 43 | - | 0.9673 | 0.9133 | - |
| 1.1512 | 50 | 0.7527 | - | - | - |
| 1.3837 | 60 | 0.6684 | - | - | - |
| 1.6163 | 70 | 0.7612 | - | - | - |
| 1.8488 | 80 | 0.7265 | - | - | - |
| 2.0116 | 87 | - | 0.4647 | 0.9534 | - |
| 2.0698 | 90 | 0.2986 | - | - | - |
| 2.3023 | 100 | 0.1964 | - | - | - |
| 2.5349 | 110 | 0.5834 | - | - | - |
| 2.7674 | 120 | 0.4893 | - | - | - |
| 3.0 | 130 | 0.1254 | 0.3544 | 0.9670 | - |
| 3.2209 | 140 | 0.278 | - | - | - |
| 3.4535 | 150 | 0.1805 | - | - | - |
| 3.6860 | 160 | 0.4525 | - | - | - |
| 3.9186 | 170 | 0.1885 | - | - | - |
| **3.9651** | **172** | **-** | **0.3396** | **0.9701** | **0.9701** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.34.2
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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|
{"base_model": "intfloat/multilingual-e5-small", "library_name": "sentence-transformers", "metrics": ["cosine_accuracy", "cosine_accuracy_threshold", "cosine_f1", "cosine_f1_threshold", "cosine_precision", "cosine_recall", "cosine_ap", "dot_accuracy", "dot_accuracy_threshold", "dot_f1", "dot_f1_threshold", "dot_precision", "dot_recall", "dot_ap", "manhattan_accuracy", "manhattan_accuracy_threshold", "manhattan_f1", "manhattan_f1_threshold", "manhattan_precision", "manhattan_recall", "manhattan_ap", "euclidean_accuracy", "euclidean_accuracy_threshold", "euclidean_f1", "euclidean_f1_threshold", "euclidean_precision", "euclidean_recall", "euclidean_ap", "max_accuracy", "max_accuracy_threshold", "max_f1", "max_f1_threshold", "max_precision", "max_recall", "max_ap"], "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:2752", "loss:OnlineContrastiveLoss"], "widget": [{"source_sentence": "Would you want to be President?", "sentences": ["Can you help me with my homework?", "How to bake cookies?", "Why do you want to be to president?"]}, {"source_sentence": "Velocity of sound waves in the atmosphere", "sentences": ["What is the speed of sound in air?", "What is the best/most memorable thing you've ever eaten and why?", "The `safe` option in the `to_spreadsheet` method controls whether a safe conversion or not is needed for certain plant attributes to store the data in a SpreadsheetTable or Row."]}, {"source_sentence": "Number of countries in the European Union", "sentences": ["How many countries are in the European Union?", "Who painted the Sistine Chapel ceiling?", "The RecipeManager class is used to manage the downloading and extraction of recipes."]}, {"source_sentence": "Official currency of the USA", "sentences": ["What is purpose of life?", "Files inside ZIP archives are accessed and yielded sequentially using iter_zip().", "What is the currency of the United States?"]}, {"source_sentence": "Who wrote the book \"1984\"?", "sentences": ["What is the speed of light?", "How to set up a home gym?", "Who wrote the book \"To Kill a Mockingbird\"?"]}], "model-index": [{"name": "SentenceTransformer based on intfloat/multilingual-e5-small", "results": [{"task": {"type": "binary-classification", "name": "Binary Classification"}, "dataset": {"name": "pair class dev", "type": "pair-class-dev"}, "metrics": [{"type": "cosine_accuracy", "value": 0.9456521739130435, "name": "Cosine Accuracy"}, {"type": "cosine_accuracy_threshold", "value": 0.8053532838821411, "name": "Cosine Accuracy Threshold"}, {"type": "cosine_f1", "value": 0.9554896142433236, "name": "Cosine F1"}, {"type": "cosine_f1_threshold", "value": 0.8053532838821411, "name": "Cosine F1 Threshold"}, {"type": "cosine_precision", "value": 0.92, "name": "Cosine Precision"}, {"type": "cosine_recall", "value": 0.9938271604938271, "name": "Cosine Recall"}, {"type": "cosine_ap", "value": 0.970102365862799, "name": "Cosine Ap"}, {"type": "dot_accuracy", "value": 0.9456521739130435, "name": "Dot Accuracy"}, {"type": "dot_accuracy_threshold", "value": 0.8053532838821411, "name": "Dot Accuracy Threshold"}, {"type": "dot_f1", "value": 0.9554896142433236, "name": "Dot F1"}, {"type": "dot_f1_threshold", "value": 0.8053532838821411, "name": "Dot F1 Threshold"}, {"type": "dot_precision", "value": 0.92, "name": "Dot Precision"}, {"type": "dot_recall", "value": 0.9938271604938271, "name": "Dot Recall"}, {"type": "dot_ap", "value": 0.970102365862799, "name": "Dot Ap"}, {"type": "manhattan_accuracy", "value": 0.9456521739130435, "name": "Manhattan Accuracy"}, {"type": "manhattan_accuracy_threshold", "value": 9.787351608276367, "name": "Manhattan Accuracy Threshold"}, {"type": "manhattan_f1", "value": 0.9554896142433236, "name": "Manhattan F1"}, {"type": "manhattan_f1_threshold", "value": 9.787351608276367, "name": "Manhattan F1 Threshold"}, {"type": "manhattan_precision", "value": 0.92, "name": "Manhattan Precision"}, {"type": "manhattan_recall", "value": 0.9938271604938271, "name": "Manhattan Recall"}, {"type": "manhattan_ap", "value": 0.9698493258522533, "name": "Manhattan Ap"}, {"type": "euclidean_accuracy", "value": 0.9456521739130435, "name": "Euclidean Accuracy"}, {"type": "euclidean_accuracy_threshold", "value": 0.6239285469055176, "name": "Euclidean Accuracy Threshold"}, {"type": "euclidean_f1", "value": 0.9554896142433236, "name": "Euclidean F1"}, {"type": "euclidean_f1_threshold", "value": 0.6239285469055176, "name": "Euclidean F1 Threshold"}, {"type": "euclidean_precision", "value": 0.92, "name": "Euclidean Precision"}, {"type": "euclidean_recall", "value": 0.9938271604938271, "name": "Euclidean Recall"}, {"type": "euclidean_ap", "value": 0.970102365862799, "name": "Euclidean Ap"}, {"type": "max_accuracy", "value": 0.9456521739130435, "name": "Max Accuracy"}, {"type": "max_accuracy_threshold", "value": 9.787351608276367, "name": "Max Accuracy Threshold"}, {"type": "max_f1", "value": 0.9554896142433236, "name": "Max F1"}, {"type": "max_f1_threshold", "value": 9.787351608276367, "name": "Max F1 Threshold"}, {"type": "max_precision", "value": 0.92, "name": "Max Precision"}, {"type": "max_recall", "value": 0.9938271604938271, "name": "Max Recall"}, {"type": "max_ap", "value": 0.970102365862799, "name": "Max Ap"}]}, {"task": {"type": "binary-classification", "name": "Binary Classification"}, "dataset": {"name": "pair class test", "type": "pair-class-test"}, "metrics": [{"type": "cosine_accuracy", "value": 0.9456521739130435, "name": "Cosine Accuracy"}, {"type": "cosine_accuracy_threshold", "value": 0.8053532838821411, "name": "Cosine Accuracy Threshold"}, {"type": "cosine_f1", "value": 0.9554896142433236, "name": "Cosine F1"}, {"type": "cosine_f1_threshold", "value": 0.8053532838821411, "name": "Cosine F1 Threshold"}, {"type": "cosine_precision", "value": 0.92, "name": "Cosine Precision"}, {"type": "cosine_recall", "value": 0.9938271604938271, "name": "Cosine Recall"}, {"type": "cosine_ap", "value": 0.970102365862799, "name": "Cosine Ap"}, {"type": "dot_accuracy", "value": 0.9456521739130435, "name": "Dot Accuracy"}, {"type": "dot_accuracy_threshold", "value": 0.8053532838821411, "name": "Dot Accuracy Threshold"}, {"type": "dot_f1", "value": 0.9554896142433236, "name": "Dot F1"}, {"type": "dot_f1_threshold", "value": 0.8053532838821411, "name": "Dot F1 Threshold"}, {"type": "dot_precision", "value": 0.92, "name": "Dot Precision"}, {"type": "dot_recall", "value": 0.9938271604938271, "name": "Dot Recall"}, {"type": "dot_ap", "value": 0.970102365862799, "name": "Dot Ap"}, {"type": "manhattan_accuracy", "value": 0.9456521739130435, "name": "Manhattan Accuracy"}, {"type": "manhattan_accuracy_threshold", "value": 9.787351608276367, "name": "Manhattan Accuracy Threshold"}, {"type": "manhattan_f1", "value": 0.9554896142433236, "name": "Manhattan F1"}, {"type": "manhattan_f1_threshold", "value": 9.787351608276367, "name": "Manhattan F1 Threshold"}, {"type": "manhattan_precision", "value": 0.92, "name": "Manhattan Precision"}, {"type": "manhattan_recall", "value": 0.9938271604938271, "name": "Manhattan Recall"}, {"type": "manhattan_ap", "value": 0.9698493258522533, "name": "Manhattan Ap"}, {"type": "euclidean_accuracy", "value": 0.9456521739130435, "name": "Euclidean Accuracy"}, {"type": "euclidean_accuracy_threshold", "value": 0.6239285469055176, "name": "Euclidean Accuracy Threshold"}, {"type": "euclidean_f1", "value": 0.9554896142433236, "name": "Euclidean F1"}, {"type": "euclidean_f1_threshold", "value": 0.6239285469055176, "name": "Euclidean F1 Threshold"}, {"type": "euclidean_precision", "value": 0.92, "name": "Euclidean Precision"}, {"type": "euclidean_recall", "value": 0.9938271604938271, "name": "Euclidean Recall"}, {"type": "euclidean_ap", "value": 0.970102365862799, "name": "Euclidean Ap"}, {"type": "max_accuracy", "value": 0.9456521739130435, "name": "Max Accuracy"}, {"type": "max_accuracy_threshold", "value": 9.787351608276367, "name": "Max Accuracy Threshold"}, {"type": "max_f1", "value": 0.9554896142433236, "name": "Max F1"}, {"type": "max_f1_threshold", "value": 9.787351608276367, "name": "Max F1 Threshold"}, {"type": "max_precision", "value": 0.92, "name": "Max Precision"}, {"type": "max_recall", "value": 0.9938271604938271, "name": "Max Recall"}, {"type": "max_ap", "value": 0.970102365862799, "name": "Max Ap"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,022 |
Sathvik6323/cardiffnlp-twitter-roberta-base-sentiment
|
Sathvik6323
|
text-classification
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"autotrain",
"dataset:autotrain-9yyoi-z5w7f/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-04-28T05:44:40Z |
2024-04-29T02:22:32+00:00
| 6 | 0 |
---
datasets:
- autotrain-9yyoi-z5w7f/autotrain-data
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.0112701654434204
f1_macro: 0.23304414883362254
f1_micro: 0.5374355326338254
f1_weighted: 0.37573861876678316
precision_macro: 0.17914517754460846
precision_micro: 0.5374355326338254
precision_weighted: 0.28883695173740354
recall_macro: 0.3333333333333333
recall_micro: 0.5374355326338254
recall_weighted: 0.5374355326338254
accuracy: 0.5374355326338254
| null |
Non_BioNLP
|
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.0112701654434204
f1_macro: 0.23304414883362254
f1_micro: 0.5374355326338254
f1_weighted: 0.37573861876678316
precision_macro: 0.17914517754460846
precision_micro: 0.5374355326338254
precision_weighted: 0.28883695173740354
recall_macro: 0.3333333333333333
recall_micro: 0.5374355326338254
recall_weighted: 0.5374355326338254
accuracy: 0.5374355326338254
|
{"datasets": ["autotrain-9yyoi-z5w7f/autotrain-data"], "tags": ["autotrain", "text-classification"], "widget": [{"text": "I love AutoTrain"}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,023 |
lvignaliAWS/sagemaker-distilbert-emotion
|
lvignaliAWS
|
text-classification
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-31T11:00:43Z |
2023-07-31T11:02:48+00:00
| 13 | 0 |
---
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: sagemaker-distilbert-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: test
args: split
metrics:
- type: accuracy
value: 0.9155
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2592
- Accuracy: 0.9155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9591 | 1.0 | 500 | 0.2592 | 0.9155 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2592
- Accuracy: 0.9155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9591 | 1.0 | 500 | 0.2592 | 0.9155 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
{"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "test", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9155, "name": "Accuracy"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,024 |
ysige/distilbert-base-uncased-finetuned-emotion
|
ysige
|
text-classification
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-04-08T11:35:21Z |
2023-04-09T12:53:07+00:00
| 8 | 0 |
---
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- type: accuracy
value: 0.922
name: Accuracy
- type: f1
value: 0.9219748629797122
name: F1
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
- Accuracy: 0.922
- F1: 0.9220
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7994 | 1.0 | 250 | 0.3069 | 0.906 | 0.9035 |
| 0.2443 | 2.0 | 500 | 0.2181 | 0.922 | 0.9220 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
| null |
Non_BioNLP
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
- Accuracy: 0.922
- F1: 0.9220
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7994 | 1.0 | 250 | 0.3069 | 0.906 | 0.9035 |
| 0.2443 | 2.0 | 500 | 0.2181 | 0.922 | 0.9220 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
|
{"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.922, "name": "Accuracy"}, {"type": "f1", "value": 0.9219748629797122, "name": "F1"}]}]}]}
|
task
|
[
"TEXT_CLASSIFICATION"
] | 42,025 |
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