modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
JunSotohigashi/jumping-water-602
|
JunSotohigashi
| 2025-06-19T11:25:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"lora",
"sft",
"dataset:JunSotohigashi/JapaneseWikipediaTypoDataset_kanji",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2",
"base_model:adapter:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T07:16:35Z |
---
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2
datasets: JunSotohigashi/JapaneseWikipediaTypoDataset_kanji
library_name: transformers
model_name: JunSotohigashi/jumping-water-602
tags:
- generated_from_trainer
- lora
- sft
licence: license
---
# Model Card for JunSotohigashi/jumping-water-602
This model is a fine-tuned version of [tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) on the [JunSotohigashi/JapaneseWikipediaTypoDataset_kanji](https://huggingface.co/datasets/JunSotohigashi/JapaneseWikipediaTypoDataset_kanji) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JunSotohigashi/jumping-water-602", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jun-sotohigashi-toyota-technological-institute/misusing-corpus-jp/runs/3t1ffk3c)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb3-seed7-2025-06-19
|
morturr
| 2025-06-19T11:21:59Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T11:21:44Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb3-seed7-2025-06-19
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-2-7b-hf-LOO_one_liners-COMB_headlines-comb3-seed7-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
JunSotohigashi/peach-hill-599
|
JunSotohigashi
| 2025-06-19T11:21:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"lora",
"sft",
"dataset:JunSotohigashi/JapaneseWikipediaTypoDataset_kanji",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2",
"base_model:adapter:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T07:15:41Z |
---
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2
datasets: JunSotohigashi/JapaneseWikipediaTypoDataset_kanji
library_name: transformers
model_name: JunSotohigashi/peach-hill-599
tags:
- generated_from_trainer
- lora
- sft
licence: license
---
# Model Card for JunSotohigashi/peach-hill-599
This model is a fine-tuned version of [tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) on the [JunSotohigashi/JapaneseWikipediaTypoDataset_kanji](https://huggingface.co/datasets/JunSotohigashi/JapaneseWikipediaTypoDataset_kanji) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JunSotohigashi/peach-hill-599", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jun-sotohigashi-toyota-technological-institute/misusing-corpus-jp/runs/4i40vne9)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
finvix/qwen-2.5-0.5B-merged_16bit
|
finvix
| 2025-06-19T11:14:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T11:12:45Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** finvix
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-0.5B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
diabolocom/openai-whisper-large-v3-LoRA-fr-CA
|
diabolocom
| 2025-06-19T11:11:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T08:37:01Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
MikeGreen2710/ner_total_dims_final
|
MikeGreen2710
| 2025-06-19T11:00:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-19T10:59:41Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
Khruna/Nico
|
Khruna
| 2025-06-19T10:56:18Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-19T10:56:04Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: >-
images/Professional_Mode_woman_shows_her_shiny_plate.00_00_29_20.Still002.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# nico
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Khruna/Nico/tree/main) them in the Files & versions tab.
|
JustKnow/wav2vec2-large-xlsr-twi
|
JustKnow
| 2025-06-19T10:53:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-19T10:41:20Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
Khruna/marya
|
Khruna
| 2025-06-19T10:52:19Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-19T10:52:12Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: >-
images/Professional_Mode_woman_shows_her_shiny_plate.00_00_29_20.Still003.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# marya
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Khruna/marya/tree/main) them in the Files & versions tab.
|
tomaarsen/splade-distilbert-base-uncased-quora-duplicates
|
tomaarsen
| 2025-06-19T10:36:10Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"distilbert",
"sparse-encoder",
"sparse",
"splade",
"generated_from_trainer",
"dataset_size:99000",
"loss:SpladeLoss",
"loss:SparseMultipleNegativesRankingLoss",
"loss:FlopsLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/quora-duplicates",
"arxiv:1908.10084",
"arxiv:2205.04733",
"arxiv:1705.00652",
"arxiv:2004.05665",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-19T10:36:01Z |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
- text: How do I know if a girl likes me at school?
- text: What are some five star hotel in Jaipur?
- text: Is it normal to fantasize your wife having sex with another man?
- text: What is the Sahara, and how do the average temperatures there compare to the
ones in the Simpson Desert?
- text: What are Hillary Clinton's most recognized accomplishments while Secretary
of State?
datasets:
- sentence-transformers/quora-duplicates
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- dot_mcc
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- euclidean_mcc
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- manhattan_mcc
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
- max_mcc
- active_dims
- sparsity_ratio
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 29.19330199735101
energy_consumed: 0.07510458396754072
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.306
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: splade-distilbert-base-uncased trained on Quora Duplicates Questions
results:
- task:
type: sparse-binary-classification
name: Sparse Binary Classification
dataset:
name: quora duplicates dev
type: quora_duplicates_dev
metrics:
- type: cosine_accuracy
value: 0.759
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8012633323669434
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6741573033707865
name: Cosine F1
- type: cosine_f1_threshold
value: 0.542455792427063
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.528169014084507
name: Cosine Precision
- type: cosine_recall
value: 0.9316770186335404
name: Cosine Recall
- type: cosine_ap
value: 0.6875984052094628
name: Cosine Ap
- type: cosine_mcc
value: 0.5059561809366392
name: Cosine Mcc
- type: dot_accuracy
value: 0.754
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 47.276466369628906
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6759581881533101
name: Dot F1
- type: dot_f1_threshold
value: 40.955284118652344
name: Dot F1 Threshold
- type: dot_precision
value: 0.5398886827458256
name: Dot Precision
- type: dot_recall
value: 0.9037267080745341
name: Dot Recall
- type: dot_ap
value: 0.6070585464263578
name: Dot Ap
- type: dot_mcc
value: 0.5042382773971489
name: Dot Mcc
- type: euclidean_accuracy
value: 0.677
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: -14.295218467712402
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.48599545798637395
name: Euclidean F1
- type: euclidean_f1_threshold
value: -0.5385364294052124
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.3213213213213213
name: Euclidean Precision
- type: euclidean_recall
value: 0.9968944099378882
name: Euclidean Recall
- type: euclidean_ap
value: 0.20430811061248494
name: Euclidean Ap
- type: euclidean_mcc
value: -0.04590966956831287
name: Euclidean Mcc
- type: manhattan_accuracy
value: 0.677
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: -163.6865234375
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.48599545798637395
name: Manhattan F1
- type: manhattan_f1_threshold
value: -2.7509355545043945
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.3213213213213213
name: Manhattan Precision
- type: manhattan_recall
value: 0.9968944099378882
name: Manhattan Recall
- type: manhattan_ap
value: 0.20563864564607998
name: Manhattan Ap
- type: manhattan_mcc
value: -0.04590966956831287
name: Manhattan Mcc
- type: max_accuracy
value: 0.759
name: Max Accuracy
- type: max_accuracy_threshold
value: 47.276466369628906
name: Max Accuracy Threshold
- type: max_f1
value: 0.6759581881533101
name: Max F1
- type: max_f1_threshold
value: 40.955284118652344
name: Max F1 Threshold
- type: max_precision
value: 0.5398886827458256
name: Max Precision
- type: max_recall
value: 0.9968944099378882
name: Max Recall
- type: max_ap
value: 0.6875984052094628
name: Max Ap
- type: max_mcc
value: 0.5059561809366392
name: Max Mcc
- type: active_dims
value: 83.36341094970703
name: Active Dims
- type: sparsity_ratio
value: 0.9972687434981421
name: Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.56
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.14666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.11200000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.24
name: Dot Recall@1
- type: dot_recall@3
value: 0.44
name: Dot Recall@3
- type: dot_recall@5
value: 0.56
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.46883808093835555
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3849920634920634
name: Dot Mrr@10
- type: dot_map@100
value: 0.39450094910993877
name: Dot Map@100
- type: query_active_dims
value: 84.87999725341797
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9972190551977781
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 104.35554504394531
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9965809729033503
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.14666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.12000000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.24
name: Dot Recall@1
- type: dot_recall@3
value: 0.44
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.46663046446554135
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3821587301587301
name: Dot Mrr@10
- type: dot_map@100
value: 0.39141822290426725
name: Dot Map@100
- type: query_active_dims
value: 94.9000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9968907672653863
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 115.97699737548828
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9962002163234556
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.18
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.18
name: Dot Precision@1
- type: dot_precision@3
value: 0.14666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.10400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.06000000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.17
name: Dot Recall@1
- type: dot_recall@3
value: 0.41
name: Dot Recall@3
- type: dot_recall@5
value: 0.48
name: Dot Recall@5
- type: dot_recall@10
value: 0.55
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3711173352982992
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.32435714285714284
name: Dot Mrr@10
- type: dot_map@100
value: 0.32104591506684527
name: Dot Map@100
- type: query_active_dims
value: 76.81999969482422
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974831269348396
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 139.53028869628906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9954285338871539
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.18
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.46
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.64
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.18
name: Dot Precision@1
- type: dot_precision@3
value: 0.1533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.10000000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.066
name: Dot Precision@10
- type: dot_recall@1
value: 0.17
name: Dot Recall@1
- type: dot_recall@3
value: 0.43
name: Dot Recall@3
- type: dot_recall@5
value: 0.46
name: Dot Recall@5
- type: dot_recall@10
value: 0.61
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.39277722565932277
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.33549999999999996
name: Dot Mrr@10
- type: dot_map@100
value: 0.3266050492721919
name: Dot Map@100
- type: query_active_dims
value: 85.72000122070312
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971915339354989
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 156.10665893554688
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.994885438079564
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.46
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.52
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.24
name: Dot Precision@3
- type: dot_precision@5
value: 0.2
name: Dot Precision@5
- type: dot_precision@10
value: 0.16
name: Dot Precision@10
- type: dot_recall@1
value: 0.010055870806195594
name: Dot Recall@1
- type: dot_recall@3
value: 0.03299225609257712
name: Dot Recall@3
- type: dot_recall@5
value: 0.043240249260663235
name: Dot Recall@5
- type: dot_recall@10
value: 0.0575687615260951
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.1901013298743406
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3606904761904762
name: Dot Mrr@10
- type: dot_map@100
value: 0.06747201795263198
name: Dot Map@100
- type: query_active_dims
value: 92.18000030517578
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9969798833528217
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 196.1699981689453
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.993572832770823
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.48
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.52
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.21600000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.174
name: Dot Precision@10
- type: dot_recall@1
value: 0.020055870806195596
name: Dot Recall@1
- type: dot_recall@3
value: 0.03516880470242261
name: Dot Recall@3
- type: dot_recall@5
value: 0.07436160102717629
name: Dot Recall@5
- type: dot_recall@10
value: 0.08924749441772001
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2174721143005973
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3753888888888888
name: Dot Mrr@10
- type: dot_map@100
value: 0.08327101018955965
name: Dot Map@100
- type: query_active_dims
value: 101.91999816894531
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9966607693411655
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 217.09109497070312
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9928873895887982
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.9
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.96
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.96
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9
name: Dot Precision@1
- type: dot_precision@3
value: 0.38666666666666655
name: Dot Precision@3
- type: dot_precision@5
value: 0.24799999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.13599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.804
name: Dot Recall@1
- type: dot_recall@3
value: 0.9053333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.9326666666666666
name: Dot Recall@5
- type: dot_recall@10
value: 0.99
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.940813094731721
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9366666666666665
name: Dot Mrr@10
- type: dot_map@100
value: 0.9174399766899767
name: Dot Map@100
- type: query_active_dims
value: 80.30000305175781
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9973691107053353
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 83.33353424072266
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9972697223563096
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.9
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.96
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1.0
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.9
name: Dot Precision@1
- type: dot_precision@3
value: 0.38666666666666655
name: Dot Precision@3
- type: dot_precision@5
value: 0.25599999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.13599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.804
name: Dot Recall@1
- type: dot_recall@3
value: 0.9086666666666667
name: Dot Recall@3
- type: dot_recall@5
value: 0.97
name: Dot Recall@5
- type: dot_recall@10
value: 0.99
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9434418368741703
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.94
name: Dot Mrr@10
- type: dot_map@100
value: 0.9210437710437711
name: Dot Map@100
- type: query_active_dims
value: 87.4000015258789
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9971364916609043
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 90.32620239257812
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.997040619802353
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.565
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.625
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.71
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.22999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.166
name: Dot Precision@5
- type: dot_precision@10
value: 0.10750000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.30601396770154893
name: Dot Recall@1
- type: dot_recall@3
value: 0.4470813973564776
name: Dot Recall@3
- type: dot_recall@5
value: 0.5039767289818324
name: Dot Recall@5
- type: dot_recall@10
value: 0.5843921903815238
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4927174602106791
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5016765873015872
name: Dot Mrr@10
- type: dot_map@100
value: 0.4251147147048482
name: Dot Map@100
- type: query_active_dims
value: 83.54500007629395
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9972627940476937
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 123.28323480743562
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9959608402199255
name: Corpus Sparsity Ratio
- type: dot_accuracy@1
value: 0.4021664050235479
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5765463108320251
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6598116169544741
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7337833594976453
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4021664050235479
name: Dot Precision@1
- type: dot_precision@3
value: 0.25656724228152794
name: Dot Precision@3
- type: dot_precision@5
value: 0.20182103610675042
name: Dot Precision@5
- type: dot_precision@10
value: 0.14312715855572997
name: Dot Precision@10
- type: dot_recall@1
value: 0.23408727816164185
name: Dot Recall@1
- type: dot_recall@3
value: 0.3568914414902249
name: Dot Recall@3
- type: dot_recall@5
value: 0.4275402562349963
name: Dot Recall@5
- type: dot_recall@10
value: 0.5040607961406979
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.45167521970189345
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5088102589020956
name: Dot Mrr@10
- type: dot_map@100
value: 0.37853024172675503
name: Dot Map@100
- type: query_active_dims
value: 105.61787400444042
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9965396149005816
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 163.73635361872905
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9946354644643625
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.14
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.32
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.42
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.52
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.14
name: Dot Precision@1
- type: dot_precision@3
value: 0.11333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.09200000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.064
name: Dot Precision@10
- type: dot_recall@1
value: 0.07166666666666666
name: Dot Recall@1
- type: dot_recall@3
value: 0.14833333333333332
name: Dot Recall@3
- type: dot_recall@5
value: 0.19
name: Dot Recall@5
- type: dot_recall@10
value: 0.25
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.1928494772790168
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2526666666666666
name: Dot Mrr@10
- type: dot_map@100
value: 0.14153388517603807
name: Dot Map@100
- type: query_active_dims
value: 102.33999633789062
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9966470088350079
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 217.80722045898438
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9928639269884351
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.56
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.78
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.82
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.56
name: Dot Precision@1
- type: dot_precision@3
value: 0.5133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.488
name: Dot Precision@5
- type: dot_precision@10
value: 0.436
name: Dot Precision@10
- type: dot_recall@1
value: 0.042268334576683116
name: Dot Recall@1
- type: dot_recall@3
value: 0.1179684188048045
name: Dot Recall@3
- type: dot_recall@5
value: 0.17514937366700764
name: Dot Recall@5
- type: dot_recall@10
value: 0.2739338942789917
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5024388532207343
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6801666666666667
name: Dot Mrr@10
- type: dot_map@100
value: 0.38220472918007364
name: Dot Map@100
- type: query_active_dims
value: 79.80000305175781
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9973854923317031
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 146.68072509765625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.995194262332165
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.64
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.72
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.82
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.88
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.64
name: Dot Precision@1
- type: dot_precision@3
value: 0.2533333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.176
name: Dot Precision@5
- type: dot_precision@10
value: 0.09399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.6066666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.7033333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.8033333333333332
name: Dot Recall@5
- type: dot_recall@10
value: 0.8633333333333333
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7368677901493659
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7063809523809523
name: Dot Mrr@10
- type: dot_map@100
value: 0.697561348294107
name: Dot Map@100
- type: query_active_dims
value: 104.22000122070312
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9965854137598879
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 228.74359130859375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9925056159062776
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.2
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.28
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.4
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.46
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.2
name: Dot Precision@1
- type: dot_precision@3
value: 0.12666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.10400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.09469047619047619
name: Dot Recall@1
- type: dot_recall@3
value: 0.15076984126984128
name: Dot Recall@3
- type: dot_recall@5
value: 0.25362698412698415
name: Dot Recall@5
- type: dot_recall@10
value: 0.3211825396825397
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.23331922670891586
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.27135714285714285
name: Dot Mrr@10
- type: dot_map@100
value: 0.18392178053045694
name: Dot Map@100
- type: query_active_dims
value: 89.73999786376953
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9970598257694853
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 131.34085083007812
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9956968465097282
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.8
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.92
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.94
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.8
name: Dot Precision@1
- type: dot_precision@3
value: 0.3933333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.264
name: Dot Precision@5
- type: dot_precision@10
value: 0.14200000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.4
name: Dot Recall@1
- type: dot_recall@3
value: 0.59
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.71
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6848748058213975
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8541666666666665
name: Dot Mrr@10
- type: dot_map@100
value: 0.6060670580971632
name: Dot Map@100
- type: query_active_dims
value: 111.23999786376953
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9963554158356671
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 166.19056701660156
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9945550564505407
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.26
name: Dot Precision@3
- type: dot_precision@5
value: 0.2
name: Dot Precision@5
- type: dot_precision@10
value: 0.14200000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.07166666666666668
name: Dot Recall@1
- type: dot_recall@3
value: 0.16066666666666665
name: Dot Recall@3
- type: dot_recall@5
value: 0.20566666666666664
name: Dot Recall@5
- type: dot_recall@10
value: 0.2916666666666667
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2850130343263586
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.47407142857142853
name: Dot Mrr@10
- type: dot_map@100
value: 0.20070977606957205
name: Dot Map@100
- type: query_active_dims
value: 113.77999877929688
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9962721971437226
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 226.21810913085938
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9925883589171464
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.08
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.32
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.38
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.44
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.08
name: Dot Precision@1
- type: dot_precision@3
value: 0.10666666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.07600000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.044000000000000004
name: Dot Precision@10
- type: dot_recall@1
value: 0.08
name: Dot Recall@1
- type: dot_recall@3
value: 0.32
name: Dot Recall@3
- type: dot_recall@5
value: 0.38
name: Dot Recall@5
- type: dot_recall@10
value: 0.44
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.26512761684329256
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.20850000000000002
name: Dot Mrr@10
- type: dot_map@100
value: 0.2135415485154769
name: Dot Map@100
- type: query_active_dims
value: 202.02000427246094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9933811675423477
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 176.61155700683594
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.994213630921734
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.78
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.19999999999999996
name: Dot Precision@3
- type: dot_precision@5
value: 0.14800000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.08599999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.415
name: Dot Recall@1
- type: dot_recall@3
value: 0.55
name: Dot Recall@3
- type: dot_recall@5
value: 0.665
name: Dot Recall@5
- type: dot_recall@10
value: 0.76
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5848481832222858
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5400476190476191
name: Dot Mrr@10
- type: dot_map@100
value: 0.5247408283859897
name: Dot Map@100
- type: query_active_dims
value: 102.4800033569336
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9966424217496581
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 216.64508056640625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9929020024714499
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.40816326530612246
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7551020408163265
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8775510204081632
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.40816326530612246
name: Dot Precision@1
- type: dot_precision@3
value: 0.43537414965986393
name: Dot Precision@3
- type: dot_precision@5
value: 0.38367346938775504
name: Dot Precision@5
- type: dot_precision@10
value: 0.3326530612244898
name: Dot Precision@10
- type: dot_recall@1
value: 0.027119934527989286
name: Dot Recall@1
- type: dot_recall@3
value: 0.08468167459585536
name: Dot Recall@3
- type: dot_recall@5
value: 0.12088537223378343
name: Dot Recall@5
- type: dot_recall@10
value: 0.21342642144981977
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.36611722725361623
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5941286038224813
name: Dot Mrr@10
- type: dot_map@100
value: 0.24827413478914825
name: Dot Map@100
- type: query_active_dims
value: 97.30612182617188
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9968119349378752
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 147.016357421875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9951832659255005
name: Corpus Sparsity Ratio
---
# splade-distilbert-base-uncased trained on Quora Duplicates Questions
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## 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 SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-quora-duplicates")
# Run inference
sentences = [
'What accomplishments did Hillary Clinton achieve during her time as Secretary of State?',
"What are Hillary Clinton's most recognized accomplishments while Secretary of State?",
'What are Hillary Clinton’s qualifications to be President?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 83.9635, 60.9402, 26.0887],
# [ 60.9402, 85.6474, 33.3293],
# [ 26.0887, 33.3293, 104.0980]])
```
<!--
### 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
#### Sparse Binary Classification
* Dataset: `quora_duplicates_dev`
* Evaluated with [<code>SparseBinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.759 |
| cosine_accuracy_threshold | 0.8013 |
| cosine_f1 | 0.6742 |
| cosine_f1_threshold | 0.5425 |
| cosine_precision | 0.5282 |
| cosine_recall | 0.9317 |
| cosine_ap | 0.6876 |
| cosine_mcc | 0.506 |
| dot_accuracy | 0.754 |
| dot_accuracy_threshold | 47.2765 |
| dot_f1 | 0.676 |
| dot_f1_threshold | 40.9553 |
| dot_precision | 0.5399 |
| dot_recall | 0.9037 |
| dot_ap | 0.6071 |
| dot_mcc | 0.5042 |
| euclidean_accuracy | 0.677 |
| euclidean_accuracy_threshold | -14.2952 |
| euclidean_f1 | 0.486 |
| euclidean_f1_threshold | -0.5385 |
| euclidean_precision | 0.3213 |
| euclidean_recall | 0.9969 |
| euclidean_ap | 0.2043 |
| euclidean_mcc | -0.0459 |
| manhattan_accuracy | 0.677 |
| manhattan_accuracy_threshold | -163.6865 |
| manhattan_f1 | 0.486 |
| manhattan_f1_threshold | -2.7509 |
| manhattan_precision | 0.3213 |
| manhattan_recall | 0.9969 |
| manhattan_ap | 0.2056 |
| manhattan_mcc | -0.0459 |
| max_accuracy | 0.759 |
| max_accuracy_threshold | 47.2765 |
| max_f1 | 0.676 |
| max_f1_threshold | 40.9553 |
| max_precision | 0.5399 |
| max_recall | 0.9969 |
| **max_ap** | **0.6876** |
| max_mcc | 0.506 |
| active_dims | 83.3634 |
| sparsity_ratio | 0.9973 |
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNQ`, `NanoNFCorpus`, `NanoQuoraRetrieval`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNQ | NanoNFCorpus | NanoQuoraRetrieval | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:----------------------|:------------|:-----------|:-------------|:-------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:------------|:------------|:---------------|
| dot_accuracy@1 | 0.24 | 0.18 | 0.3 | 0.9 | 0.14 | 0.56 | 0.64 | 0.2 | 0.8 | 0.34 | 0.08 | 0.44 | 0.4082 |
| dot_accuracy@3 | 0.44 | 0.46 | 0.42 | 0.96 | 0.32 | 0.78 | 0.72 | 0.28 | 0.9 | 0.56 | 0.32 | 0.58 | 0.7551 |
| dot_accuracy@5 | 0.6 | 0.5 | 0.48 | 1.0 | 0.42 | 0.82 | 0.82 | 0.4 | 0.92 | 0.66 | 0.38 | 0.7 | 0.8776 |
| dot_accuracy@10 | 0.74 | 0.64 | 0.52 | 1.0 | 0.52 | 0.88 | 0.88 | 0.46 | 0.94 | 0.78 | 0.44 | 0.78 | 0.9592 |
| dot_precision@1 | 0.24 | 0.18 | 0.3 | 0.9 | 0.14 | 0.56 | 0.64 | 0.2 | 0.8 | 0.34 | 0.08 | 0.44 | 0.4082 |
| dot_precision@3 | 0.1467 | 0.1533 | 0.2467 | 0.3867 | 0.1133 | 0.5133 | 0.2533 | 0.1267 | 0.3933 | 0.26 | 0.1067 | 0.2 | 0.4354 |
| dot_precision@5 | 0.12 | 0.1 | 0.216 | 0.256 | 0.092 | 0.488 | 0.176 | 0.104 | 0.264 | 0.2 | 0.076 | 0.148 | 0.3837 |
| dot_precision@10 | 0.074 | 0.066 | 0.174 | 0.136 | 0.064 | 0.436 | 0.094 | 0.07 | 0.142 | 0.142 | 0.044 | 0.086 | 0.3327 |
| dot_recall@1 | 0.24 | 0.17 | 0.0201 | 0.804 | 0.0717 | 0.0423 | 0.6067 | 0.0947 | 0.4 | 0.0717 | 0.08 | 0.415 | 0.0271 |
| dot_recall@3 | 0.44 | 0.43 | 0.0352 | 0.9087 | 0.1483 | 0.118 | 0.7033 | 0.1508 | 0.59 | 0.1607 | 0.32 | 0.55 | 0.0847 |
| dot_recall@5 | 0.6 | 0.46 | 0.0744 | 0.97 | 0.19 | 0.1751 | 0.8033 | 0.2536 | 0.66 | 0.2057 | 0.38 | 0.665 | 0.1209 |
| dot_recall@10 | 0.74 | 0.61 | 0.0892 | 0.99 | 0.25 | 0.2739 | 0.8633 | 0.3212 | 0.71 | 0.2917 | 0.44 | 0.76 | 0.2134 |
| **dot_ndcg@10** | **0.4666** | **0.3928** | **0.2175** | **0.9434** | **0.1928** | **0.5024** | **0.7369** | **0.2333** | **0.6849** | **0.285** | **0.2651** | **0.5848** | **0.3661** |
| dot_mrr@10 | 0.3822 | 0.3355 | 0.3754 | 0.94 | 0.2527 | 0.6802 | 0.7064 | 0.2714 | 0.8542 | 0.4741 | 0.2085 | 0.54 | 0.5941 |
| dot_map@100 | 0.3914 | 0.3266 | 0.0833 | 0.921 | 0.1415 | 0.3822 | 0.6976 | 0.1839 | 0.6061 | 0.2007 | 0.2135 | 0.5247 | 0.2483 |
| query_active_dims | 94.9 | 85.72 | 101.92 | 87.4 | 102.34 | 79.8 | 104.22 | 89.74 | 111.24 | 113.78 | 202.02 | 102.48 | 97.3061 |
| query_sparsity_ratio | 0.9969 | 0.9972 | 0.9967 | 0.9971 | 0.9966 | 0.9974 | 0.9966 | 0.9971 | 0.9964 | 0.9963 | 0.9934 | 0.9966 | 0.9968 |
| corpus_active_dims | 115.977 | 156.1067 | 217.0911 | 90.3262 | 217.8072 | 146.6807 | 228.7436 | 131.3409 | 166.1906 | 226.2181 | 176.6116 | 216.6451 | 147.0164 |
| corpus_sparsity_ratio | 0.9962 | 0.9949 | 0.9929 | 0.997 | 0.9929 | 0.9952 | 0.9925 | 0.9957 | 0.9946 | 0.9926 | 0.9942 | 0.9929 | 0.9952 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nq",
"nfcorpus",
"quoraretrieval"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.4 |
| dot_accuracy@3 | 0.565 |
| dot_accuracy@5 | 0.625 |
| dot_accuracy@10 | 0.71 |
| dot_precision@1 | 0.4 |
| dot_precision@3 | 0.23 |
| dot_precision@5 | 0.166 |
| dot_precision@10 | 0.1075 |
| dot_recall@1 | 0.306 |
| dot_recall@3 | 0.4471 |
| dot_recall@5 | 0.504 |
| dot_recall@10 | 0.5844 |
| **dot_ndcg@10** | **0.4927** |
| dot_mrr@10 | 0.5017 |
| dot_map@100 | 0.4251 |
| query_active_dims | 83.545 |
| query_sparsity_ratio | 0.9973 |
| corpus_active_dims | 123.2832 |
| corpus_sparsity_ratio | 0.996 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
"msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.4022 |
| dot_accuracy@3 | 0.5765 |
| dot_accuracy@5 | 0.6598 |
| dot_accuracy@10 | 0.7338 |
| dot_precision@1 | 0.4022 |
| dot_precision@3 | 0.2566 |
| dot_precision@5 | 0.2018 |
| dot_precision@10 | 0.1431 |
| dot_recall@1 | 0.2341 |
| dot_recall@3 | 0.3569 |
| dot_recall@5 | 0.4275 |
| dot_recall@10 | 0.5041 |
| **dot_ndcg@10** | **0.4517** |
| dot_mrr@10 | 0.5088 |
| dot_map@100 | 0.3785 |
| query_active_dims | 105.6179 |
| query_sparsity_ratio | 0.9965 |
| corpus_active_dims | 163.7364 |
| corpus_sparsity_ratio | 0.9946 |
<!--
## 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
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 99,000 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: 6 tokens</li><li>mean: 14.1 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.83 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.21 tokens</li><li>max: 75 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What are the best GMAT coaching institutes in Delhi NCR?</code> | <code>Which are the best GMAT coaching institutes in Delhi/NCR?</code> | <code>What are the best GMAT coaching institutes in Delhi-Noida Area?</code> |
| <code>Is a third world war coming?</code> | <code>Is World War 3 more imminent than expected?</code> | <code>Since the UN is unable to control terrorism and groups like ISIS, al-Qaeda and countries that promote terrorism (even though it consumed those countries), can we assume that the world is heading towards World War III?</code> |
| <code>Should I build iOS or Android apps first?</code> | <code>Should people choose Android or iOS first to build their App?</code> | <code>How much more effort is it to build your app on both iOS and Android?</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Evaluation Dataset
#### quora-duplicates
* Dataset: [quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 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: 14.05 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.14 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.56 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------|:------------------------------------------------------------|:-----------------------------------------------------------------|
| <code>What happens if we use petrol in diesel vehicles?</code> | <code>Why can't we use petrol in diesel?</code> | <code>Why are diesel engines noisier than petrol engines?</code> |
| <code>Why is Saltwater taffy candy imported in Switzerland?</code> | <code>Why is Saltwater taffy candy imported in Laos?</code> | <code>Is salt a consumer product?</code> |
| <code>Which is your favourite film in 2016?</code> | <code>What movie is the best movie of 2016?</code> | <code>What will the best movie of 2017 be?</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 12
- `per_device_eval_batch_size`: 12
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `bf16`: 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`: 12
- `per_device_eval_batch_size`: 12
- `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`: 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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: True
- `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
- `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
- `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
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | quora_duplicates_dev_max_ap | NanoMSMARCO_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
|:-------:|:--------:|:-------------:|:---------------:|:---------------------------:|:-----------------------:|:------------------:|:------------------------:|:------------------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
| 0.0242 | 200 | 6.2275 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0485 | 400 | 0.4129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0727 | 600 | 0.3238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0970 | 800 | 0.2795 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1212 | 1000 | 0.255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1455 | 1200 | 0.2367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1697 | 1400 | 0.25 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 1600 | 0.2742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2 | 1650 | - | 0.1914 | 0.6442 | 0.3107 | 0.2820 | 0.1991 | 0.8711 | 0.4157 | - | - | - | - | - | - | - | - | - |
| 0.2182 | 1800 | 0.2102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2424 | 2000 | 0.1797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2667 | 2200 | 0.2021 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2909 | 2400 | 0.1734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3152 | 2600 | 0.1849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3394 | 2800 | 0.1871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3636 | 3000 | 0.1685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3879 | 3200 | 0.1512 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4 | 3300 | - | 0.1139 | 0.6637 | 0.4200 | 0.3431 | 0.1864 | 0.9222 | 0.4679 | - | - | - | - | - | - | - | - | - |
| 0.4121 | 3400 | 0.1165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4364 | 3600 | 0.1518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 3800 | 0.1328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4848 | 4000 | 0.1098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5091 | 4200 | 0.1389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5333 | 4400 | 0.1224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5576 | 4600 | 0.09 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 4800 | 0.1162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 4950 | - | 0.0784 | 0.6666 | 0.4404 | 0.3688 | 0.2239 | 0.9478 | 0.4952 | - | - | - | - | - | - | - | - | - |
| 0.6061 | 5000 | 0.1054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6303 | 5200 | 0.0949 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6545 | 5400 | 0.1315 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6788 | 5600 | 0.1246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7030 | 5800 | 0.1047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7273 | 6000 | 0.0861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7515 | 6200 | 0.103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7758 | 6400 | 0.1062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| **0.8** | **6600** | **0.1275** | **0.0783** | **0.6856** | **0.4666** | **0.3928** | **0.2175** | **0.9434** | **0.5051** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
| 0.8242 | 6800 | 0.1131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8485 | 7000 | 0.0651 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8727 | 7200 | 0.0657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8970 | 7400 | 0.1065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9212 | 7600 | 0.0691 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9455 | 7800 | 0.1136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9697 | 8000 | 0.0834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9939 | 8200 | 0.0867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 8250 | - | 0.0720 | 0.6876 | 0.4688 | 0.3711 | 0.1901 | 0.9408 | 0.4927 | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | - | 0.4666 | 0.3928 | 0.2175 | 0.9434 | 0.4517 | 0.1928 | 0.5024 | 0.7369 | 0.2333 | 0.6849 | 0.2850 | 0.2651 | 0.5848 | 0.3661 |
* The bold row denotes the saved checkpoint.
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.075 kWh
- **Carbon Emitted**: 0.029 kg of CO2
- **Hours Used**: 0.306 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## 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",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMultipleNegativesRankingLoss
```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}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
<!--
## 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.*
-->
|
stewy33/0524_original_augmented_original_subtle_colorless_dreams-6f81ce55
|
stewy33
| 2025-06-19T10:21:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-06-19T10:19:35Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# 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 0.15.1
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb2-seed42-2025-06-19
|
morturr
| 2025-06-19T10:17:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T10:17:37Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_headlines-comb2-seed42-2025-06-19
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-2-7b-hf-LOO_one_liners-COMB_headlines-comb2-seed42-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Khruna/devon
|
Khruna
| 2025-06-19T10:14:39Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-19T10:14:12Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: >-
images/Professional_Mode_woman_shows_her_shiny_plate.00_00_02_11.Still001.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# devon
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Khruna/devon/tree/main) them in the Files & versions tab.
|
altinkedi/xxtrgpt2
|
altinkedi
| 2025-06-19T10:12:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T10:09:59Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
sgonzalezygil/sd-finetuning-dreambooth-v17-1200
|
sgonzalezygil
| 2025-06-19T10:05:10Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-19T10:03:30Z |
---
library_name: diffusers
---
# 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. -->
This is the model card of a 🧨 diffusers 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 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]
|
willystumblr/opencharacter-checkpoint
|
willystumblr
| 2025-06-19T09:56:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T09:56:41Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
NLPGenius/LlamaDastak
|
NLPGenius
| 2025-06-19T09:49:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2024-11-27T09:40:10Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: transformers
model_name: outputs
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for outputs
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "What is the step-by-step procedure for the dinking water service in KP?"
generator = pipeline("text-generation", model="NLPGenius/outputs", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu121
- Datasets: 2.21.0
- Tokenizers: 0.20.4
## 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}}
}
```
|
RubanAgnesh/delete-empathetic-ecommerce
|
RubanAgnesh
| 2025-06-19T09:37:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T09:33:32Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
steven567/q-FrozenLake-v1-4x4-noSlippery
|
steven567
| 2025-06-19T09:33:23Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-19T09:33:02Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="steven567/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Zekrompogu/content
|
Zekrompogu
| 2025-06-19T09:33:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/trocr-base-str",
"base_model:finetune:microsoft/trocr-base-str",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T09:31:31Z |
---
library_name: transformers
base_model: microsoft/trocr-base-str
tags:
- generated_from_trainer
model-index:
- name: microsoft/trocr-base-str
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. -->
# microsoft/trocr-base-str
This model is a fine-tuned version of [microsoft/trocr-base-str](https://huggingface.co/microsoft/trocr-base-str) 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.17.0
- Tokenizers 0.21.1
|
Achalkamble/codeparrot_model
|
Achalkamble
| 2025-06-19T09:26:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T08:27:48Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
townwish/EVACLIP-ViT-L-14-336px
|
townwish
| 2025-06-19T09:24:41Z | 0 | 0 | null |
[
"safetensors",
"clip",
"custom_code",
"arxiv:2303.15389",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T08:42:38Z |
---
license: apache-2.0
---
# EVA-CLIP
> <div align="center">
>
> <h2><a href="http://arxiv.org/abs/2303.15389">EVA-CLIP: Improved Training Techniques for CLIP at Scale</a></h2>
>
> [Quan Sun](https://github.com/Quan-Sun)<sup>1</sup>, [Yuxin Fang](https://github.com/Yuxin-CV)<sup>2,1</sup>, [Ledell Wu](https://scholar.google.com/citations?user=-eJHVt8AAAAJ&hl=en)<sup>1</sup>, [Xinlong Wang](https://www.xloong.wang/)<sup>1</sup>, [Yue Cao](http://yue-cao.me/)<sup>1</sup>
>
> <sup>1</sup>[BAAI](https://www.baai.ac.cn/english.html), <sup>2</sup>[HUST](http://english.hust.edu.cn/)
>
> </div>
>
>
> We launch EVA-CLIP, a series of models that significantly improve the efficiency and effectiveness of CLIP training.
> Our approach incorporates new techniques for representation learning, optimization, and augmentation, enabling EVA-CLIP to achieve superior performance compared to previous CLIP models with the same number of parameters but significantly smaller training costs.
>
> Notably, using exclusively publicly accessible training data, our large-sized EVA-02 CLIP-L/14 can reach up to **80.4** zero-shot top-1 on ImageNet-1K, outperforming the previous largest & best open-modeld CLIP with only ~1/6 parameters and ~1/6 image-text training data.
> Our largest 5.0B-parameter EVA-02 CLIP-E/14 with only 9 billion seen samples achieves **82.0** zero-shot top-1 accuracy on ImageNet-1K.
## Usage
```py
import torch
from modeling_evaclip import EvaCLIPVisionModelWithProjection
model = EvaCLIPVisionModelWithProjection.from_pretrained("townwish/EVACLIP-ViT-L-14-336px")
img_size = model.config.image_size
fake_image = torch.randn(1, 3, img_size, img_size)
with torch.no_grad():
outputs = model(fake_image).image_embeds
```
|
veddhanth/lora-trained-xl-stage-1-597
|
veddhanth
| 2025-06-19T09:24:30Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-19T08:57:34Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a realistic portrait of sks face
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-1-597
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-1-597 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a realistic portrait of sks face to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-1-597/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
Adarsh203/Llama-3.2-3B-Instruct_cot_lora_model_
|
Adarsh203
| 2025-06-19T09:16:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T09:15:38Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Adarsh203
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
CSLin3303/qwen3-laws-20250619
|
CSLin3303
| 2025-06-19T09:11:20Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T09:09:33Z |
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CSLin3303
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Kortix/FastApply-1.5B-v1.0
|
Kortix
| 2025-06-19T09:07:52Z | 2,101 | 34 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"fast-apply",
"instant-apply",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-18T11:55:22Z |
---
base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- fast-apply
- instant-apply
---
# FastApply-1.5B-v1.0
*🚀 Update May 2025:* For production-grade throughput, we use *[Morph](https://morphllm.com)* (the hosted Fast Apply API powering [SoftGen AI](https://softgen.ai/)).
- Morph hits *~4,500 tok/s* even on huge token diffs
- Larger model trained on millions of examples and tuned for accuracy.
> Stable inference, large free tier, highly recommended if you need serious speed in prod.
[Github: kortix-ai/fast-apply](https://github.com/kortix-ai/fast-apply)
[Dataset: Kortix/FastApply-dataset-v1.0](https://huggingface.co/datasets/Kortix/FastApply-dataset-v1.0)
[Try it now on 👉 Google Colab](https://colab.research.google.com/drive/1BNCab4oK-xBqwFQD4kCcjKc7BPKivkm1?usp=sharing)
## Model Details
### Basic Information
- **Developed by:** Kortix
- **License:** apache-2.0
- **Finetuned from model:** [unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit)
### Model Description
FastApply-1.5B-v1.0 is a 1.5B model designed for instant code application, producing full file edits to power [SoftGen AI](https://softgen.ai/).
It is part of the Fast Apply pipeline for data generation and fine-tuning Qwen2.5 Coder models.
The model achieves high throughput when deployed on fast providers like Fireworks while maintaining high edit accuracy, with a speed of approximately 340 tokens/second.
## Intended Use
FastApply-1.5B-v1.0 is intended for use in AI-powered code editors and tools that require fast, accurate code modifications. It is particularly well-suited for:
- Instant code application tasks
- Full file edits
- Integration with AI-powered code editors like Aider and PearAI
- Local tools to reduce the cost of frontier model output
## Inference template
FastApply-1.5B-v1.0 is based on the Qwen2.5 Coder architecture and is fine-tuned for code editing tasks. It uses a specific prompt structure for inference:
```
<|im_start|>system
You are a coding assistant that helps merge code updates, ensuring every modification is fully integrated.<|im_end|>
<|im_start|>user
Merge all changes from the <update> snippet into the <code> below.
- Preserve the code's structure, order, comments, and indentation exactly.
- Output only the updated code, enclosed within <updated-code> and </updated-code> tags.
- Do not include any additional text, explanations, placeholders, ellipses, or code fences.
<code>{original_code}</code>
<update>{update_snippet}</update>
Provide the complete updated code.<|im_end|>
<|im_start|>assistant
```
The model's output is structured as:
```
<updated-code>[Full-complete updated file]</updated-code>
```
## Additional Information
For more details on the Fast Apply pipeline, data generation process, and deployment instructions, please refer to the [GitHub repository](https://github.com/kortix-ai/fast-apply).
## How to Use
To use the model, you can load it using the Hugging Face Transformers library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Kortix/FastApply-1.5B-v1.0", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Kortix/FastApply-1.5B-v1.0")
# Prepare your input following the prompt structure mentioned above
input_text = """<|im_start|>system
You are a coding assistant that helps merge code updates, ensuring every modification is fully integrated.<|im_end|>
<|im_start|>user
Merge all changes from the <update> snippet into the <code> below.
- Preserve the code's structure, order, comments, and indentation exactly.
- Output only the updated code, enclosed within <updated-code> and </updated-code> tags.
- Do not include any additional text, explanations, placeholders, ellipses, or code fences.
<code>{original_code}</code>
<update>{update_snippet}</update>
Provide the complete updated code.<|im_end|>
<|im_start|>assistant
"""
input_text = input_text.format(
original_code=original_code,
update_snippet=update_snippet,
).strip()
# Generate the response
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=8192,)
response = tokenizer.decode(output[0][len(input_ids[0]):])
print(response)
# Extract the updated code from the response
updated_code = response.split("<updated-code>")[1].split("</updated-code>")[0]
```
## Evaluation:
|
morturr/Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb3-seed7-2025-06-19
|
morturr
| 2025-06-19T09:01:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T00:45:48Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_headlines-COMB_one_liners-comb3-seed7-2025-06-19
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-2-7b-hf-LOO_headlines-COMB_one_liners-comb3-seed7-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
ASIEK/dqn-SpaceInvadersNoFrameskip-v4
|
ASIEK
| 2025-06-19T08:58:36Z | 14 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-18T04:31:29Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 645.50 +/- 149.22
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ASIEK -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ASIEK -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ASIEK
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Alphatao/Affine-5878053
|
Alphatao
| 2025-06-19T08:56:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:finetune:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T08:50:32Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B-Base
---
# Qwen3-8B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
makataomu/q-Taxi-v3-v2
|
makataomu
| 2025-06-19T08:55:29Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-19T08:49:25Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="makataomu/q-Taxi-v3-v2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
New-tutorial-kamal-Kaur-19-videos/FULL.VIDEO.kamal.Kaur.viral.video.Link.viral.On.Social.Media.Official
|
New-tutorial-kamal-Kaur-19-videos
| 2025-06-19T08:44:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T08:44:50Z |
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://caddo.gov/wp-content/uploads/ninja-forms/11/xxx-viral-new-video-media-streams-us-tvs-01.pdf)
https://caddo.gov/wp-content/uploads/ninja-forms/11/xxx-viral-new-video-media-streams-us-tvs-01.pdf
|
New-tutorial-Jobz-Hunting-18-Viral-Videos/FULL.VIDEO.Jobz.Hunting.Sajal.Malik.Viral.Video.Tutorial.Official
|
New-tutorial-Jobz-Hunting-18-Viral-Videos
| 2025-06-19T08:41:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-19T08:41:31Z |
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://caddo.gov/wp-content/uploads/ninja-forms/11/xxx-viral-new-video-media-streams-us-uk-01.pdf)
https://caddo.gov/wp-content/uploads/ninja-forms/11/xxx-viral-new-video-media-streams-us-uk-01.pdf
|
John6666/3x3x3mix-xl-celestique-real-mix-v10-sdxl
|
John6666
| 2025-06-19T08:37:34Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"semi-realistic",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-06-19T08:30:49Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- semi-realistic
- pony
---
Original model is [here](https://civitai.com/models/1693612/3x3x3mixxl-celestiquerealmix?modelVersionId=1916711).
This model created by [wagalipagirl](https://civitai.com/user/wagalipagirl).
|
LandCruiser/sn29C1_1906_1
|
LandCruiser
| 2025-06-19T08:35:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:48:06Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
sungkwan2/my_awesome_billsum_model
|
sungkwan2
| 2025-06-19T08:29:04Z | 0 | 0 |
transformers
|
[
"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"
] |
text2text-generation
| 2025-06-19T08:20:38Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_billsum_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. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5008
- Rouge1: 0.1473
- Rouge2: 0.054
- Rougel: 0.1217
- Rougelsum: 0.1215
- Gen Len: 20.0
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.7951 | 0.1351 | 0.0403 | 0.1121 | 0.1121 | 20.0 |
| No log | 2.0 | 124 | 2.5808 | 0.1425 | 0.0509 | 0.1187 | 0.1187 | 20.0 |
| No log | 3.0 | 186 | 2.5186 | 0.1453 | 0.0524 | 0.1194 | 0.1193 | 20.0 |
| No log | 4.0 | 248 | 2.5008 | 0.1473 | 0.054 | 0.1217 | 0.1215 | 20.0 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
LandCruiser/sn29C1_1906_4
|
LandCruiser
| 2025-06-19T08:18:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:48:07Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
khs2617/gemma-3-1b-it-lora-strategy_try_2
|
khs2617
| 2025-06-19T08:15:13Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-3-1b-it",
"base_model:adapter:google/gemma-3-1b-it",
"region:us"
] | null | 2025-06-19T08:14:45Z |
---
base_model: google/gemma-3-1b-it
library_name: peft
---
# 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 0.15.2
|
vk888/nanoVLM
|
vk888
| 2025-06-19T08:12:16Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-06-19T07:10:06Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("vk888/nanoVLM")
```
|
ramzanniaz331/lora_model_llama_3_2_3b_pretraining
|
ramzanniaz331
| 2025-06-19T08:11:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:59:37Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ramzanniaz331
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
thekarthikeyansekar/thirukkural-multilingual-slm
|
thekarthikeyansekar
| 2025-06-19T08:00:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T08:00:39Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
nnilayy/dreamer-arousal-multi-classification-Kfold-2
|
nnilayy
| 2025-06-19T07:53:30Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-19T07:53:26Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
pepematta/josematta
|
pepematta
| 2025-06-19T07:34:15Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-19T05:33:33Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: JSM
---
# Josematta
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `JSM` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "JSM",
"lora_weights": "https://huggingface.co/pepematta/josematta/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('pepematta/josematta', weight_name='lora.safetensors')
image = pipeline('JSM').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/pepematta/josematta/discussions) to add images that show off what you’ve made with this LoRA.
|
rinabuoy/nanoVLM
|
rinabuoy
| 2025-06-19T07:26:08Z | 0 | 1 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-06-19T07:25:26Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("rinabuoy/nanoVLM")
```
|
Fayaz/Llama3.1_8b_law_new
|
Fayaz
| 2025-06-19T07:22:35Z | 0 | 0 |
transformers
|
[
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T07:21:43Z |
---
library_name: transformers
tags:
- unsloth
---
# 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. -->
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 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]
|
HANI-LAB/Med-REFL-Huatuo-o1-8B-lora
|
HANI-LAB
| 2025-06-19T07:08:39Z | 0 | 2 | null |
[
"safetensors",
"medical",
"medical-reasoning",
"lora",
"dpo",
"reflection",
"text-generation",
"en",
"arxiv:2506.13793",
"base_model:FreedomIntelligence/HuatuoGPT-o1-8B",
"base_model:adapter:FreedomIntelligence/HuatuoGPT-o1-8B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-10T12:34:41Z |
---
license: apache-2.0
language:
- en
base_model:
- FreedomIntelligence/HuatuoGPT-o1-8B
pipeline_tag: text-generation
tags:
- medical
- medical-reasoning
- lora
- dpo
- reflection
---
<div align="center">
<h1>
Med-REFL-Huatuo-o1-8B-lora
</h1>
</div>
<div align="center">
<a href="https://github.com/TianYin123/Med-REFL" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2506.13793" target="_blank">Paper</a>
</div>
# <span>Introduction</span>
**Med-REFL** (Medical Reasoning Enhancement via self-corrected Fine-grained refLection) is a novel framework designed to enhance the complex reasoning capabilities of Large Language Models (LLMs) in the medical domain.
Instead of focusing solely on the final answer, Med-REFL improves the model's intermediate reasoning process. It leverages a Tree-of-Thought (ToT) methodology to explore diverse reasoning paths and automatically constructs Direct Preference Optimization (DPO) data. This trains the model to identify and correct its own reasoning errors, leading to more accurate and trustworthy outputs.
This repository contains the LoRA weights produced by the Med-REFL framework for various base models.
# <span>Performance</span>
| Domain | Benchmark | Original | **+ Med-REFL** |
| :--- | :--- | :--- | :--- |
| **In-Domain** | MedQA-USMLE | 69.59 | **73.72** <span style="color: #2E8B57; font-size: small;">(+4.13)</span> |
| **Out-of-Domain**| MedMCQA | 62.13 | **64.66** <span style="color: #2E8B57; font-size: small;">(+2.53)</span> |
| **Out-of-Domain**| GPQA (Med+) | 50.67 | **56.80** <span style="color: #2E8B57; font-size: small;">(+6.13)</span> |
| **Out-of-Domain**| MMLU-Pro (Med+) | 61.87 | **64.97** <span style="color: #2E8B57; font-size: small;">(+3.10)</span> |
# <span>Available Weights</span>
The Med-REFL LoRA weights can be applied to the following base models to enhance their medical reasoning abilities.
| LoRA for Base Model | Backbone | Hugging Face Link |
| :--- | :--- | :--- |
| **Med-REFL for Llama-3.1-8B** | Llama-3.1-8B | [HF Link](https://huggingface.co/HANI-LAB/Med-REFL-Llama-3.1-8B-lora) |
| **Med-REFL for Qwen2.5-7B** | Qwen2.5-7B | [HF Link](https://huggingface.co/HANI-LAB/Med-REFL-Qwen2.5-7B-lora) |
| **Med-REFL for Huatuo-o1-8B** | Huatuo-o1-8b | [HF Link](https://huggingface.co/HANI-LAB/Med-REFL-Huatuo-o1-8B-lora) |
| **Med-REFL for MedReason-8B**| MedReason-8B | [HF Link](https://huggingface.co/HANI-LAB/Med-REFL-MedReason-8B-lora) |
# <span>Usage</span>
You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm). For more usages, please refer to our github page.
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
# Define the paths for the base model and your LoRA adapter on the Hugging Face Hub
base_model_path = "FreedomIntelligence/HuatuoGPT-o1-8B"
lora_path = "HANI-LAB/Med-REFL-Huatuo-o1-8B-lora/huatuo-o1-Med-REFL-LoraAdapter"
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
# Load the base model
base_model = AutoModelForCausalLM.from_pretrained(
base_model_path,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Load and merge your LoRA weights into the base model
model = PeftModel.from_pretrained(base_model, lora_path)
# Prepare the prompt
system_prompt = '''You are a helpful medical expert specializing in USMLE exam questions, and your task is to answer a multi-choice medical question. Please first think step-by-step and then choose the answer from the provided options. Your responses will be used for research purposes only, so please have a definite answer.\nProvide your response in the following JSON format:\n{"reason": "Step-by-step explanation of your thought process","answer": "Chosen answer from the given options"}\n'''
user_prompt = "A 67-year-old man with transitional cell carcinoma of the bladder comes to the physician because of a 2-day history of ringing sensation in his ear. He received this first course of neoadjuvant chemotherapy 1 week ago. Pure tone audiometry shows a sensorineural hearing loss of 45 dB. The expected beneficial effect of the drug that caused this patient's symptoms is most likely due to which of the following actions?\nOptions:\nA: Inhibition of thymidine synthesis\nB: Inhibition of proteasome\nC: Hyperstabilization of microtubules\nD: Generation of free radicals\nE: Cross-linking of DNA"
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
]
# Convert the formatted prompt into input tensors
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
# Generate the response
outputs = model.generate(
input_ids,
max_new_tokens=4096,
do_sample=True,
temperature=0.2,
top_p=0.7,
repetition_penalty=1.1
)
# Decode and print the generated text
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
# <span>📖 Citation</span>
If you use these weights or the Med-REFL framework in your research, please cite our paper:
```
@misc{yang2025medreflmedicalreasoningenhancement,
title={Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection},
author={Zongxian Yang and Jiayu Qian and Zegao Peng and Haoyu Zhang and Zhi-An Huang},
year={2025},
eprint={2506.13793},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2506.13793},
}
```
|
betki/MCP-Course-Model
|
betki
| 2025-06-19T07:05:54Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T07:05:54Z |
---
license: apache-2.0
---
|
vuitton/21v1scrip_34
|
vuitton
| 2025-06-19T06:31:28Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-06-16T15:35:14Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
KoichiYasuoka/modernbert-large-classical-chinese-ud-square
|
KoichiYasuoka
| 2025-06-19T06:24:35Z | 0 | 0 | null |
[
"pytorch",
"modernbert",
"classical chinese",
"literary chinese",
"ancient chinese",
"token-classification",
"pos",
"dependency-parsing",
"lzh",
"dataset:universal_dependencies",
"base_model:KoichiYasuoka/modernbert-large-classical-chinese",
"base_model:finetune:KoichiYasuoka/modernbert-large-classical-chinese",
"license:apache-2.0",
"region:us"
] |
token-classification
| 2025-06-19T06:22:37Z |
---
language:
- "lzh"
tags:
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: KoichiYasuoka/modernbert-large-classical-chinese
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
widget:
- text: "孟子見梁惠王"
---
# modernbert-large-classical-chinese-ud-square
## Model Description
This is a ModernBERT model pretrained on Classical Chinese texts for POS-tagging and dependency-parsing (using `goeswith` for subwords), derived from [modernbert-large-classical-chinese](https://huggingface.co/KoichiYasuoka/modernbert-large-classical-chinese) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-large-classical-chinese-ud-square",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("孟子見梁惠王"))
```
|
JunSotohigashi/denim-gorge-591
|
JunSotohigashi
| 2025-06-19T06:22:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"lora",
"sft",
"dataset:JunSotohigashi/JapaneseWikipediaTypoDataset_kanji",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T02:16:03Z |
---
base_model: meta-llama/Llama-3.1-8B
datasets: JunSotohigashi/JapaneseWikipediaTypoDataset_kanji
library_name: transformers
model_name: JunSotohigashi/denim-gorge-591
tags:
- generated_from_trainer
- lora
- sft
licence: license
---
# Model Card for JunSotohigashi/denim-gorge-591
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the [JunSotohigashi/JapaneseWikipediaTypoDataset_kanji](https://huggingface.co/datasets/JunSotohigashi/JapaneseWikipediaTypoDataset_kanji) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JunSotohigashi/denim-gorge-591", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jun-sotohigashi-toyota-technological-institute/misusing-corpus-jp/runs/0xbtb6zn)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
JunSotohigashi/youthful-sun-580
|
JunSotohigashi
| 2025-06-19T06:22:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"lora",
"sft",
"dataset:JunSotohigashi/JapaneseWikipediaTypoDataset_kanji",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T02:11:36Z |
---
base_model: elyza/Llama-3-ELYZA-JP-8B
datasets: JunSotohigashi/JapaneseWikipediaTypoDataset_kanji
library_name: transformers
model_name: JunSotohigashi/youthful-sun-580
tags:
- generated_from_trainer
- lora
- sft
licence: license
---
# Model Card for JunSotohigashi/youthful-sun-580
This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the [JunSotohigashi/JapaneseWikipediaTypoDataset_kanji](https://huggingface.co/datasets/JunSotohigashi/JapaneseWikipediaTypoDataset_kanji) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JunSotohigashi/youthful-sun-580", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jun-sotohigashi-toyota-technological-institute/misusing-corpus-jp/runs/a86dwdhc)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
nnilayy/dreamer-arousal-multi-classification-Kfold-1
|
nnilayy
| 2025-06-19T06:22:03Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-19T06:22:01Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
humendra/chronos-t5-large-fine-tuned-run-32
|
humendra
| 2025-06-19T06:08:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-19T06:07:15Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
SaraHe/aya_compress_Q1_Q4_16_LayerNORM_layers
|
SaraHe
| 2025-06-19T06:05:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:finetune:CohereLabs/aya-expanse-8b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T06:05:24Z |
---
base_model: CohereForAI/aya-expanse-8b
library_name: transformers
model_name: aya_compress_Q1_Q4_16_LayerNORM_layers
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for aya_compress_Q1_Q4_16_LayerNORM_layers
This model is a fine-tuned version of [CohereForAI/aya-expanse-8b](https://huggingface.co/CohereForAI/aya-expanse-8b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SaraHe/aya_compress_Q1_Q4_16_LayerNORM_layers", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Tun-Wellens/whisper-medium-lb-final-1e-5-cosine-nofreezing-without
|
Tun-Wellens
| 2025-06-19T06:03:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-18T22:05:37Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker
|
veddhanth
| 2025-06-19T05:45:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-19T05:39:17Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of sks sneaker
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks sneaker to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
jusjinuk/Llama-2-70b-hf-3bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:41:44Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T05:22:31Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 2
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Meta-Llama-3-70B-2bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:33:21Z | 13 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Meta-Llama-3-70B",
"base_model:quantized:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"region:us"
] | null | 2025-05-20T20:54:32Z |
---
base_model:
- meta-llama/Meta-Llama-3-70B
base_model_relation: quantized
license: llama3
---
# Model Card
- Base model: `meta-llama/Meta-Llama-3-70B`
- Quantization method: SqueezeLLM
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-70b-hf-3bit-LNQ
|
jusjinuk
| 2025-06-19T05:31:59Z | 29 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T10:46:34Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: LNQ
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-2bit-LNQ
|
jusjinuk
| 2025-06-19T05:31:12Z | 65 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:27:59Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: LNQ
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Meta-Llama-3-70B-2bit-LNQ
|
jusjinuk
| 2025-06-19T05:29:02Z | 64 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Meta-Llama-3-70B",
"base_model:quantized:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"region:us"
] | null | 2025-05-20T10:46:52Z |
---
base_model:
- meta-llama/Meta-Llama-3-70B
base_model_relation: quantized
license: llama3
---
# Model Card
- Base model: `meta-llama/Meta-Llama-3-70B`
- Quantization method: LNQ
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Meta-Llama-3-8B-3bit-LNQ
|
jusjinuk
| 2025-06-19T05:28:21Z | 93 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:quantized:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | null | 2025-05-20T10:26:35Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B
base_model_relation: quantized
license: llama3
---
# Model Card
- Base model: `meta-llama/Meta-Llama-3-8B`
- Quantization method: LNQ
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-70b-hf-4bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:14:45Z | 135 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T12:11:03Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 2
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-70b-hf-3bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:14:36Z | 57 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T11:12:16Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 2
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-4bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:14:04Z | 29 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:58:04Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 4
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-2bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:13:06Z | 90 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:33:21Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 4
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Meta-Llama-3-70B-4bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:10:34Z | 19 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Meta-Llama-3-70B",
"base_model:quantized:meta-llama/Meta-Llama-3-70B",
"license:llama3",
"region:us"
] | null | 2025-05-20T12:06:55Z |
---
base_model:
- meta-llama/Meta-Llama-3-70B
base_model_relation: quantized
license: llama3
---
# Model Card
- Base model: `meta-llama/Meta-Llama-3-70B`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.2-1B-Instruct-4bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:01:33Z | 7 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2025-06-10T13:14:13Z |
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
base_model_relation: quantized
license: llama3.2
---
# Model Card
- Base model: `meta-llama/Llama-3.2-1B-Instruct`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.1-8B-Instruct-3bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T04:58:51Z | 10 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-06-10T08:01:14Z |
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-8B-Instruct`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.1-8B-Instruct-3bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:57:53Z | 1,455 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-05-25T15:41:48Z |
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-8B-Instruct`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
gabriellarson/INTELLECT-2-GGUF
|
gabriellarson
| 2025-06-19T04:54:30Z | 0 | 0 | null |
[
"gguf",
"dataset:PrimeIntellect/Intellect-2-RL-Dataset",
"arxiv:2505.07291",
"base_model:PrimeIntellect/INTELLECT-2",
"base_model:quantized:PrimeIntellect/INTELLECT-2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-19T03:05:38Z |
---
license: apache-2.0
datasets:
- PrimeIntellect/Intellect-2-RL-Dataset
base_model:
- PrimeIntellect/INTELLECT-2
---
# INTELLECT-2
INTELLECT-2 is a 32 billion parameter language model trained through a reinforcement learning run leveraging globally distributed, permissionless GPU resources contributed by the community.
The model was trained using [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl), a framework designed for distributed asynchronous RL, using GRPO over verifiable rewards along with modifications for improved training stability. For detailed information on our infrastructure and training recipe, see our [technical report](https://www.primeintellect.ai/intellect-2).
## Model Information
- Training Dataset (verifiable math & coding tasks): [PrimeIntellect/Intellect-2-RL-Dataset](https://huggingface.co/datasets/PrimeIntellect/INTELLECT-2-RL-Dataset)
- Base Model: [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)
- Training Code: [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl)
## Usage
INTELLECT-2 is based on the `qwen2` architecture, making it compatible with popular libraries and inference engines such as [vllm](https://github.com/vllm-project/vllm) or [sglang](https://github.com/sgl-project/sglang).
Given that INTELLECT-2 was trained with a length control budget, you will achieve the best results by appending the prompt `"Think for 10000 tokens before giving a response."` to your instruction. As reported in our technical report, the model did not train for long enough to fully learn the length control objective, which is why results won't differ strongly if you specify lengths other than 10,000. If you wish to do so, you can expect the best results with 2000, 4000, 6000 and 8000, as these were the other target lengths present during training.
## Performance
During training, INTELLECT-2 improved upon QwQ in its mathematical and coding abilities. Performance on IFEval slightly decreased, which can likely be attributed to the lack of diverse training data and pure focus on mathematics and coding.
| **Model** | **AIME24** | **AIME25** | **LiveCodeBench (v5)** | **GPQA-Diamond** | **IFEval** |
| ------------------- | ---------- | ---------- | ---------------------- | ---------------- | ---------- |
| INTELLECT-2 | **78.8** | 64.9 | **67.8** | 66.8 | 81.5 |
| QwQ-32B | 76.6 | 64.8 | 66.1 | 66.3 | 83.4 |
| Qwen-R1-Distill-32B | 69.9 | 58.4 | 55.1 | 65.2 | 72.0 |
| Deepseek-R1 | 78.6 | 65.1 | 64.1 | 71.6 | 82.7 |
## Citation
Feel free to cite INTELLECT-2:
```
@misc{primeintellectteam2025intellect2reasoningmodeltrained,
title={INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning},
author={Prime Intellect Team and Sami Jaghouar and Justus Mattern and Jack Min Ong and Jannik Straube and Manveer Basra and Aaron Pazdera and Kushal Thaman and Matthew Di Ferrante and Felix Gabriel and Fares Obeid and Kemal Erdem and Michael Keiblinger and Johannes Hagemann},
year={2025},
eprint={2505.07291},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.07291},
}
```
|
jusjinuk/gemma-3-27b-it-4bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T04:53:34Z | 19 | 0 | null |
[
"pytorch",
"gemma3",
"arxiv:2505.07004",
"base_model:google/gemma-3-27b-it",
"base_model:quantized:google/gemma-3-27b-it",
"license:gemma",
"region:us"
] | null | 2025-06-02T03:34:56Z |
---
base_model:
- google/gemma-3-27b-it
base_model_relation: quantized
license: gemma
---
# Model Card
- Base model: `google/gemma-3-27b-it`
- Quantization method: SqueezeLLM
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
sinngam-khaidem/llava15_7B_mmsd2
|
sinngam-khaidem
| 2025-06-19T04:31:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llava",
"trl",
"en",
"base_model:unsloth/llava-1.5-7b-hf",
"base_model:finetune:unsloth/llava-1.5-7b-hf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T04:31:31Z |
---
base_model: unsloth/llava-1.5-7b-hf
tags:
- text-generation-inference
- transformers
- unsloth
- llava
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sinngam-khaidem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llava-1.5-7b-hf
This llava model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
KasuleTrevor/all_mini_lm_text_classifier_331h_whisper
|
KasuleTrevor
| 2025-06-19T04:31:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:KasuleTrevor/all-MiniLM-L6-v2_intents",
"base_model:finetune:KasuleTrevor/all-MiniLM-L6-v2_intents",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-19T04:18:34Z |
---
library_name: transformers
base_model: KasuleTrevor/all-MiniLM-L6-v2_intents
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: all_mini_lm_text_classifier_331h_whisper
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. -->
# all_mini_lm_text_classifier_331h_whisper
This model is a fine-tuned version of [KasuleTrevor/all-MiniLM-L6-v2_intents](https://huggingface.co/KasuleTrevor/all-MiniLM-L6-v2_intents) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3747
- Accuracy: 0.927
- Precision: 0.932
- Recall: 0.927
- F1: 0.927
## 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: 8
- eval_batch_size: 8
- seed: 42
- 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----:|
| 2.7544 | 1.0 | 232 | 1.9815 | 0.441 | 0.327 | 0.441 | 0.316 |
| 1.58 | 2.0 | 464 | 0.9619 | 0.693 | 0.72 | 0.693 | 0.631 |
| 0.8075 | 3.0 | 696 | 0.4160 | 0.914 | 0.904 | 0.914 | 0.906 |
| 0.4276 | 4.0 | 928 | 0.2829 | 0.935 | 0.925 | 0.935 | 0.928 |
| 0.2676 | 5.0 | 1160 | 0.2485 | 0.928 | 0.919 | 0.928 | 0.921 |
| 0.2006 | 6.0 | 1392 | 0.2550 | 0.941 | 0.945 | 0.941 | 0.942 |
| 0.1547 | 7.0 | 1624 | 0.2331 | 0.951 | 0.955 | 0.951 | 0.952 |
| 0.1236 | 8.0 | 1856 | 0.2301 | 0.953 | 0.955 | 0.953 | 0.953 |
| 0.0985 | 9.0 | 2088 | 0.2265 | 0.948 | 0.951 | 0.948 | 0.948 |
| 0.0789 | 10.0 | 2320 | 0.2813 | 0.941 | 0.944 | 0.941 | 0.942 |
| 0.0649 | 11.0 | 2552 | 0.2906 | 0.943 | 0.947 | 0.943 | 0.944 |
| 0.0536 | 12.0 | 2784 | 0.2782 | 0.948 | 0.951 | 0.948 | 0.948 |
| 0.0444 | 13.0 | 3016 | 0.3242 | 0.932 | 0.937 | 0.932 | 0.932 |
| 0.0394 | 14.0 | 3248 | 0.2991 | 0.937 | 0.94 | 0.937 | 0.937 |
| 0.0301 | 15.0 | 3480 | 0.3409 | 0.928 | 0.934 | 0.928 | 0.929 |
| 0.0247 | 16.0 | 3712 | 0.3552 | 0.93 | 0.935 | 0.93 | 0.93 |
| 0.0205 | 17.0 | 3944 | 0.3668 | 0.93 | 0.935 | 0.93 | 0.93 |
| 0.0162 | 18.0 | 4176 | 0.3730 | 0.927 | 0.932 | 0.927 | 0.927 |
| 0.0168 | 19.0 | 4408 | 0.3745 | 0.927 | 0.932 | 0.927 | 0.927 |
| 0.014 | 20.0 | 4640 | 0.3747 | 0.927 | 0.932 | 0.927 | 0.927 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
kankichi57301/segformer-b0-scene-parse-150
|
kankichi57301
| 2025-06-19T04:02:03Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T07:43:28Z |
---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
datasets:
- scene_parse_150
model-index:
- name: segformer-b0-scene-parse-150
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. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5670
- Mean Iou: 0.0774
- Mean Accuracy: 0.1223
- Overall Accuracy: 0.5135
- Per Category Iou: [0.4213891557064807, 0.40007258150986036, 0.8231900478645917, 0.31773881264336534, 0.28457760460984965, 0.5986723895121013, 0.3926239843434319, 0.05552524795943285, 0.3580998394812543, 0.6047520580036383, 0.0, 0.0, 0.6585019058951657, 0.03675010419475839, 0.0, 0.0, 0.0, 0.0035096310806398954, 0.0, 0.02546663313395952, 0.5147571035747021, 0.0, 0.0, 0.0033711666102861407, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.384688111763659, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
- Per Category Accuracy: [0.9070256204963971, 0.5848030301508043, 0.8408483946668286, 0.8314708252280996, 0.46407965528154443, 0.8074403982184962, 0.891030421548643, 0.06024205049576947, 0.5533670612505526, 0.6868088585017836, 0.0, 0.0, 0.8836340755475267, 0.03675010419475839, 0.0, 0.0, 0.0, 0.0035684647302904565, 0.0, 0.061115776214967985, 0.8697537556140622, nan, 0.0, 0.005954711227412276, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5638245842843169, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan]
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.7722 | 0.5 | 20 | 4.9346 | 0.0068 | 0.0204 | 0.1746 | [0.23264604678879683, 0.005805919273889585, 0.6233954313430884, 0.015344170824615054, 0.0001419849495953429, 0.003131647949339534, 2.2034682590397284e-05, 0.0, 0.0036496350364963502, 0.003120170943962617, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015504157602895563, 0.0, 0.0, 0.0, 0.0002785709311233373, 0.0, 0.0, 0.0, 0.0035981168143829883, 0.0, 0.004043204528389072, 0.0, 0.004859554188724426, 0.03552857756768371, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00023686579184234213, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0007068414397574748, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0023220278044103547, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001279304058592126, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] | [0.35624028634672444, 0.005825203701285001, 0.8525752307492455, 0.017458470003174267, 0.00014606714219636292, 0.0031891739384534773, 2.209663965352469e-05, 0.0, 0.0037943906871653814, 0.0031361474435196196, 0.0, 0.0, 0.0, 0.0, 0.0, 0.042040710776164614, 0.0, 0.0, 0.0, 0.001049648367796788, 0.0, nan, 0.0, 0.018770733315476067, 0.0, 0.0048006858122588945, nan, 0.018047866079602425, 0.03887798168373285, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0022415493589168834, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.004024864719824695, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.005584780572169777, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.13377926421404682, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 4.4785 | 1.0 | 40 | 4.5788 | 0.0129 | 0.0372 | 0.2868 | [0.3484589918619191, 0.023607431835793193, 0.7023338590169422, 0.09585241213333541, 0.05399417586563857, 0.07921791381090977, 0.005852308512741299, 0.005145816849323524, 0.0020840207707403485, 0.06974059134811086, 0.0, 0.0, 0.05295184098625441, 0.0, 0.0, 0.006912051316567268, 0.0, 0.0008168266285480906, 0.0, 0.007909610787244782, 0.022168371926045594, 0.0, 0.0, 0.0064893920128597125, 0.0, 0.000656407264240391, 0.0, 0.0, 0.06924187207438537, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.005356334841628959, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001459992415623815, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan] | [0.8005875288466067, 0.02369063526669547, 0.9414181188137186, 0.1996858289314114, 0.13586678676631692, 0.08501944741137825, 0.007032255569734232, 0.005169080219389969, 0.0029470995628468983, 0.0700282401902497, 0.0, 0.0, 0.05434334003152888, 0.0, 0.0, 0.014543081261467607, 0.0, 0.000995850622406639, 0.0, 0.037419964311955496, 0.1530122347839554, nan, 0.0, 0.031442523643706345, 0.0, 0.0006943849121303044, nan, 0.0, 0.10207968399148847, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.08490988971577154, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.001460878803976626, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 4.6482 | 1.5 | 60 | 4.1535 | 0.0144 | 0.0409 | 0.3092 | [0.2840689095339594, 0.03765912871033777, 0.6311113803600676, 0.09238418198837846, 0.039226914817466, 0.07563740180183587, 0.03410427009884133, 0.0, 0.0, 0.12753666975994704, 0.0, 0.0, 0.0, 0.0, 0.0, 8.922895033293552e-05, 0.01122990054171802, 0.0, 0.0, 0.0012196665477683553, 0.06042466210579009, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0020454545454545456, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8895669476757877, 0.03785277244732903, 0.9842174433101698, 0.21286717726247933, 0.08004479392360689, 0.08062110799862961, 0.053998663153300965, 0.0, 0.0, 0.1317553507728894, 0.0, 0.0, 0.0, 0.0, 0.0, 9.851367492950115e-05, 0.1047537564836105, 0.0, 0.0, 0.0027815681746614883, 0.44866036859222547, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0021161284254828886, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 4.4625 | 2.0 | 80 | 4.1918 | 0.0252 | 0.0614 | 0.3685 | [0.34430440677701024, 0.3451462970039025, 0.7124157753837798, 0.11374745212939397, 0.047227158407861645, 0.20291379325647238, 0.22597261654441334, 0.0, 0.0, 0.1613950122865965, 0.0, 0.0, 0.07595558091093628, 0.0, 0.0, 0.009249878424379965, 0.0005736888191068571, 0.0, 0.0, 0.01602915692914262, 0.06172912276565332, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0014428472185111954, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9331394527386615, 0.44286417570052145, 0.7434739845093067, 0.29954909126425366, 0.2367991820240037, 0.23037121380060863, 0.6441336183799848, 0.0, 0.0, 0.16741973840665875, 0.0, 0.0, 0.07746902930291405, 0.0, 0.0, 0.01124287315132932, 0.002031976899631036, 0.0, 0.0, 0.10583079668311116, 0.6487532910020133, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.002420873307630234, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.443 | 2.5 | 100 | 3.8183 | 0.0269 | 0.0625 | 0.3820 | [0.34071730094887837, 0.3395644283121597, 0.7794551946350704, 0.0933331055513321, 0.03765763977844733, 0.045566249299196414, 0.23403390882779904, 0.0, 0.0, 0.15101261780454656, 0.0, 0.0, 0.013026416926251063, 0.0, 0.0, 0.001479307171727761, 0.0067303429653810505, 0.0, 0.0, 0.0011128775834658188, 0.135993320514118, 0.0, 0.0, 0.0004152577564216646, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9260949936419725, 0.39475349883958083, 0.9586734891850263, 0.2223330050544102, 0.20325242836623902, 0.04913746196166945, 0.8496213188379377, 0.0, 0.0, 0.15111474435196195, 0.0, 0.0, 0.013071630097225324, 0.0, 0.0, 0.0015639045895058307, 0.01786000748623068, 0.0, 0.0, 0.005143277002204262, 0.8324299210159517, nan, 0.0, 0.000432695279500546, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 4.2625 | 3.0 | 120 | 3.7568 | 0.0290 | 0.0623 | 0.3995 | [0.32921696743868784, 0.40674123899067227, 0.8039807983078715, 0.10025477732703686, 0.06485865724381626, 0.08822787280941885, 0.2703681931461595, 0.0, 0.0, 0.14822582431783776, 0.0, 0.0, 0.04679282478514188, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.036095336046147654, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9728665285169312, 0.6157957662282861, 0.8208161041125749, 0.20225372976404613, 0.17873749300094943, 0.09336772737349107, 0.9019516856973976, 0.0, 0.0, 0.14916765755053507, 0.0, 0.0, 0.046894134681091465, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6250580765061174, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.5158 | 3.5 | 140 | 3.4686 | 0.0303 | 0.0629 | 0.3950 | [0.3011542243357567, 0.42363566000440794, 0.8521897163976807, 0.0965025366938394, 0.05025471042441554, 0.025374158867661846, 0.3516823437063449, 6.200187132920739e-05, 0.0, 0.10887123591354148, 0.0, 0.0, 0.022373736695880924, 0.0, 0.0, 0.0, 2.6575954076751355e-05, 0.0, 0.0, 0.0, 0.07148742243621102, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9960179908632789, 0.49051470366612077, 0.8931341238951789, 0.22146618591439246, 0.16018696594201134, 0.026445456560730336, 0.8761372864221674, 6.20064148454631e-05, 0.0, 0.10949019024970273, 0.0, 0.0, 0.02255735752802755, 0.0, 0.0, 0.0, 5.347307630607989e-05, 0.0, 0.0, 0.0, 0.8599969025863404, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.1906 | 4.0 | 160 | 3.4524 | 0.0316 | 0.0666 | 0.4015 | [0.3294081905791065, 0.4296261515547418, 0.7679935594386041, 0.11293295275370206, 0.06938710326144819, 0.1512469685452637, 0.2580952410840939, 0.0, 0.0, 0.11861429222468871, 0.0, 0.0, 0.08271023116212943, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08149794238683128, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9855790514764753, 0.5662805300754523, 0.8062971642010113, 0.23813922824609524, 0.28558560751758894, 0.1684165978114105, 0.9086193467128486, 0.0, 0.0, 0.1199241973840666, 0.0, 0.0, 0.08501972246466533, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7667647514325538, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.3223 | 4.5 | 180 | 3.3679 | 0.0420 | 0.0787 | 0.4359 | [0.35985890172247764, 0.4019150756690028, 0.745944534056749, 0.167789354708794, 0.06282208367869112, 0.1052884484618802, 0.29933212011585375, 0.0012348357167887597, 0.0, 0.48340418975045, 0.0, 0.0, 0.5011873552543409, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005737487863006444, 0.062037037037037036, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9521558423209155, 0.6182231018858068, 0.8238798265015361, 0.42047646565686986, 0.1697543637558731, 0.11701698877491384, 0.9031835733580815, 0.0012514021905175282, 0.0, 0.5129236028537455, 0.0, 0.0, 0.576890548778425, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0006822714390679123, 0.726343503174849, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.7853 | 5.0 | 200 | 3.3221 | 0.0351 | 0.0648 | 0.4024 | [0.30863100190029163, 0.5086277300449416, 0.711078168155528, 0.13503872808805545, 0.04745350016909029, 0.054643406875320676, 0.27440415522613265, 0.0, 0.0, 0.3105837387074357, 0.0, 0.0, 0.12244682046907751, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12158931662682385, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9959073140865634, 0.6419860749701104, 0.7153656812480282, 0.32353108747141124, 0.08540058913747353, 0.05687612099715846, 0.835844064013965, 0.0, 0.0, 0.3321417954815696, 0.0, 0.0, 0.12529008599400546, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6853027721852253, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.3482 | 5.5 | 220 | 3.1096 | 0.0420 | 0.0723 | 0.4255 | [0.34998513954179555, 0.43090522432783107, 0.7927202057442717, 0.15231827424210695, 0.1031253683048868, 0.0927226224231742, 0.29762323136631724, 0.0, 0.0, 0.3594222614585293, 0.0, 0.0, 0.17099439074961165, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.35589155370177267, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9844063486082985, 0.5952538354113711, 0.8491842164310671, 0.4173469636912659, 0.46863208121333105, 0.09753431007033313, 0.8608077426625346, 0.0, 0.0, 0.36912901307966706, 0.0, 0.0, 0.17799608933633737, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5285736410097569, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.5064 | 6.0 | 240 | 3.1201 | 0.0493 | 0.0862 | 0.4545 | [0.3674694708128321, 0.4223641255605381, 0.826376941579355, 0.16505652620760533, 0.17579346906460303, 0.16083599927270226, 0.277313467826732, 0.0, 0.0, 0.5987111551078242, 0.0, 0.0, 0.5639548068592163, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1361790770751686, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9884695992087787, 0.5914319873810696, 0.8549320700539694, 0.41175130022871004, 0.5513547727438712, 0.16489490336752585, 0.8756124912303961, 0.0, 0.0, 0.687685790725327, 0.0, 0.0, 0.6561863586849886, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5973362242527489, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.2608 | 6.5 | 260 | 3.0519 | 0.0525 | 0.0895 | 0.4706 | [0.4055868626633699, 0.40732423781911414, 0.9099319040969384, 0.1611970299872831, 0.15084911569509252, 0.27583344666363413, 0.26436971010835963, 0.0, 0.0, 0.6310371924173448, 0.0, 0.0, 0.516632899000601, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00021014289717007564, 0.16364672364672364, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9784274478406254, 0.5784312740498126, 0.9608042608685383, 0.4797416634788341, 0.5218005209728072, 0.2963412668023619, 0.8508366340188815, 0.0, 0.0, 0.7221239595719382, 0.0, 0.0, 0.5641572113855793, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.00023617088275427732, 0.667182902276599, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.8075 | 7.0 | 280 | 3.0128 | 0.0496 | 0.0864 | 0.4572 | [0.3805448751724067, 0.4504342631036978, 0.7971077518222591, 0.18977938630999214, 0.12957393247409557, 0.33695012619419973, 0.2862777419156523, 0.0, 0.0, 0.6198228878888444, 0.0, 0.0, 0.2664019052686251, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.21096307224194366, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9609228559318043, 0.6029618317542925, 0.8287436545011919, 0.6135126116080513, 0.33456678920076927, 0.43854920295842487, 0.8231605928528419, 0.0, 0.0, 0.7953180737217598, 0.0, 0.0, 0.28456505706997925, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7086882453151618, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.0423 | 7.5 | 300 | 2.9816 | 0.0558 | 0.0993 | 0.4875 | [0.4051151828067635, 0.38149801004934975, 0.8920188225054894, 0.21012895758312677, 0.19357450895463363, 0.4353747656865497, 0.32610481917630124, 0.0, 0.00065081782012869, 0.5429491876441984, 0.0, 0.0, 0.5515051173991571, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.24550243514561176, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9493347619271888, 0.5811097826851396, 0.9821322225652601, 0.585131406525968, 0.7507120773182072, 0.48445214727635477, 0.8396888793136784, 0.0, 0.00065081782012869, 0.666327288941736, 0.0, 0.0, 0.7437432764325005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.765061173919777, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.3296 | 8.0 | 320 | 2.9934 | 0.0545 | 0.0916 | 0.4644 | [0.3569585407592784, 0.46658814491566347, 0.7912837202717541, 0.24257224905894387, 0.18256983045531375, 0.2713017726517216, 0.2974443669478477, 0.0, 0.0, 0.6347536330258245, 0.0, 0.0, 0.5430431523464937, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.24824633794099443, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9826249234681863, 0.588247114022485, 0.810931550435855, 0.6813239136273735, 0.5101394941207975, 0.32401100340581607, 0.7491589466531877, 0.0, 0.0, 0.76445451843044, 0.0, 0.0, 0.622485639474699, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7453925971813535, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.5202 | 8.5 | 340 | 2.9020 | 0.0565 | 0.0947 | 0.4701 | [0.3666900118095858, 0.36648454120315266, 0.8531000612901336, 0.21918176338004983, 0.18965159344921392, 0.47197387221882015, 0.29383651201587085, 0.0, 0.0, 0.639443440302834, 0.0, 0.0, 0.595366641111747, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1844474761255116, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.819284839636415, 0.6286156350155225, 0.9087699053384013, 0.7368776604836281, 0.42259658689777735, 0.6989581024163156, 0.720019003110102, 0.0, 0.0, 0.6967300832342449, 0.0, 0.0, 0.7461045595090696, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6281554901657116, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.9397 | 9.0 | 360 | 2.8181 | 0.0578 | 0.0942 | 0.4781 | [0.38127422660630456, 0.43972128420586704, 0.8059774819548174, 0.22026420780130443, 0.24133561156201053, 0.38274480987718124, 0.3218665215189235, 0.0, 0.0, 0.6216751260661318, 0.0, 0.0, 0.5985957760744252, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2624290437277774, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9545224414825978, 0.6188118513457849, 0.8358428526359654, 0.685938809893947, 0.41261533218102586, 0.4776153241571109, 0.8460582356938069, 0.0, 0.0, 0.8401530915576695, 0.0, 0.0, 0.6477831679082009, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6515409632956481, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.3682 | 9.5 | 380 | 2.8357 | 0.0622 | 0.0988 | 0.4892 | [0.3943019457688491, 0.489329699998112, 0.7877540913277731, 0.2510226471935977, 0.2085563981372879, 0.4305237364194615, 0.27407909326105706, 0.0, 0.0, 0.7005248469196484, 0.0, 0.0, 0.6336613635121098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4301783264746228, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9374982338812227, 0.6249505189233722, 0.8426164459133216, 0.8538372007846138, 0.4372033011174136, 0.7347040567501663, 0.6144799279649548, 0.0, 0.0, 0.7498885255648038, 0.0, 0.0, 0.7841827863140304, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.728511692736565, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.1605 | 10.0 | 400 | 2.8080 | 0.0576 | 0.0943 | 0.4765 | [0.3664803953465878, 0.44536265776161277, 0.8057122608783482, 0.27475559253974324, 0.3801216290926961, 0.27644968393812647, 0.2842132604103876, 0.0, 0.0, 0.6423146597786147, 0.0, 0.0, 0.5977701127089001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.24814105452906715, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.981426317524608, 0.6402137984387087, 0.8209173284205803, 0.8008350764672847, 0.6025756506073958, 0.31001491304084966, 0.6407362600332555, 0.0, 0.0, 0.8353002378121284, 0.0, 0.0, 0.6660036129660828, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6822053585256311, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.1234 | 10.5 | 420 | 2.8147 | 0.0542 | 0.0924 | 0.4593 | [0.34250898856296874, 0.32254588262138995, 0.9174984298682348, 0.25300016056420543, 0.22138901707429626, 0.5527822600471123, 0.32052765857864896, 0.0, 0.0, 0.18871135552913199, 0.0, 0.0, 0.6142363276335511, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2784287123828318, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7684300381481656, 0.6248801904895863, 0.9833671591229252, 0.8400250685723123, 0.6540399737079144, 0.6230678543358659, 0.6960993906851616, 0.0, 0.0, 0.18871135552913199, 0.0, 0.0, 0.7597445213496525, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6992411336533995, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 3.3001 | 11.0 | 440 | 2.8009 | 0.0584 | 0.0962 | 0.4814 | [0.3650316639649382, 0.472128591925004, 0.7995504960988178, 0.2788911346966387, 0.34430991922889626, 0.2857926921078067, 0.33841093440633585, 0.0, 0.0, 0.5912131822447353, 0.0, 0.0, 0.631826053023097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2144180790960452, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9796719728724156, 0.6341112997699255, 0.812033208321313, 0.8321789310044521, 0.4991601139323709, 0.29645210697083896, 0.7610137938273037, 0.0, 0.0, 0.8708531510107016, 0.0, 0.0, 0.7010913322643284, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7347065200557534, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.3062 | 11.5 | 460 | 2.7428 | 0.0597 | 0.1021 | 0.4663 | [0.33856077394996614, 0.3549633860128141, 0.8112245730491454, 0.2620079907395194, 0.4036655503785673, 0.4064820085506316, 0.3129697182789083, 1.127389360826602e-05, 0.0, 0.6567312845126867, 0.0, 0.0, 0.6377347362212603, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23394924858339491, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7344251872085904, 0.6324334642781791, 0.8387209971269167, 0.8649389970943245, 0.5399371911288555, 0.8454585760061264, 0.7080757693773719, 1.127389360826602e-05, 0.0, 0.8378790130796671, 0.0, 0.0, 0.8193584617154146, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7353260027876722, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.4235 | 12.0 | 480 | 2.7353 | 0.0611 | 0.1001 | 0.4842 | [0.37643467245678014, 0.4596187537665637, 0.8260886509448209, 0.2515867512934023, 0.37965766359902875, 0.31795948402735696, 0.3328306080330211, 0.0, 0.003941745665307726, 0.6066723894911985, 0.0, 0.0, 0.6342017389795963, 0.0, 0.0, 0.0, 0.02833711649703449, 0.0, 0.0, 0.0, 0.30128246439389056, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9251024348890877, 0.6267850863532698, 0.8582657239309448, 0.8830241814053051, 0.6166467853056455, 0.3700802079764616, 0.718046878021025, 0.0, 0.003941745665307726, 0.8508472057074911, 0.0, 0.0, 0.7915710989776794, 0.0, 0.0, 0.0, 0.041388161060905836, 0.0, 0.0, 0.0, 0.7240204429301533, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.3795 | 12.5 | 500 | 2.7458 | 0.0587 | 0.0986 | 0.4566 | [0.3247253561049852, 0.32418163043085196, 0.8114292985883185, 0.2600999029450009, 0.26864789102536024, 0.5361544681580946, 0.3280981871641592, 0.0, 0.0, 0.6866491521375687, 0.0, 0.0, 0.6702127659574468, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.18934218231504976, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7486094758159468, 0.6484844222519165, 0.8283370701973706, 0.8474072747694587, 0.597268544440928, 0.6614437435763084, 0.7076559332239549, 0.0, 0.0, 0.7264417360285375, 0.0, 0.0, 0.7770515761057097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.751122812451603, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.6853 | 13.0 | 520 | 2.7218 | 0.0621 | 0.0977 | 0.4878 | [0.3744369794817338, 0.5074940785443108, 0.8064720916539754, 0.2628991820092689, 0.3515340706386015, 0.4441108717889012, 0.2818125196124112, 0.0, 0.0, 0.6761454333984108, 0.0, 0.0, 0.5956386219412316, 0.0, 0.0, 0.0, 0.021711976487876563, 0.0, 0.0, 0.0, 0.2701399506056686, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9743936325531013, 0.6341735906684215, 0.8272657796043141, 0.8449329741256522, 0.47975752854395404, 0.5381088651982023, 0.6548615369317711, 0.0, 0.0, 0.8467746730083234, 0.0, 0.0, 0.6839601897145486, 0.0, 0.0, 0.0, 0.03160258809689322, 0.0, 0.0, 0.0, 0.7114759176087967, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.6186 | 13.5 | 540 | 2.7203 | 0.0590 | 0.0983 | 0.4753 | [0.3500521838135298, 0.36213981360332415, 0.7976328642768159, 0.2764512418051424, 0.1489461848229695, 0.49799574707305744, 0.3465683707314453, 0.0, 0.0, 0.7116124234160853, 0.0, 0.0, 0.6667328907358046, 0.0, 0.0, 0.0, 3.2902313032606195e-05, 0.0, 0.0, 0.0, 0.20609406049900642, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7819172985447181, 0.6329036600926325, 0.8846650234587334, 0.8733101096343081, 0.2773328139834944, 0.821204731867556, 0.7128541677024467, 0.0, 0.0, 0.7466483353151011, 0.0, 0.0, 0.8174098957381886, 0.0, 0.0, 0.0, 0.00010694615261215978, 0.0, 0.0, 0.0, 0.7227814774663156, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.3088 | 14.0 | 560 | 2.6945 | 0.0606 | 0.1018 | 0.4706 | [0.3413745179238439, 0.3307422514354005, 0.8619139832781608, 0.27090630879989536, 0.4004125971053734, 0.4819001640766678, 0.31802953493497904, 0.0, 0.0, 0.6417413730966753, 0.0, 0.0, 0.6667092114849738, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23475869915615816, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7684194414355013, 0.6395165422523184, 0.889066593785165, 0.8595508818765617, 0.6048153467877401, 0.5904153483404204, 0.7173729305115925, 0.0, 0.0, 0.8469307372175982, 0.0, 0.0, 0.8482080635449016, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7669196221155336, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.616 | 14.5 | 580 | 2.7210 | 0.0634 | 0.0991 | 0.4829 | [0.3772812039947381, 0.41412856443745166, 0.81815476677076, 0.25550691937974856, 0.334551388938163, 0.503223386548515, 0.30000240477106577, 0.0, 0.0, 0.7257033074458263, 0.0, 0.0, 0.6422282878129113, 0.0, 0.0, 0.0, 0.014128903580345064, 0.0, 0.0, 0.0, 0.3687438665358194, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8958731691235341, 0.6325520179237037, 0.8442242253388061, 0.8530314252460057, 0.4591377169705675, 0.6488986517804962, 0.6891555216740414, 0.0, 0.0, 0.7944337098692034, 0.0, 0.0, 0.7625388190878275, 0.0, 0.0, 0.0, 0.054168226298058925, 0.0, 0.0, 0.0, 0.6983119095555211, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.8175 | 15.0 | 600 | 2.7777 | 0.0597 | 0.1005 | 0.4623 | [0.35092145737613534, 0.3704267201477281, 0.8174505570287421, 0.2563355468220626, 0.3122945965091568, 0.4469174333294515, 0.3006064769963819, 0.0003487377941772038, 0.0, 0.5989064783915717, 0.0, 0.0, 0.631512043779894, 0.0, 0.0, 0.0, 0.04115162638480419, 0.0, 0.0, 0.0, 0.35161971830985916, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.06200496789614285, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8353423915603071, 0.6134889936001126, 0.8399188181049797, 0.8621309914294784, 0.6546729313240987, 0.48337397472844157, 0.6719311910641189, 0.00034949070185624656, 0.0, 0.6455335909631391, 0.0, 0.0, 0.7932557966454895, 0.0, 0.0, 0.0, 0.19902679001122936, 0.0, 0.0, 0.0, 0.7732693201177017, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.06470492337789371, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.7604 | 15.5 | 620 | 2.6876 | 0.0608 | 0.1019 | 0.4766 | [0.34074525703558955, 0.39392844874618604, 0.8338804291414061, 0.2788654777272403, 0.28930663003398166, 0.5292636033809578, 0.34050315535559683, 0.0, 0.0, 0.6238380966549305, 0.0, 0.0, 0.6293732103843841, 0.0, 0.0, 0.0, 0.055775806531542696, 0.0, 0.0, 0.0, 0.24513073316901068, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8343757358828239, 0.6267710206665126, 0.8592847152981984, 0.8430935269365065, 0.4870608856537722, 0.6754347957517987, 0.7517166326930832, 0.0, 0.0, 0.8414164684898929, 0.0, 0.0, 0.7153131575564441, 0.0, 0.0, 0.0, 0.12036789476498583, 0.0, 0.0, 0.0, 0.785504104073099, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.4291 | 16.0 | 640 | 2.6727 | 0.0607 | 0.1016 | 0.4868 | [0.4008332073812501, 0.38702429296280444, 0.8632965098052593, 0.23925252418459586, 0.2269614918307257, 0.5571082516005064, 0.3126285449082782, 0.0, 0.0, 0.6829834549592733, 0.0, 0.0, 0.6415073031170004, 0.0, 0.0, 0.0, 0.0003425024082200578, 0.0, 0.0, 0.0, 0.23702639612138623, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8586940140347572, 0.634073121477299, 0.8966432332393635, 0.920932257880729, 0.47851595783528494, 0.616427520606195, 0.7070206548339162, 0.0, 0.0, 0.7782996432818073, 0.0, 0.0, 0.8070784365464374, 0.0, 0.0, 0.0, 0.0008555692208972783, 0.0, 0.0, 0.0, 0.817717206132879, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.0919 | 16.5 | 660 | 2.7082 | 0.0639 | 0.1019 | 0.5001 | [0.388963664607155, 0.48247224287370777, 0.8302806866167262, 0.2791128467007109, 0.2487091222030981, 0.6187175588999937, 0.31870195397101175, 0.0, 0.0034996807308806917, 0.6545066514903607, 0.0, 0.0, 0.6222386790641543, 0.0, 0.0, 0.0, 0.013549423724623798, 0.0, 0.0, 0.0, 0.2655611868326527, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9802748080817595, 0.6337496106818844, 0.8571539436146863, 0.8394512587190611, 0.45731187769311293, 0.6930382297817457, 0.7397347298409594, 0.0, 0.0034996807308806917, 0.6837395957193817, 0.0, 0.0, 0.767457595009506, 0.0, 0.0, 0.0, 0.04689588792043206, 0.0, 0.0, 0.0, 0.8358370760415054, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.3036 | 17.0 | 680 | 2.6872 | 0.0587 | 0.0968 | 0.4697 | [0.3642904047582657, 0.3592732536576704, 0.8086038391829761, 0.27482732616820404, 0.2240590960819379, 0.4858774277932221, 0.27724942006451, 0.0, 0.0, 0.6428704065363411, 0.0, 0.0, 0.7092953070771087, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2545484801420013, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8615421749164037, 0.6423437452905066, 0.8286981035625895, 0.876516933495031, 0.5368210920953331, 0.5221730718848875, 0.6087348016550383, 0.0, 0.0, 0.7461281212841855, 0.0, 0.0, 0.8268753256067279, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7107015641938981, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.1857 | 17.5 | 700 | 2.6426 | 0.0660 | 0.1095 | 0.5056 | [0.4243172917121508, 0.4197431076593277, 0.8356735031544251, 0.2521133076037894, 0.27120081411126185, 0.6153296125660925, 0.3405670039644293, 0.0007158922441248922, 0.008635395710537772, 0.6305677993949215, 0.0, 0.0, 0.6384760793934885, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.28221914008321775, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.22979053984726558, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8970235011538643, 0.6240020897591754, 0.8657242683591371, 0.896946192100144, 0.46712272074396866, 0.8020948791842164, 0.8100683338581285, 0.0007158922441248922, 0.009013212829706763, 0.8271328775267539, 0.0, 0.0, 0.8592499374158497, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7878271643177946, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2555754809259863, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.6 | 18.0 | 720 | 2.7315 | 0.0625 | 0.1002 | 0.4936 | [0.3927965347080424, 0.48893971802838454, 0.8244443211596889, 0.2646584090228063, 0.19000919399325775, 0.49847715260336617, 0.31560089645557454, 0.0, 0.004567152938890036, 0.6920523173551437, 0.0, 0.0, 0.6115214548172136, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.28878374617311436, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.17525264974118807, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9816370743653746, 0.6292606472225292, 0.8524166460000371, 0.8554487518618299, 0.4226209314214767, 0.5359777110497571, 0.732801909149666, 0.0, 0.00461712264846014, 0.7451545778834721, 0.0, 0.0, 0.6643527445687106, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8034691032987455, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.18545810238017607, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.9919 | 18.5 | 740 | 2.6616 | 0.0645 | 0.1077 | 0.4850 | [0.348582801853762, 0.36093719140561387, 0.8384100066744122, 0.29010974680397583, 0.23891118355242252, 0.6061314171325576, 0.3742078103076474, 0.0025538329995724862, 0.003509433073462562, 0.6003887374132765, 0.0, 0.0, 0.6822571338363219, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.297688361099902, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.19198334515063287, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7910234069608628, 0.6279465102026464, 0.8582859687925458, 0.8691143794307481, 0.494218175621394, 0.7658148767658854, 0.8780817907116775, 0.002559173849076386, 0.0036593152905348984, 0.8264045778834721, 0.0, 0.0, 0.8479780245059235, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7997522069072325, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2074665797195957, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.3695 | 19.0 | 760 | 2.6910 | 0.0652 | 0.1054 | 0.4945 | [0.37359259981109727, 0.39944216617332673, 0.8552160106729121, 0.2874821788134592, 0.2498833048078419, 0.5896245271994833, 0.3309904856688811, 0.005279140914187198, 0.005788223236746483, 0.6372549019607843, 0.0, 0.0, 0.5928855550619876, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.29204425091208663, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.20219952158148913, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8801982762680732, 0.6247435523896597, 0.8846464656689325, 0.8517861357772478, 0.4691676607347177, 0.6762157151206143, 0.7973461935776117, 0.00528181915547263, 0.0061029520113954515, 0.8245763971462544, 0.0, 0.0, 0.794669860149796, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7686231996283104, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.21910661884577765, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.7818 | 19.5 | 780 | 2.6738 | 0.0644 | 0.1040 | 0.4972 | [0.4111776805637955, 0.4488437809472857, 0.8239248795354392, 0.25915936919156113, 0.2717226701592109, 0.5301523181825085, 0.297545218131567, 0.001747453509281233, 0.004752773431525946, 0.7063845756907633, 0.0, 0.0, 0.6445489416766799, 0.0, 0.0, 0.0, 0.029444001594260662, 0.0, 0.0, 0.0, 0.2966007812955513, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.10517505487737473, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9433228936090049, 0.6338621361759417, 0.8437771513117827, 0.8806394113768995, 0.48486987852082675, 0.7035327784607324, 0.7047391767896897, 0.001747453509281233, 0.004960950930792279, 0.7833382877526753, 0.0, 0.0, 0.75489340396885, 0.0, 0.0, 0.0, 0.06320517619378643, 0.0, 0.0, 0.0, 0.7878271643177946, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.10857515487447017, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.4978 | 20.0 | 800 | 2.6439 | 0.0634 | 0.1048 | 0.4872 | [0.383246946819098, 0.3989213903042229, 0.8049923521073749, 0.27641785070002234, 0.2638329676688285, 0.5537318314229865, 0.3228099084462035, 0.0016396940735166946, 0.003189387198539353, 0.6312339124839125, 0.0, 0.0, 0.669773780918341, 0.0, 0.0, 0.0, 0.008270546462284575, 0.0, 0.0, 0.0, 0.264578194375925, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.1736123776521369, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8800722931286206, 0.629758974410497, 0.8479239737964008, 0.8655046678007211, 0.5328042456849331, 0.7335906168759194, 0.6947680681460366, 0.0016459884668068388, 0.003389164497273933, 0.6998365041617123, 0.0, 0.0, 0.8279172671362169, 0.0, 0.0, 0.0, 0.020426715148922518, 0.0, 0.0, 0.0, 0.8305714728201952, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.18622432344310402, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.1816 | 20.5 | 820 | 2.6886 | 0.0666 | 0.1056 | 0.4985 | [0.4094993277827805, 0.42455573819921993, 0.8490434340156201, 0.26497737100210533, 0.26318487299236815, 0.5471743295019157, 0.3105103637074697, 0.0007046183505166262, 0.0019410969508602063, 0.6729380156279672, 0.0, 0.0, 0.6857834417194016, 0.0, 0.0, 0.0, 0.03823687390895663, 0.0, 0.0, 0.0, 0.2532841328413284, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2768615051558138, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9578910186973107, 0.6139531612630986, 0.8783216334903997, 0.8553754995401383, 0.4499841760595954, 0.6101499365188126, 0.7017671787562907, 0.0007046183505166262, 0.002062969693992829, 0.7110508323424495, 0.0, 0.0, 0.7955358894730076, 0.0, 0.0, 0.0, 0.12181166782524999, 0.0, 0.0, 0.0, 0.797274275979557, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.31690577111183565, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.2749 | 21.0 | 840 | 2.6472 | 0.0671 | 0.1087 | 0.5013 | [0.399438207195943, 0.40701474207949234, 0.8343087291378222, 0.28354123986666935, 0.26665851421583614, 0.6346904584724483, 0.32638796157142475, 0.006798589014148867, 0.003903033311148462, 0.6562253646038628, 0.0, 0.0, 0.6173291701693286, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3285910968334098, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2696600348243085, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9076355201808506, 0.6206785689168417, 0.8600540200390389, 0.8533203649593449, 0.5308566837889817, 0.8035509159428468, 0.7426293896355711, 0.006877075101042272, 0.004248735203104278, 0.7918400713436385, 0.0, 0.0, 0.8347169504942457, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7762118630943162, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.30802086729703293, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.4211 | 21.5 | 860 | 2.6342 | 0.0704 | 0.1095 | 0.5041 | [0.4122767751291967, 0.43810088011369697, 0.8293726668487663, 0.27005556072568493, 0.2948622277290455, 0.574774619333132, 0.3479932940585061, 0.015704162528824627, 0.08280600253118785, 0.6550518081099352, 0.0, 0.0, 0.6005291793548541, 0.0, 0.0, 0.0, 0.021727354799879625, 0.0, 0.0, 0.0, 0.4621916338934573, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2764132447445812, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9315075589883671, 0.6107381471471773, 0.875949610539475, 0.8738961282078412, 0.5098230153127054, 0.8056064972491486, 0.7762936201477161, 0.015777814104768292, 0.08998477331892529, 0.7056703329369798, 0.0, 0.0, 0.6818154139687823, 0.0, 0.0, 0.0, 0.0965189027324742, 0.0, 0.0, 0.0, 0.8008363016880904, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.33097489403325725, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.7149 | 22.0 | 880 | 2.5923 | 0.0637 | 0.1050 | 0.4899 | [0.3934766100865817, 0.39311831545807324, 0.8181437067207076, 0.2624896708338223, 0.3526563726773096, 0.5913125954637279, 0.34458974677852927, 0.018148545253478986, 0.005967351044858018, 0.6180788922949144, 0.0, 0.0, 0.6015585016937587, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2715797843263927, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.10363169764367369, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8864727075778269, 0.6342881255463012, 0.8329815788630148, 0.8829305812164769, 0.5248679309589308, 0.7255647810402853, 0.7959430569596129, 0.01822424901776202, 0.006409941549192003, 0.8406212841854934, 0.0, 0.0, 0.6680266033382725, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8463682824841258, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.1086240626018911, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.3311 | 22.5 | 900 | 2.6322 | 0.0725 | 0.1129 | 0.5043 | [0.40454798137042486, 0.4714705083486916, 0.7930497857766087, 0.2750721733742865, 0.3331850137937172, 0.4954613547971576, 0.36798268659511707, 0.042526680449237304, 0.09932261675340712, 0.7035167057567799, 0.0, 0.0, 0.6502186464519243, 0.0, 0.0, 0.0, 0.07809595519894585, 0.0, 0.0, 0.0, 0.4489795918367347, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2725485203417643, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9699453680591532, 0.6173590668421528, 0.8219076395672323, 0.8491856783571946, 0.4557294836526523, 0.7015175026702404, 0.7814918546262077, 0.042902802126256336, 0.10569035807259688, 0.7445377526753865, 0.0, 0.0, 0.7474780278888505, 0.0, 0.0, 0.0, 0.3549542805197583, 0.0, 0.0, 0.0, 0.848381601362862, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.31410172807303555, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.4737 | 23.0 | 920 | 2.6441 | 0.0654 | 0.1071 | 0.4825 | [0.3690590981434508, 0.3574844848221589, 0.8080182746251572, 0.2892839566587054, 0.29375052830295006, 0.5749495927842012, 0.34642273013724495, 0.00957301153528489, 0.02145617357606542, 0.6600360102604578, 0.0, 0.0, 0.6943245517226307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.006855022387147666, 0.2719792549743181, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.27039718254744666, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8589353836009984, 0.6217837300191896, 0.8193449775028976, 0.8468212561959255, 0.5076076636560606, 0.6220702928195724, 0.7804256917629252, 0.009735007130737708, 0.024067979763249668, 0.7954964328180737, 0.0, 0.0, 0.8598385667214701, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.016190826073265455, 0.8446647049713489, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3154222367134007, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.4955 | 23.5 | 940 | 2.6445 | 0.0659 | 0.1100 | 0.4934 | [0.3724898514114116, 0.36901530190289655, 0.8336472967747692, 0.31043051725099857, 0.2565769579679468, 0.562249121382751, 0.3882215782127869, 0.015202406041447137, 0.03669937417909295, 0.643436190772663, 0.0, 0.0, 0.681591507394645, 0.00451102012797568, 0.0, 0.0, 0.0, 0.0, 0.0, 0.011642030480119171, 0.2428161553740896, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.27795782650784967, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8406454575425046, 0.6159444606311475, 0.8511192877857688, 0.8378071510544265, 0.41312656717871316, 0.8164940247072812, 0.9050396910889776, 0.015614342647448437, 0.040829608526941404, 0.7373216409036861, 0.0, 0.0, 0.8618615570936597, 0.00451102012797568, 0.0, 0.0, 0.0, 0.0, 0.0, 0.026661068542038417, 0.8519436270713954, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.32298663188783827, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.7405 | 24.0 | 960 | 2.6627 | 0.0694 | 0.1078 | 0.4911 | [0.36195120764129707, 0.3961970716214391, 0.805587216857827, 0.2991287385269349, 0.2700001583857326, 0.5796402602372752, 0.3848879353391327, 0.03079425498484077, 0.12481204645993593, 0.7016250527288432, 0.0, 0.0, 0.677145087903508, 0.025717718012209175, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40232828870779974, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.21416419505879164, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8756110770969717, 0.6306631971305999, 0.8167806283667627, 0.8362240869912015, 0.41500109550356645, 0.6486215513593035, 0.8899200654060534, 0.03137524591180433, 0.1406626062183801, 0.7787381093935791, 0.0, 0.0, 0.850846746639062, 0.025717718012209175, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8028496205668267, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.23190414085425498, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.8688 | 24.5 | 980 | 2.6659 | 0.0712 | 0.1105 | 0.4753 | [0.3560729060501982, 0.31439464774944076, 0.8108693050347083, 0.2907996841560705, 0.3235617442286552, 0.5933484104712993, 0.3900194562759636, 0.05096993183967292, 0.030034407194772485, 0.6075469868838381, 0.0, 0.0, 0.6381684705612982, 0.13275638039667556, 0.0, 0.0, 0.0006078897693218297, 0.0, 0.0, 0.011138259701952497, 0.4827804966467283, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.30400032845666425, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7449630292469269, 0.6182070268152271, 0.8229637465140879, 0.8692771623678406, 0.4299242885312949, 0.7904516233046492, 0.9013992697060594, 0.052564528948540315, 0.037194852399430225, 0.6519842449464923, 0.0, 0.0, 0.9051697891083281, 0.13275638039667556, 0.0, 0.0, 0.003047965349446554, 0.0, 0.0, 0.026556103705258738, 0.8249961282329255, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.36212911640039125, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.0373 | 25.0 | 1000 | 2.6283 | 0.0678 | 0.1079 | 0.4902 | [0.38023038726290015, 0.38030267678469803, 0.8188453231433793, 0.289342474504659, 0.28640762548825277, 0.5380916976456009, 0.3395279619220977, 0.05663741011128428, 0.009796225246840552, 0.6057539075279939, 0.0, 0.0, 0.7014339902974911, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.006568504594820384, 0.4018807406327453, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.26927915820776027, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8865256911411482, 0.6190107803442074, 0.8314952686071366, 0.8589241675687554, 0.478394235216788, 0.6016404344934604, 0.7624997928440033, 0.05838185805040558, 0.010879709219509799, 0.8531064209274672, 0.0, 0.0, 0.8432622242068728, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.016505720583604493, 0.8538020752671519, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.30705901532442126, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.186 | 25.5 | 1020 | 2.6090 | 0.0725 | 0.1123 | 0.5041 | [0.41726551593078526, 0.40615779415188213, 0.7954163678693871, 0.2819509108531548, 0.25358569475349185, 0.5877929835173143, 0.33182745661303903, 0.11492303125397565, 0.15350890484993585, 0.6487471994145252, 0.0, 0.0, 0.6433030816794655, 0.04295275687072495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.000872547475065744, 0.4964585274930102, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2643632036309293, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9216467291480243, 0.6029839349763395, 0.8196233443499122, 0.8559167528059709, 0.4605496993451323, 0.7833528143326414, 0.7555227788734028, 0.12220900671360364, 0.1866005206542561, 0.7510107015457789, 0.0, 0.0, 0.8728493041319071, 0.04295275687072495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0018893670620342186, 0.8249961282329255, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3114607107923052, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.817 | 26.0 | 1040 | 2.6553 | 0.0747 | 0.1117 | 0.5038 | [0.4104302417168752, 0.4437987152159143, 0.8056719478650091, 0.2731990471114764, 0.2660969199049373, 0.5598205612463989, 0.33142081469900253, 0.08251349992348221, 0.23041600017410419, 0.6292041257944065, 0.0, 0.0, 0.6519043584285685, 0.11503101326337983, 0.0, 0.0, 0.005709378329808044, 0.0, 0.0, 0.0, 0.5181496025284934, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2771298902740277, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9448323364574012, 0.6116363417158128, 0.8271898613733102, 0.8657447726329326, 0.41159286218565133, 0.676518006489188, 0.7552962883169542, 0.08510098590199604, 0.26002013851367944, 0.6850029726516053, 0.0, 0.0, 0.8537154687722005, 0.11503101326337983, 0.0, 0.0, 0.01919683439388268, 0.0, 0.0, 0.0, 0.8378503949202416, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3145745027714379, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9985 | 26.5 | 1060 | 2.6413 | 0.0688 | 0.1069 | 0.4902 | [0.3795555713106494, 0.391700127443563, 0.8271534418088647, 0.28529311605656504, 0.20179444972526836, 0.5612798980451059, 0.32863589234341456, 0.08415906627396451, 0.1389918378714197, 0.6293022538217257, 0.0, 0.0, 0.6810285564636688, 0.001716148961729878, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.43596408317580343, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.21634419628719218, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8824671501907408, 0.6231460922508113, 0.8452735839984614, 0.8557458307220237, 0.42378946855904764, 0.6212893734507567, 0.7458941681443795, 0.08665678321993675, 0.15933984969792228, 0.7275044589774079, 0.0, 0.0, 0.8445342047753398, 0.001716148961729878, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8572092302927056, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.23387675252689924, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.0289 | 27.0 | 1080 | 2.6788 | 0.0706 | 0.1068 | 0.5000 | [0.4150924631142093, 0.5027812449570381, 0.8030670641573006, 0.25622610536319845, 0.24501451174983616, 0.5349160616472555, 0.2813759410849417, 0.05073850587652542, 0.16444237474870235, 0.6587735440571553, 0.0, 0.0, 0.6252138783269962, 0.0025006742013778224, 0.0, 0.0, 0.03287706835311444, 0.0, 0.0, 0.0, 0.4473169140433861, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.27242056713358387, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9779129185701503, 0.6135251625089166, 0.8203032342853479, 0.8689556660670829, 0.38225771112788176, 0.728668305757643, 0.6433270910326312, 0.050811438492454945, 0.17778378112873913, 0.7236325802615934, 0.0, 0.0, 0.7120114207616999, 0.0025006742013778224, 0.0, 0.0, 0.04058606491631463, 0.0, 0.0, 0.0, 0.84946569614372, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3119823932181285, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.1636 | 27.5 | 1100 | 2.5853 | 0.0737 | 0.1138 | 0.5171 | [0.41815160408803803, 0.47103226936231474, 0.8248333089308246, 0.3003613638976655, 0.19874094224704494, 0.5979543193889233, 0.3600564798270789, 0.08306149602132917, 0.2284969864389754, 0.6265296354746074, 0.0, 0.0, 0.6945357042054756, 0.0028193875799848, 0.0, 0.0, 0.0, 0.0, 0.0, 0.014485841365617756, 0.419771103771411, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2888686003230564, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9691046955211228, 0.6107542222177569, 0.8458708074156928, 0.8398948422226382, 0.4019524308006914, 0.8026037363213155, 0.8226523701408108, 0.09114379287602663, 0.26814922147453213, 0.7198944708680143, 0.0, 0.0, 0.8324503893749028, 0.0028193875799848, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03427101920856513, 0.8463682824841258, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3352787740462993, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.0571 | 28.0 | 1120 | 2.6102 | 0.0746 | 0.1160 | 0.5100 | [0.4178043004942549, 0.3988510111517976, 0.8273362454045934, 0.2806607090348892, 0.18802840716942848, 0.6341080593914212, 0.3648687927479495, 0.11774373703526408, 0.3119468596214818, 0.6375809907328532, 0.0, 0.0, 0.687407886302248, 0.019956360783544583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0154413799464994, 0.55311004784689, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2882300860470907, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8667004191588565, 0.616330262325058, 0.847414478112774, 0.8933934544980995, 0.32485332424471114, 0.8365661715805808, 0.88761096656226, 0.12478509140309244, 0.3938675769929761, 0.7342226516052318, 0.0, 0.0, 0.8488778830995731, 0.019956360783544583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04392778419229558, 0.8056372928604615, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.33966416693837626, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.2911 | 28.5 | 1140 | 2.6024 | 0.0757 | 0.1159 | 0.5108 | [0.3928936228210195, 0.42369315197102975, 0.8162792497443657, 0.3154200313584745, 0.20881631066073728, 0.6311554448579647, 0.39470024156357514, 0.08054281753225978, 0.25074890044382997, 0.6450666258232501, 0.0, 0.0, 0.6476719010784024, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.018363098498959132, 0.5538922155688623, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2995221843003413, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9062296896340601, 0.6093777942993781, 0.8363439129605917, 0.8497879752244369, 0.4183849842977822, 0.7749591906652425, 0.9233743778414898, 0.0851460814764291, 0.307333366078884, 0.7824836504161712, 0.0, 0.0, 0.8480456830467994, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04398026661068542, 0.8308812141861546, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3576785132050864, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9941 | 29.0 | 1160 | 2.5889 | 0.0719 | 0.1098 | 0.4973 | [0.405518083538319, 0.4237782858666217, 0.8108185465525086, 0.27182257424529876, 0.2441892159777976, 0.5943004619047296, 0.29988945249636806, 0.04226822541564779, 0.25171786984139666, 0.6273713700617196, 0.0, 0.0, 0.6825306937398198, 0.0005883939297359582, 0.0, 0.0, 0.006572633415733605, 0.0, 0.0, 0.0, 0.5213857170505128, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.20649543876523543, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8969540338152875, 0.6260617081771874, 0.8267984607156896, 0.8670266882625364, 0.47980621759135283, 0.8828872856250378, 0.6773559160990592, 0.042677324254091015, 0.3094822928434599, 0.6557000594530321, 0.0, 0.0, 0.7909960013802342, 0.0005883939297359582, 0.0, 0.0, 0.012084915245174055, 0.0, 0.0, 0.0, 0.8344432398946879, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.22399739158787088, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.5703 | 29.5 | 1180 | 2.6152 | 0.0731 | 0.1122 | 0.4976 | [0.3855282935419713, 0.3878360919911833, 0.8128902196987261, 0.2864065129854912, 0.24449783757808746, 0.6260697054714921, 0.3743335717207184, 0.07383711529132755, 0.21356884785226818, 0.6413237699760916, 0.0, 0.0, 0.6877993970950945, 0.0032361666135477703, 0.0, 0.0, 0.0, 0.0, 0.0, 0.008949232844561915, 0.49990877577084475, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.23775502688370037, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8446792728300287, 0.6219163493514713, 0.8340208150918695, 0.875320478907401, 0.37159480974754727, 0.8061052780072953, 0.864553123083807, 0.07722053426981809, 0.26130949457242497, 0.7575208085612366, 0.0, 0.0, 0.8490402635976753, 0.0032361666135477703, 0.0, 0.0, 0.0, 0.0, 0.0, 0.021412826703054475, 0.8486913427288214, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.2631235735246169, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.6401 | 30.0 | 1200 | 2.6029 | 0.0755 | 0.1155 | 0.5157 | [0.4256689529670895, 0.4503524436786995, 0.8222082190058929, 0.28470005781919133, 0.2392352626007867, 0.613592986639885, 0.3294096249681714, 0.08197729598464483, 0.26755286890858226, 0.6691627000606971, 0.0, 0.0, 0.6928587819798844, 0.06026134497045772, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004353816478368697, 0.497463768115942, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.30023731994039404, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.966147035275279, 0.6103161765444627, 0.838687255690915, 0.8476270317345336, 0.4456751953648027, 0.7726113943693195, 0.7503853101539584, 0.08715283453870046, 0.33588339309396337, 0.7783442330558858, 0.0, 0.0, 0.8375518433569462, 0.06026134497045772, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010365277631993281, 0.8505497909245779, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.35474404955983047, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.2722 | 30.5 | 1220 | 2.5906 | 0.0735 | 0.1138 | 0.5080 | [0.4154933764750469, 0.41513943356008887, 0.8297711339318535, 0.29094065580763956, 0.28879664193553384, 0.6119032885827795, 0.30060848806102747, 0.08628029165773107, 0.23396498136045865, 0.6557051463375001, 0.0, 0.0, 0.6558155687888786, 0.016205349481477848, 0.0, 0.0, 0.0, 0.0, 0.0, 0.013865152994791666, 0.4585912665216664, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3137421345513831, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9461710544906513, 0.613245858157596, 0.84543722996307, 0.8381815518097393, 0.4539036443751978, 0.6875264504947503, 0.6691691111074284, 0.09071538491891253, 0.3152045778279876, 0.8096759809750297, 0.0, 0.0, 0.8688033233875279, 0.016205349481477848, 0.0, 0.0, 0.0, 0.0, 0.0, 0.035766768132675555, 0.8490010840947808, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.37960547766547115, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.4541 | 31.0 | 1240 | 2.6063 | 0.0779 | 0.1178 | 0.5122 | [0.42904956595277477, 0.441438145516982, 0.8154988474594286, 0.2683541376309271, 0.24588484634404803, 0.5820222769569662, 0.32920167070102996, 0.08145933603824104, 0.2469954536469658, 0.674141445820919, 0.0, 0.0, 0.7057408866707527, 0.06827821226311015, 0.0, 0.0, 0.16051043705153295, 0.0, 0.0, 0.00586870888404551, 0.5429343474779824, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.32263409680091937, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9312591249470165, 0.6085639938512855, 0.8284382945053759, 0.8971985056526375, 0.3389244589429608, 0.7774127889401664, 0.7663059390243229, 0.08386085760508678, 0.3068790215629451, 0.8306629013079667, 0.0, 0.0, 0.8180188226060717, 0.06827821226311015, 0.0, 0.0, 0.21052350141703652, 0.0, 0.0, 0.015403589797417865, 0.8401734551649372, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3936093902836648, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.0807 | 31.5 | 1260 | 2.5775 | 0.0748 | 0.1164 | 0.5096 | [0.4014483223487889, 0.4053022318703309, 0.8290979849691739, 0.30966988285390823, 0.2828337005682477, 0.6363647995086875, 0.38660220115783445, 0.07743551814658944, 0.19894842801716134, 0.644111471654896, 0.0, 0.0, 0.6685886817772, 0.02365833925813332, 0.0, 0.0, 0.009710595551303516, 0.0, 0.0, 0.007365784036808959, 0.4542816878056868, 0.0, 0.0, 0.00026441036488630354, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.34940630093006725, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9179119766401357, 0.5950046718173871, 0.8453444410140651, 0.8393169627959597, 0.47498600189887286, 0.7517532899377279, 0.872828314634052, 0.08380448813704545, 0.2499754408369763, 0.7523409631391201, 0.0, 0.0, 0.8629711571640246, 0.02365833925813332, 0.0, 0.0, 0.010855034490134217, 0.0, 0.0, 0.01936601238585074, 0.8486913427288214, nan, 0.0, 0.0003296725939051779, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4648516465601565, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9001 | 32.0 | 1280 | 2.5916 | 0.0749 | 0.1156 | 0.5095 | [0.4033062660436654, 0.4058225824557038, 0.8219672174505254, 0.29608748658655115, 0.26586027781020194, 0.6269219525215763, 0.3878128017840328, 0.05835041267379383, 0.25786456762019694, 0.6551075908914316, 0.0, 0.0, 0.6869064985824778, 0.017382137340949767, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0045871559633027525, 0.4758381719423113, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3302218079197048, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9106885508406726, 0.6029437472998905, 0.8389032008813263, 0.8578904959182179, 0.4435815663266548, 0.7454908204187743, 0.8895830916513371, 0.05937959763473712, 0.3463578761235817, 0.7769470868014269, 0.0, 0.0, 0.8442432730495734, 0.017382137340949767, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01057520730555264, 0.7868979402199163, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4215846103684382, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.0586 | 32.5 | 1300 | 2.6179 | 0.0737 | 0.1164 | 0.4985 | [0.38539019441600836, 0.36041729164498376, 0.8166693775123731, 0.30107285846837467, 0.224163696976664, 0.6128046415662184, 0.3944979848468148, 0.06958664845026655, 0.17465431601557255, 0.6454446190950432, 0.0, 0.0, 0.7009741838605041, 0.0012748535144279094, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019551415178306532, 0.5164619164619164, 0.0, 0.0, 0.00010801302328452173, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.37735369572583377, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8359852587952715, 0.5914641375222288, 0.8301371758080652, 0.8636041770101658, 0.42724639092436156, 0.7960742427601217, 0.9094921639791629, 0.07321266509207952, 0.22366029765705583, 0.7995243757431629, 0.0, 0.0, 0.8875041440856286, 0.0012748535144279094, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05187887057835625, 0.8138454390583862, nan, 0.0, 0.00014423175983351534, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5083632213889795, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.641 | 33.0 | 1320 | 2.5954 | 0.0751 | 0.1144 | 0.5042 | [0.41387346401730557, 0.40975107249598447, 0.809839964821576, 0.2712893666309553, 0.24932165852656898, 0.6015142354952495, 0.338925749162722, 0.08025282196844676, 0.28651749747036026, 0.6681910601697144, 0.0, 0.0, 0.7029714855026798, 0.013753708107578023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.003975962122131844, 0.5296080862019643, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3295311342938433, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9177424292375077, 0.6074367295268905, 0.8202576833467455, 0.8635716204227473, 0.4138325583659956, 0.7569174341508635, 0.7681178634759119, 0.08223741692549648, 0.3685716390785402, 0.7742048156956005, 0.0, 0.0, 0.7924641917172415, 0.013753708107578023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010312795213603444, 0.8601517732693201, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.41351483534398437, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.381 | 33.5 | 1340 | 2.6200 | 0.0728 | 0.1146 | 0.4893 | [0.3746821112229337, 0.3444207345389141, 0.8322420810476022, 0.30737891508501747, 0.30937233960219795, 0.596650965481038, 0.36969748865429547, 0.08351192002050756, 0.22701985080288278, 0.5797890978810618, 0.0, 0.0, 0.6885186506738397, 0.0029910024761577877, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019972119918769148, 0.4846335697399527, 0.0, 0.0, 0.00044451029782189953, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.31504199676744127, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.7894656901992182, 0.6110274984176103, 0.8489699583124558, 0.8544842629595566, 0.4865739951797843, 0.8474990427439996, 0.8347668528308557, 0.0918202264925226, 0.308671840463677, 0.6517241379310345, 0.0, 0.0, 0.8486004830819819, 0.0029910024761577877, 0.0, 0.0, 0.0, 0.0, 0.0, 0.060905846541408626, 0.8572092302927056, nan, 0.0, 0.0006181361135722086, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.38767525268992503, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.7518 | 34.0 | 1360 | 2.6741 | 0.0757 | 0.1183 | 0.5010 | [0.3895956288817351, 0.40329104840022884, 0.8109052494718024, 0.29256538389363435, 0.28066967505997215, 0.5973251685561506, 0.379824311192547, 0.06784272188140159, 0.2574730415477323, 0.6239575236162896, 0.0, 0.0, 0.6709411764705883, 0.039005614258746234, 0.0, 0.0, 0.055849037435871385, 0.0, 0.0, 0.014148683726785575, 0.46321097883597884, 0.0, 0.0, 0.0, 0.0, 0.0016713378416600242, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4082701843016185, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9028387415815005, 0.5623481659349161, 0.8249291851611913, 0.8565353279669226, 0.407308226014558, 0.7637139517542976, 0.8680001988697569, 0.07112699477455031, 0.318998968515153, 0.7039090368608799, 0.0, 0.0, 0.7717133172306007, 0.039005614258746234, 0.0, 0.0, 0.11758729479706967, 0.0, 0.0, 0.03332633567754802, 0.867740436735326, nan, 0.0, 0.0, 0.0, 0.0016716673810544364, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.6402999673948484, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.1024 | 34.5 | 1380 | 2.6454 | 0.0750 | 0.1148 | 0.5022 | [0.40745155251592413, 0.41295731243727635, 0.8083063687489488, 0.2947414466755003, 0.29634855003631894, 0.623697132631686, 0.34175455889600787, 0.05560853336239544, 0.25645633912873605, 0.5584291632145816, 0.0, 0.0, 0.6887554383730411, 0.007575571845350462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.018705807772955576, 0.5417495029821073, 0.0, 0.0, 1.5805778592653473e-05, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3850289141143714, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9522983092356238, 0.5729938814262606, 0.8188557266808718, 0.8472973962869212, 0.5164690702826399, 0.7410370609217871, 0.7661125934273545, 0.05752504213617736, 0.32241269217545065, 0.6261370392390012, 0.0, 0.0, 0.7829716984323516, 0.007575571845350462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05033063923585599, 0.8440452222394301, nan, 0.0, 2.060453711907362e-05, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5861428105640691, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.2307 | 35.0 | 1400 | 2.6095 | 0.0786 | 0.1193 | 0.4994 | [0.38375355364954733, 0.35855001370668654, 0.8137648623382095, 0.3019221779962321, 0.25447279148659324, 0.6142626160120263, 0.3893349961206917, 0.09680361489109984, 0.2377400156195298, 0.6712919570207968, 0.0, 0.0, 0.6902862377916773, 0.2212851504081983, 0.0, 0.0, 0.0, 0.0, 0.0, 0.017759089231595075, 0.571353065539112, 0.0, 0.0, 0.00025616683995599956, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3533401445118724, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8235953468657279, 0.5965900756533009, 0.8341287876870752, 0.8674214368849857, 0.3999318353336417, 0.8172950968340017, 0.903697320230026, 0.10470064993996651, 0.3252124367601552, 0.734519916765755, 0.0, 0.0, 0.8564488738235871, 0.2212851504081983, 0.0, 0.0, 0.0, 0.0, 0.0, 0.041671040201532485, 0.8370760415053431, nan, 0.0, 0.00035027713102425153, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4679654385392892, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.6428 | 35.5 | 1420 | 2.5788 | 0.0776 | 0.1196 | 0.5067 | [0.40073019859304687, 0.40033843108457584, 0.8042227593656146, 0.31223410477580144, 0.2788005230448261, 0.6099705835445057, 0.37320155811748756, 0.09004413425023093, 0.3179416908569677, 0.6334034179740148, 0.0, 0.0, 0.6569863327280957, 0.1607296084728726, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02069147590394579, 0.4638329604772558, 0.0, 0.0, 0.002019040675256937, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.37272210861167915, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9269804078556964, 0.5795002662433565, 0.824011418101943, 0.8255373871710767, 0.46196168171969715, 0.6936831180347031, 0.839407147158096, 0.09890586862531778, 0.4330762807603517, 0.6985136741973841, 0.0, 0.0, 0.8543920541809595, 0.1607296084728726, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04935971449564396, 0.8669660833204275, nan, 0.0, 0.0029670533451466013, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5348386044995109, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.8997 | 36.0 | 1440 | 2.6139 | 0.0779 | 0.1200 | 0.5036 | [0.40721164277433086, 0.38011079957125266, 0.8220592144613544, 0.28545731009102, 0.23502155172413794, 0.63805045202683, 0.35438385635133346, 0.05822163045180925, 0.34573054105747725, 0.6246010540834072, 0.0, 0.0, 0.6969385972574024, 0.10850964720880629, 0.0, 0.0, 0.0, 0.0, 0.0, 0.015520588535812813, 0.5529699938763013, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.39221146822498176, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.875070644751095, 0.5750494810766279, 0.8347310723197069, 0.8724799166551362, 0.4247632495070234, 0.7936508736220552, 0.8144213718698728, 0.05866370539061223, 0.5238346677145242, 0.6966557669441141, 0.0, 0.0, 0.8637830596545355, 0.10850964720880629, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03986039676708303, 0.8390893603840793, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5602217150309748, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.6652 | 36.5 | 1460 | 2.6338 | 0.0741 | 0.1145 | 0.4899 | [0.37124193384350673, 0.35303396999324915, 0.8109747249063005, 0.3156822336506321, 0.2461985588230888, 0.5703206529010391, 0.3797450313550492, 0.052267823188839276, 0.22556253110620203, 0.6796259615827537, 0.0, 0.0, 0.7205890628341193, 0.13824805707421117, 0.0, 0.0, 0.0, 0.0, 0.0, 0.022331699949066513, 0.41954152574220216, 0.0, 0.0, 0.0004645760743321719, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.32492641697945074, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8423833184194415, 0.6178734691007003, 0.8202509350595452, 0.827852974451218, 0.4000779024758381, 0.6442987847886983, 0.8536926246940997, 0.05516316142524563, 0.317218429195933, 0.7675237812128418, 0.0, 0.0, 0.8663879134782579, 0.13824805707421117, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0667313949826808, 0.864488152392752, nan, 0.0, 0.0007005542620485031, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.39233778937072056, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.8766 | 37.0 | 1480 | 2.5985 | 0.0753 | 0.1197 | 0.4983 | [0.3826107350953541, 0.3573884141227676, 0.8239454407678707, 0.30122917021924905, 0.1857993560703194, 0.6016373677517709, 0.4001846426317227, 0.06419853837421596, 0.3597742580298153, 0.6493853256939423, 0.0, 0.0, 0.6951324998034127, 0.0018142146166858712, 0.0, 0.0, 0.0, 0.00024896265560165974, 0.0, 0.0227712487524696, 0.5269826372595026, 0.0, 0.0, 0.001105460977227504, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.34833256017731046, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.785596712664249, 0.6045351792872715, 0.8364906882071994, 0.8770419084671545, 0.31750127808749423, 0.8271346808810786, 0.9386652524817288, 0.06917661118032029, 0.5503217250356108, 0.7808115338882283, 0.0, 0.0, 0.8971522520145331, 0.0018142146166858712, 0.0, 0.0, 0.0, 0.00024896265560165974, 0.0, 0.05867534375984045, 0.8695988849310825, nan, 0.0, 0.0018544083407166258, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.44069122921421583, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.6223 | 37.5 | 1500 | 2.5848 | 0.0775 | 0.1183 | 0.5115 | [0.42007005580490925, 0.42021254954228193, 0.8133823264216902, 0.30698202829260646, 0.2159593023255814, 0.6052680503603007, 0.38607505083195975, 0.04884219644630369, 0.3115001535693914, 0.5582960897768189, 0.0, 0.0, 0.7065824561403509, 0.17681237588565546, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01810354570580197, 0.5441305835206725, 0.0, 0.0, 0.0011090719444407768, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.35675920451829585, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9283933028775962, 0.6018446143490098, 0.8294319797956281, 0.8445260167829208, 0.3617109331255934, 0.8201265593196428, 0.886334885622269, 0.052124847097817936, 0.4358883049265681, 0.5922934007134364, 0.0, 0.0, 0.8515503954641714, 0.17681237588565546, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04623701060144852, 0.8621650921480564, nan, 0.0, 0.001730781118002184, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.46237365503749595, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.3247 | 38.0 | 1520 | 2.6079 | 0.0783 | 0.1183 | 0.5102 | [0.42379617160547683, 0.4148421424205116, 0.8246053939127822, 0.29545808460939527, 0.2431227339350459, 0.6209879165998257, 0.372445175969405, 0.057092723641709565, 0.3075566408899742, 0.5460970674570378, 0.0, 0.0, 0.6999648441327163, 0.1997989752641514, 0.0, 0.0, 0.0, 0.0, 0.0, 0.013787729570026299, 0.524743052912067, 0.0, 0.0, 0.0004975254654481657, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.402349222716625, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.947083549192295, 0.5809148724543619, 0.8427615340881293, 0.8570684420859006, 0.3836210044550478, 0.7216148404909212, 0.8500135341917878, 0.06107631862278116, 0.4454049805982612, 0.5696195005945304, 0.0, 0.0, 0.821733276500159, 0.1998087719728358, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0334313005143277, 0.8539569459501316, nan, 0.0, 0.0007829724105247976, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5852298663188784, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.6401 | 38.5 | 1540 | 2.5587 | 0.0797 | 0.1233 | 0.5179 | [0.4345586096121971, 0.4015117238442499, 0.8228426987118607, 0.30054787889767504, 0.2414159946961871, 0.6351802579312736, 0.39048461453410954, 0.05136730424387328, 0.3364503489080038, 0.6355738269304765, 0.0, 0.0, 0.7151682477026017, 0.15575277648385594, 0.0, 0.0, 0.0, 0.011697833429442294, 0.0, 0.018682701165935275, 0.5170433493886625, 0.0, 0.0, 0.001672595643762092, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3854040552774233, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9180426694296614, 0.5759577225643756, 0.8462976365811152, 0.8641739172899897, 0.38118655208510843, 0.8491364543237742, 0.8974439712080785, 0.05425561298978022, 0.5292990814873029, 0.6997027348394768, 0.0, 0.0, 0.8482621903776023, 0.15575277648385594, 0.0, 0.0, 0.0, 0.011784232365145229, 0.0, 0.049490920541618556, 0.864488152392752, nan, 0.0, 0.002761007973955865, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5760515161395501, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.7518 | 39.0 | 1560 | 2.6691 | 0.0761 | 0.1172 | 0.4946 | [0.38781739686600575, 0.3686425740965286, 0.8127632764473347, 0.30712932667470294, 0.2776715163677411, 0.5972466881082535, 0.37751164955784217, 0.04255795960835579, 0.3350158550888319, 0.5284892874255596, 0.0, 0.0, 0.7233545710475626, 0.09934050847042095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019082913331356855, 0.5199186616138275, 0.0, 0.0, 0.001341198385594536, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3857985796224104, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8567124287665426, 0.5925733173922216, 0.8248448315711868, 0.8419377680831496, 0.431165859239964, 0.7943058382539651, 0.8534495616579109, 0.04496028770976488, 0.47352522226042537, 0.5737737812128418, 0.0, 0.0, 0.8257589596822755, 0.09934050847042095, 0.0, 0.0, 0.0, 0.0, 0.0, 0.050671774955389944, 0.8711475917608796, nan, 0.0, 0.002225290008859951, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5340234757091621, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.8133 | 39.5 | 1580 | 2.6610 | 0.0763 | 0.1163 | 0.4976 | [0.3900028981181697, 0.36600124803858014, 0.8241278612850654, 0.3017464363531737, 0.27013057671381935, 0.6161330480921474, 0.3679345362782208, 0.054935067664117, 0.3353671347771643, 0.6129737609329446, 0.0, 0.0, 0.7303704752131913, 0.04817475299713158, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019402167120153063, 0.5244293346284604, 0.0, 0.0, 0.0006490257852535933, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3398202063540709, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8492688268261668, 0.6069524680256799, 0.840342273126802, 0.8493159047068686, 0.41095990456946707, 0.7704953547893029, 0.8296072874717577, 0.058945552730818876, 0.49001670023085614, 0.6875, 0.0, 0.0, 0.8379239653317636, 0.04817475299713158, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05295476015534796, 0.8361468174074648, nan, 0.0, 0.0009890177817155337, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4338441473752853, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.4764 | 40.0 | 1600 | 2.5792 | 0.0798 | 0.1215 | 0.5138 | [0.42741288836109337, 0.42032758947169097, 0.809282435689991, 0.31037035271829305, 0.27938028258353026, 0.6153930924140634, 0.38446136820339577, 0.04806691353548955, 0.3485307105924319, 0.5653754650514292, 0.0, 0.0, 0.6684392873138362, 0.2394038192827201, 0.0, 0.0, 0.0, 0.0, 0.0, 0.024357181280258204, 0.5392675283979632, 0.0, 0.0, 0.002809967255753466, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3818990893202097, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9420359817265577, 0.5887635256648549, 0.8269182428134959, 0.8480461977975469, 0.42890181853592035, 0.7596330182785515, 0.8751097926782785, 0.05231650328915846, 0.49911587013114594, 0.5951694411414982, 0.0, 0.0, 0.8785123240032205, 0.239427296575057, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05941009761729821, 0.8528728511692737, nan, 0.0, 0.004986297982815816, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5414574502771438, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.3027 | 40.5 | 1620 | 2.6219 | 0.0776 | 0.1182 | 0.5002 | [0.39459372621237376, 0.3776909327370923, 0.8171110245216852, 0.3049580716865396, 0.2691372113276675, 0.6282631280499666, 0.38327130984420377, 0.046733509234828496, 0.3470448202265638, 0.5649395100246173, 0.0, 0.0, 0.721629934552855, 0.04199661673490402, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01760503667274346, 0.5979718678442918, 0.0, 0.0, 0.0008154389779831476, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3870319353961479, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.861811802383083, 0.5923000411923683, 0.8349284597203173, 0.8614879988279629, 0.43635124278793486, 0.7647417424074484, 0.875595918750656, 0.04992080089740193, 0.5191438675769929, 0.6378492865636147, 0.0, 0.0, 0.8444868437967267, 0.04199661673490402, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04358664847276163, 0.8493108254607403, nan, 0.0, 0.0014217130612160797, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5336485164656015, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.6424 | 41.0 | 1640 | 2.6308 | 0.0767 | 0.1163 | 0.4997 | [0.3937274032055961, 0.37883212493443397, 0.8171427621572652, 0.3038863704167243, 0.26620020672656436, 0.6357870577928407, 0.37098505914952834, 0.050221550110510674, 0.3289169849422714, 0.6040964358047868, 0.0, 0.0, 0.7107471704013089, 0.08387065140111305, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01864501217549218, 0.5371445439338778, 0.0, 0.0, 0.0006694418072494462, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.32896822753613536, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8649755098196203, 0.6138768046778456, 0.8293459391338236, 0.8497839056510097, 0.4075273267278526, 0.7787882146671772, 0.8379487689409633, 0.05353972074565532, 0.4600176825973771, 0.6672042211652794, 0.0, 0.0, 0.8612052692471635, 0.08387065140111305, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0474178650152199, 0.8454390583862474, nan, 0.0, 0.0011332495415490491, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.40257580697750245, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.6814 | 41.5 | 1660 | 2.5670 | 0.0764 | 0.1187 | 0.5075 | [0.41344155777651165, 0.40303819901743465, 0.8154165061363734, 0.3066852996474387, 0.2847141873782569, 0.5940730820738473, 0.3748394243593554, 0.045819753099462776, 0.3475975852483016, 0.583594102203718, 0.0, 0.0, 0.6941755751327439, 0.041015960185344084, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019902675408233903, 0.525530694205393, 0.0, 0.0, 0.0018938931111165415, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3542243302891005, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9030742240851505, 0.5995438698723037, 0.8355273702093488, 0.8404035389010523, 0.4732575407162159, 0.7822343362689184, 0.843030996061274, 0.04889487657904972, 0.5189719534358269, 0.6541840071343639, 0.0, 0.0, 0.8641957767538785, 0.041015960185344084, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05130156397606802, 0.8511692736564969, nan, 0.0, 0.0031937032534564112, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4736061297685034, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.8641 | 42.0 | 1680 | 2.5567 | 0.0762 | 0.1209 | 0.5092 | [0.41091894607049795, 0.3963368513987473, 0.8154669667095538, 0.3022817837429232, 0.27008625872023934, 0.6079310788622865, 0.3856475936143892, 0.04025284368933597, 0.35224659957047205, 0.651526151320981, 0.0, 0.0, 0.6909684148591907, 0.00733040770796048, 0.0, 0.0, 0.0, 0.0, 0.0, 0.017655759890058013, 0.46391239422598307, 0.0, 0.0, 0.0013879979005073774, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.38084381189258903, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8938527292422173, 0.5917374137220821, 0.8265555223764768, 0.8511105865883138, 0.4703118533485892, 0.7853479373652285, 0.8679504814305364, 0.04242929859470916, 0.5135812171521195, 0.766981272294887, 0.0, 0.0, 0.8740130310349727, 0.00733040770796048, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04416395507504986, 0.8660368592225491, nan, 0.0, 0.002451939917169761, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5391913922399739, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9104 | 42.5 | 1700 | 2.6042 | 0.0768 | 0.1190 | 0.5067 | [0.4110623284434588, 0.3959656880486447, 0.8153054621729924, 0.3013723020988741, 0.283365900561397, 0.6113520243871023, 0.3823173714038208, 0.03630681756529476, 0.349464494724151, 0.614193836886615, 0.0, 0.0, 0.6762733417404931, 0.042707592733334965, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01838433629161069, 0.5319396051103368, 0.0, 0.0, 0.0010563893341104472, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3629114893115835, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.894134130834079, 0.5957622095184512, 0.8268979979518948, 0.8541302100713803, 0.4448231370353239, 0.7891265794724008, 0.8679339089507964, 0.0377224480132581, 0.487622181836043, 0.6940992865636147, 0.0, 0.0, 0.8671659866983309, 0.042707592733334965, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04560722158077044, 0.8511692736564969, nan, 0.0, 0.001792594729359405, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5023312683403978, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.1043 | 43.0 | 1720 | 2.5750 | 0.0759 | 0.1202 | 0.4995 | [0.389146547637641, 0.3722663391798766, 0.8191311735684669, 0.30820235756385067, 0.273470347421996, 0.5815910276656314, 0.38229545370518714, 0.04448802042315682, 0.3464086428044342, 0.6034708093614842, 0.0, 0.0, 0.7025626259717823, 0.1184387947731006, 0.0, 0.0, 0.0, 0.0, 0.0, 0.018451673409742745, 0.4375724777734828, 0.0, 0.0, 0.001216671880974728, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.368607025394613, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.836081806621768, 0.5957039373876001, 0.8425590854721187, 0.84781423211219, 0.48136426710811403, 0.8368231192438685, 0.85681377504516, 0.04744618125038754, 0.5130409155655975, 0.6657104637336504, 0.0, 0.0, 0.8419428826597926, 0.1184387947731006, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04440012595780413, 0.8765680656651695, nan, 0.0, 0.00216347639750273, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.49149005542875773, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9556 | 43.5 | 1740 | 2.5637 | 0.0751 | 0.1193 | 0.5005 | [0.401462828035228, 0.38076576313621713, 0.8131516731827134, 0.30277347762554435, 0.2636489723868365, 0.593182158359527, 0.3800452239809326, 0.03709887250650477, 0.3525748780669576, 0.5672447243135151, 0.0, 0.0, 0.6853654521218687, 0.03113584544852779, 0.0, 0.0, 0.0, 0.005143696930111038, 0.0, 0.02214859620859803, 0.4925634295713036, 0.0, 0.0, 0.0014760839259146449, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.38129014370870107, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8596618471247587, 0.5897923301819497, 0.8344139028212901, 0.8516559094275739, 0.45813959149889233, 0.7986638721509038, 0.8588245692536307, 0.03857926392748632, 0.5228400216120634, 0.6366453626634958, 0.0, 0.0, 0.8731672992740238, 0.03113584544852779, 0.0, 0.0, 0.0, 0.005228215767634855, 0.0, 0.05736328330009447, 0.8719219451757783, nan, 0.0, 0.0025961716770032763, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5380828170850994, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.7157 | 44.0 | 1760 | 2.5829 | 0.0773 | 0.1201 | 0.5016 | [0.3954828937607999, 0.3831458874960456, 0.8156650013794295, 0.30762253956001545, 0.28216888665493844, 0.6060465506831458, 0.38523759162685534, 0.04518031132739828, 0.3350534554702353, 0.6125576975291882, 0.0, 0.0, 0.7033203114268602, 0.11875750815170757, 0.0, 0.0, 0.0, 0.0, 0.0, 0.023196655511421096, 0.477705389626872, 0.0, 0.0, 0.002407494038314526, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.37819084162396893, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8687809070786041, 0.591375724634041, 0.8279878463347522, 0.8433499100624272, 0.4373250237359106, 0.7636283025332017, 0.8753252349149003, 0.04846083167513148, 0.4999017633479051, 0.6706450653983353, 0.0, 0.0, 0.8660631524820536, 0.11875750815170757, 0.0, 0.0, 0.0, 0.0, 0.0, 0.058386690458696336, 0.8743998761034536, nan, 0.0, 0.004347557332124534, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5366807955656994, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.2576 | 44.5 | 1780 | 2.5928 | 0.0764 | 0.1183 | 0.5037 | [0.3991390784290964, 0.3960550709339303, 0.8109814157583183, 0.308142222974341, 0.25757529476449387, 0.583558802802759, 0.37852804875980806, 0.03330342277670868, 0.3237404554131443, 0.6372990733271686, 0.0, 0.0, 0.7190120322012868, 0.05871681090490083, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02362506170725073, 0.5137758875739645, 0.0, 0.0, 0.0009350433467625567, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3583552876923262, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8866022229548345, 0.6001185536455246, 0.821900891280032, 0.829944735192857, 0.4105703921902768, 0.8052084802805264, 0.8631831314252885, 0.03472922926026347, 0.4664890220541284, 0.7221759809750298, 0.0, 0.0, 0.8429848241892816, 0.05871681090490083, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0615356355620867, 0.8606163853182592, nan, 0.0, 0.0016689675066449633, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.4856374307140528, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9678 | 45.0 | 1800 | 2.5630 | 0.0761 | 0.1217 | 0.5065 | [0.4063738735272585, 0.3927450206953195, 0.8177303803100906, 0.3117023819971734, 0.27786123298877075, 0.5581791949789238, 0.38286040280564543, 0.0489933062081638, 0.3471346424020649, 0.6150602961779686, 0.0, 0.0, 0.6797332984703883, 0.04513471769349579, 0.0, 0.0, 0.0, 0.0, 0.0, 0.024697497099287254, 0.4805424528301887, 0.0, 0.0, 0.0019890098776253243, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3912250674005213, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8705258324306504, 0.5841982056202466, 0.8339955090148682, 0.8383158477328406, 0.4548043917520754, 0.8459472803853207, 0.867508548637466, 0.05260398757616924, 0.5218699346726263, 0.6981866825208085, 0.0, 0.0, 0.8794392460132204, 0.04513471769349579, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05864910255064553, 0.8835372463992566, nan, 0.0, 0.0036470030700760307, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5701336811216172, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.5933 | 45.5 | 1820 | 2.5953 | 0.0766 | 0.1194 | 0.5023 | [0.4024299378069735, 0.38060857733839826, 0.8176591219933907, 0.3031658983154537, 0.2737911639299646, 0.5897270290936356, 0.36824805415211753, 0.03844149984692151, 0.34395073014651045, 0.6040173301767968, 0.0, 0.0, 0.7100138652655451, 0.03998627080830616, 0.0, 0.0, 0.0, 0.0, 0.0, 0.017068428378885968, 0.5385662873261355, 0.0, 0.0, 0.0009224169546904347, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3917642614280317, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8724862242735365, 0.5834527242221172, 0.8361161582675797, 0.8470165957204366, 0.4408306351486233, 0.8004473912254892, 0.8342586301188247, 0.040343628277179945, 0.5266835306252763, 0.6807000594530321, 0.0, 0.0, 0.855772288414828, 0.03998627080830616, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04054266820615094, 0.857518971658665, nan, 0.0, 0.0017101765808831104, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5747962178024127, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.0615 | 46.0 | 1840 | 2.5807 | 0.0764 | 0.1202 | 0.5058 | [0.4049097714833398, 0.3851121769714488, 0.8210027264013985, 0.31079441671765795, 0.25706572804963534, 0.5707867422499991, 0.37891174338087524, 0.04989750724883966, 0.35209521061292615, 0.6295192045940655, 0.0, 0.0, 0.6986685276872606, 0.021868641055186447, 0.0, 0.0, 0.0, 0.01129989431753516, 0.0, 0.021082769390942217, 0.5271713448634344, 0.0, 0.0, 0.001329070728115234, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.36740390050876204, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8679896858663402, 0.5964675032401314, 0.8367235041156117, 0.833009123983624, 0.4196022104827519, 0.8314574474516838, 0.8603050441104169, 0.05296475217163376, 0.5543739869345252, 0.7218043995243757, 0.0, 0.0, 0.8602309862585503, 0.021868641055186447, 0.0, 0.0, 0.0, 0.011535269709543569, 0.0, 0.0531384486197124, 0.8638686696608332, nan, 0.0, 0.002287103620217172, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5085914574502771, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.773 | 46.5 | 1860 | 2.5638 | 0.0774 | 0.1211 | 0.5087 | [0.4095285229786404, 0.38698025908326783, 0.8233361447566955, 0.31179575862382813, 0.23937494868777537, 0.6017799389177791, 0.3842821812245215, 0.04444645653354922, 0.35988251827756634, 0.650270913691386, 0.0, 0.0, 0.6738853436661201, 0.05810390056142588, 0.0, 0.0, 0.0, 0.0037495924356048256, 0.0, 0.02099472075576549, 0.5480902434197507, 0.0, 0.0, 0.0018154043702539352, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3672046739628763, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8797755851740215, 0.5976228989380407, 0.8349706365153194, 0.8364682613968404, 0.4258830975971955, 0.8080752100925012, 0.8738060909387205, 0.047040321080489965, 0.5597278844736971, 0.7429548156956005, 0.0, 0.0, 0.8730996407331479, 0.05810390056142588, 0.0, 0.0, 0.0, 0.003817427385892116, 0.0, 0.049569644169203314, 0.8578287130246245, nan, 0.0, 0.0033791440875280735, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5092435604825563, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.2391 | 47.0 | 1880 | 2.5872 | 0.0779 | 0.1204 | 0.5045 | [0.40144136255027096, 0.37591353655481474, 0.8276003696478078, 0.30845734185941476, 0.2556016474121653, 0.5982214327643646, 0.39130749483680444, 0.03699747732274167, 0.3605459736399546, 0.6368301396557324, 0.0, 0.0, 0.728183955435626, 0.04104047659908309, 0.0, 0.0, 0.0, 8.216926869350862e-05, 0.0, 0.022230281407133685, 0.5617772367620207, 0.0, 0.0, 0.001677144424285895, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3735823882588392, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8571362972731126, 0.5939859142194046, 0.8430635199403451, 0.8449004175382336, 0.41850670691627917, 0.7906984945889846, 0.8959413997116389, 0.03885547432088883, 0.5660150302077706, 0.7285969084423306, 0.0, 0.0, 0.8508264490767992, 0.04104047659908309, 0.0, 0.0, 0.0, 8.298755186721991e-05, 0.0, 0.05951506245407789, 0.8576738423416447, nan, 0.0, 0.0032761214019327058, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5203782197587219, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 2.2394 | 47.5 | 1900 | 2.6078 | 0.0777 | 0.1201 | 0.5077 | [0.4051611432957912, 0.38443083132421607, 0.8315133045046957, 0.31542374927601124, 0.2582469509908342, 0.6055015853478027, 0.3978268539787006, 0.04110397168322199, 0.3560044507715216, 0.6158537769749237, 0.0, 0.0, 0.6942619289777161, 0.014685331829659958, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02455073854849891, 0.5795550847457627, 0.0, 0.0, 0.002556011360050489, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3756997910726859, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8749929355248905, 0.5917816201661761, 0.8460479499547021, 0.8399640249709025, 0.41909097548506463, 0.7831764777009734, 0.9156129331631893, 0.04359614658316469, 0.5343214303256545, 0.7068816884661118, 0.0, 0.0, 0.8728898992564327, 0.014685331829659958, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06040726356670515, 0.847297506582004, nan, 0.0, 0.00500690251993489, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5306162373655038, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9883 | 48.0 | 1920 | 2.6043 | 0.0782 | 0.1209 | 0.5098 | [0.41530158882416546, 0.3977827330301219, 0.8260393256564773, 0.30170868682040863, 0.27302893349949703, 0.5983777651730006, 0.37243654375128477, 0.04651312507041347, 0.34893915341783577, 0.6353505117106913, 0.0, 0.0, 0.7126514745613935, 0.12768148275270294, 0.0, 0.0, 0.0, 0.0, 0.0, 0.018355962754780977, 0.502227766886473, 0.0, 0.0, 0.000950504230902979, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.36908779768978467, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8971906937314558, 0.5964634844724865, 0.8408517688104288, 0.8528808510291951, 0.44268081894977723, 0.7972481409080833, 0.8407108488976539, 0.04887232879183319, 0.5317427182081634, 0.7003492865636147, 0.0, 0.0, 0.8459279707173835, 0.12768148275270294, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04516112102445681, 0.8728511692736565, nan, 0.0, 0.0016895720437640367, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5063253994131073, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.9271 | 48.5 | 1940 | 2.5714 | 0.0791 | 0.1220 | 0.5107 | [0.4177729591568349, 0.3955885991523053, 0.8265613361623487, 0.30852322777545443, 0.26822884012539183, 0.5882885915654367, 0.38756335914027357, 0.04714201220929005, 0.35697905395584695, 0.611063540604825, 0.0, 0.0, 0.7140724067615302, 0.13650739169874232, 0.0, 0.0, 0.0, 0.0, 0.0, 0.022710495017020056, 0.5491868306227687, 0.0, 0.0, 0.002268381995528047, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3822195690357836, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.88833419676918, 0.591130579807702, 0.8405868985378149, 0.8466503341119784, 0.41660783406772645, 0.8276183470707966, 0.8857438005115372, 0.050321024120495376, 0.5428680190579105, 0.6682372175980975, 0.0, 0.0, 0.8671524549901557, 0.13650739169874232, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05812427836674714, 0.8576738423416447, nan, 0.0, 0.00432695279500546, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.542777958917509, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 1.1036 | 49.0 | 1960 | 2.5498 | 0.0776 | 0.1220 | 0.5114 | [0.4133487645378117, 0.38840612166775607, 0.8276707988684311, 0.31391034720361366, 0.2731137088204038, 0.6067755185172613, 0.3888670458402831, 0.05633560662502149, 0.3514952544903338, 0.6482954215092803, 0.0, 0.0, 0.683471837488458, 0.049228958787908506, 0.0, 0.0, 0.0, 0.003574039476890586, 0.0, 0.024570584786432433, 0.506481650538616, 0.0, 0.0, 0.0023997998271234246, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.3585263732223132, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.8818254603682947, 0.5994112505400219, 0.8435527707623709, 0.8342910396132277, 0.45672760912432747, 0.8105187319884726, 0.8796451279671644, 0.06095230579309023, 0.5666412888648755, 0.7457565398335315, 0.0, 0.0, 0.8764149092360675, 0.049228958787908506, 0.0, 0.0, 0.0, 0.0036514522821576765, 0.0, 0.059383856408103286, 0.8592225491714418, nan, 0.0, 0.004347557332124534, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.492777958917509, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.4636 | 49.5 | 1980 | 2.6073 | 0.0783 | 0.1209 | 0.5123 | [0.4190899057663369, 0.4034074834995509, 0.8188614900314796, 0.310745736798069, 0.2526684555754323, 0.5953904678325604, 0.39341237711050014, 0.0400709295390645, 0.34969157750252194, 0.6252807249784311, 0.0, 0.0, 0.7207004683827422, 0.06832724509058814, 0.0, 0.0, 0.0, 0.0, 0.0, 0.021518661518661518, 0.5526315789473685, 0.0, 0.0, 0.002074080281572111, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.38047540908883726, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9102894079969859, 0.5902484603096461, 0.8320587505883663, 0.840358773593352, 0.41261533218102586, 0.8066796316075855, 0.9034708296735774, 0.0424180247011009, 0.5193157817181591, 0.7055737217598097, 0.0, 0.0, 0.8567939323820543, 0.06832724509058814, 0.0, 0.0, 0.0, 0.0, 0.0, 0.054844127217382174, 0.855350782096949, nan, 0.0, 0.0040796983495765765, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5443267036191718, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
| 0.4043 | 50.0 | 2000 | 2.5670 | 0.0774 | 0.1223 | 0.5135 | [0.4213891557064807, 0.40007258150986036, 0.8231900478645917, 0.31773881264336534, 0.28457760460984965, 0.5986723895121013, 0.3926239843434319, 0.05552524795943285, 0.3580998394812543, 0.6047520580036383, 0.0, 0.0, 0.6585019058951657, 0.03675010419475839, 0.0, 0.0, 0.0, 0.0035096310806398954, 0.0, 0.02546663313395952, 0.5147571035747021, 0.0, 0.0, 0.0033711666102861407, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.384688111763659, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] | [0.9070256204963971, 0.5848030301508043, 0.8408483946668286, 0.8314708252280996, 0.46407965528154443, 0.8074403982184962, 0.891030421548643, 0.06024205049576947, 0.5533670612505526, 0.6868088585017836, 0.0, 0.0, 0.8836340755475267, 0.03675010419475839, 0.0, 0.0, 0.0, 0.0035684647302904565, 0.0, 0.061115776214967985, 0.8697537556140622, nan, 0.0, 0.005954711227412276, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.5638245842843169, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan] |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
JayHyeon/Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep
|
JayHyeon
| 2025-06-19T03:42:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:19:45Z |
---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/bmljfinm)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
ilybawkugo/lora_llama_1e5_16_32
|
ilybawkugo
| 2025-06-19T03:35:17Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T03:35:16Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ilybawkugo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
elliotthwang/Llama-3.2-3B-Instruct-tw
|
elliotthwang
| 2025-06-19T03:28:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T03:20:12Z |
---
library_name: transformers
tags: []
---
# 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. -->
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]
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- **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]
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[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]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
OgiServiceDesigner/swallow-7b-hf-finetuned-cot-fermi
|
OgiServiceDesigner
| 2025-06-19T03:07:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:tokyotech-llm/Swallow-7b-hf",
"base_model:adapter:tokyotech-llm/Swallow-7b-hf",
"region:us"
] | null | 2025-06-19T01:52:02Z |
---
base_model: tokyotech-llm/Swallow-7b-hf
library_name: peft
---
# 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 0.15.2
|
Huangxs/MedPLIB-7b-2e
|
Huangxs
| 2025-06-19T03:07:39Z | 27 | 1 | null |
[
"pytorch",
"medplib_moe_llama",
"license:apache-2.0",
"region:us"
] | null | 2024-12-25T08:50:09Z |
---
license: apache-2.0
---
|
nnilayy/dreamer-arousal-binary-classification-Kfold-5
|
nnilayy
| 2025-06-19T03:06:53Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-19T03:06:51Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
snowman477342/pass-finetuned-qwen3-rec
|
snowman477342
| 2025-06-19T02:32:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:adapter:Qwen/Qwen3-8B-Base",
"region:us"
] | null | 2025-06-19T02:31:46Z |
---
base_model: Qwen/Qwen3-8B-Base
library_name: peft
---
# 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]
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[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 0.15.2
|
minhxle/truesight-ft-job-0baa27e6-7a7f-426c-833d-b89df0f0c2d6
|
minhxle
| 2025-06-19T02:30:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T02:30:08Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Sayan01/Phi3-TL-Meta-DKD-5
|
Sayan01
| 2025-06-19T02:19:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:17:49Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
allenai/Molmo-7B-O-0924
|
allenai
| 2025-06-19T02:18:50Z | 6,272 | 159 |
transformers
|
[
"transformers",
"safetensors",
"molmo",
"text-generation",
"multimodal",
"olmo",
"pixmo",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"arxiv:2409.17146",
"base_model:openai/clip-vit-large-patch14-336",
"base_model:finetune:openai/clip-vit-large-patch14-336",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
image-text-to-text
| 2024-09-25T05:53:18Z |
---
license: apache-2.0
language:
- en
base_model:
- openai/clip-vit-large-patch14-336
- allenai/OLMo-7B-1124
pipeline_tag: image-text-to-text
tags:
- multimodal
- olmo
- molmo
- pixmo
library_name: transformers
---
<img src="molmo_logo.png" alt="Logo for the Molmo Project" style="width: auto; height: 50px;">
# Molmo 7B-O
Molmo is a family of open vision-language models developed by the Allen Institute for AI.
Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs.
It has state-of-the-art performance among multimodal models with a similar size while being fully open-source.
You can find all models in the Molmo family [here](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19).
**Learn more** about the Molmo family [in our announcement blog post](https://molmo.allenai.org/blog) or the [paper](https://huggingface.co/papers/2409.17146).
Molmo 7B-O is based on [OLMo-7B-1024](https://huggingface.co/allenai/OLMo-7B-1024-preview) (a **preview** of next generation of OLMo models)
and uses [OpenAI CLIP](https://huggingface.co/openai/clip-vit-large-patch14-336) as vision backbone.
It performs comfortably between GPT-4V and GPT-4o on both academic benchmarks and human evaluation.
This checkpoint is a **preview** of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
[**Sign up here**](https://docs.google.com/forms/d/e/1FAIpQLSdML1MhNNBDsCHpgWG65Oydg2SjZzVasyqlP08nBrWjZp_c7A/viewform) to be the first to know when artifacts are released.
Quick links:
- 💬 [Demo](https://molmo.allenai.org/)
- 📂 [All Models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19)
- 📃 [Paper](https://molmo.allenai.org/paper.pdf)
- 🎥 [Blog with Videos](https://molmo.allenai.org/blog)
## Quick Start
To run Molmo, first install dependencies:
```bash
pip install einops torchvision
```
Then, follow these steps:
```python
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
# load the processor
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-O-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/Molmo-7B-O-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# process the image and text
inputs = processor.process(
images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
text="Describe this image."
)
# move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
# only get generated tokens; decode them to text
generated_tokens = output[0,inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
# print the generated text
print(generated_text)
# >>> This photograph captures an adorable black Labrador puppy sitting on a weathered
# wooden deck. The deck's planks, which are a mix of light and dark brown with ...
```
To make inference more efficient, run with autocast:
```python
with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
```
We did most of our evaluations in this setting (autocast on, but float32 weights)
To even further reduce the memory requirements, the model can be run with bfloat16 weights:
```python
model.to(dtype=torch.bfloat16)
inputs["images"] = inputs["images"].to(torch.bfloat16)
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
```
Note that this can sometimes change the output of the model compared to running with float32 weights.
## Evaluations
| Model | Average Score on 11 Academic Benchmarks | Human Preference Elo Rating |
|-----------------------------|-----------------------------------------|-----------------------------|
| Molmo 72B | 81.2 | 1077 |
| Molmo 7B-D | 77.3 | 1056 |
| **Molmo 7B-O (this model)** | **74.6** | **1051** |
| MolmoE 1B | 68.6 | 1032 |
| GPT-4o | 78.5 | 1079 |
| GPT-4V | 71.1 | 1041 |
| Gemini 1.5 Pro | 78.3 | 1074 |
| Gemini 1.5 Flash | 75.1 | 1054 |
| Claude 3.5 Sonnet | 76.7 | 1069 |
| Claude 3 Opus | 66.4 | 971 |
| Claude 3 Haiku | 65.3 | 999 |
| Qwen VL2 72B | 79.4 | 1037 |
| Qwen VL2 7B | 73.7 | 1025 |
| Intern VL2 LLAMA 76B | 77.1 | 1018 |
| Intern VL2 8B | 69.4 | 953 |
| Pixtral 12B | 69.5 | 1016 |
| Phi3.5-Vision 4B | 59.7 | 982 |
| PaliGemma 3B | 50.0 | 937 |
| LLAVA OneVision 72B | 76.6 | 1051 |
| LLAVA OneVision 7B | 72.0 | 1024 |
| Cambrian-1 34B | 66.8 | 953 |
| Cambrian-1 8B | 63.4 | 952 |
| xGen - MM - Interleave 4B | 59.5 | 979 |
| LLAVA-1.5 13B | 43.9 | 960 |
| LLAVA-1.5 7B | 40.7 | 951 |
*Benchmarks: AI2D test, ChartQA test, VQA v2.0 test, DocQA test, InfographicVQA test, TextVQA val, RealWorldQA, MMMU val, MathVista testmini, CountBenchQA, Flickr Count (we collected this new dataset that is significantly harder than CountBenchQA).*
## FAQs
### I'm getting an error a broadcast error when processing images!
Your image might not be in RGB format. You can convert it using the following code snippet:
```python
from PIL import Image
image = Image.open(...)
if image.mode != "RGB":
image = image.convert("RGB")
```
### Molmo doesn't work great with transparent images!
We received reports that Molmo models might struggle with transparent images.
For the time being, we recommend adding a white or dark background to your images before passing them to the model. The code snippet below shows how to do this using the Python Imaging Library (PIL):
```python
# Load the image
url = "..."
image = Image.open(requests.get(url, stream=True).raw)
# Convert the image to grayscale to calculate brightness
gray_image = image.convert('L') # Convert to grayscale
# Calculate the average brightness
stat = ImageStat.Stat(gray_image)
average_brightness = stat.mean[0] # Get the average value
# Define background color based on brightness (threshold can be adjusted)
bg_color = (0, 0, 0) if average_brightness > 127 else (255, 255, 255)
# Create a new image with the same size as the original, filled with the background color
new_image = Image.new('RGB', image.size, bg_color)
# Paste the original image on top of the background (use image as a mask if needed)
new_image.paste(image, (0, 0), image if image.mode == 'RGBA' else None)
# Now you can pass the new_image to Molmo
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
```
## License and Use
This model is licensed under Apache 2.0. It is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
|
FormlessAI/7d53b76d-29f6-45b5-ab72-7ac04e2ee669
|
FormlessAI
| 2025-06-19T02:09:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:finetune:unsloth/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T23:23:25Z |
---
base_model: unsloth/Qwen2.5-Math-1.5B
library_name: transformers
model_name: 7d53b76d-29f6-45b5-ab72-7ac04e2ee669
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 7d53b76d-29f6-45b5-ab72-7ac04e2ee669
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/7d53b76d-29f6-45b5-ab72-7ac04e2ee669", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/pylz4h49)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Yuichi1218/Llama-3.1-Lafeak-8B-nonfilter-Lafeak-Corpus
|
Yuichi1218
| 2025-06-19T02:07:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T23:46:03Z |
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Dataset
Lafeak Corpus (non-filtering)
# Uploaded model
- **Developed by:** Yuichi1218
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BootesVoid/cmc2ot9lm00dqaqihbt3pnz4i_cmc2ozs3d00e7aqihptp1wayy
|
BootesVoid
| 2025-06-19T02:03:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-19T02:03:49Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SOPHW69
---
# Cmc2Ot9Lm00Dqaqihbt3Pnz4I_Cmc2Ozs3D00E7Aqihptp1Wayy
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SOPHW69` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SOPHW69",
"lora_weights": "https://huggingface.co/BootesVoid/cmc2ot9lm00dqaqihbt3pnz4i_cmc2ozs3d00e7aqihptp1wayy/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc2ot9lm00dqaqihbt3pnz4i_cmc2ozs3d00e7aqihptp1wayy', weight_name='lora.safetensors')
image = pipeline('SOPHW69').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc2ot9lm00dqaqihbt3pnz4i_cmc2ozs3d00e7aqihptp1wayy/discussions) to add images that show off what you’ve made with this LoRA.
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_one_liners-comb1-seed28-2025-06-19
|
morturr
| 2025-06-19T02:03:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T02:02:53Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_one_liners-comb1-seed28-2025-06-19
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-2-7b-hf-LOO_amazon-COMB_one_liners-comb1-seed28-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None 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: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
brendmung/AbodeLLM
|
brendmung
| 2025-06-19T01:56:52Z | 0 | 0 | null |
[
"text-generation",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct",
"region:us"
] |
text-generation
| 2024-09-30T20:26:36Z |
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- HuggingFaceTB/SmolLM2-360M-Instruct
pipeline_tag: text-generation
---
# Models for AbodeLLM App
This repository contains models used by **AbodeLLM**, an offline AI chat assistant app built for Android devices.
## Usage
To run the models on your Android device, download the **AbodeLLM** app from the following repository:
[AbodeLLM App on GitHub](https://github.com/brendmung/AbodeLLM)
|
buttercoconut/Qwen2.5-ko-alpaca-0.5B-Q4
|
buttercoconut
| 2025-06-19T01:47:00Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"text-generation",
"conversational",
"ko",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:quantized:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-06-19T01:25:27Z |
---
license: apache-2.0
language:
- ko
base_model:
- Qwen/Qwen2.5-0.5B
pipeline_tag: text-generation
---
|
morturr/Mistral-7B-v0.1-amazon-seed-42-2025-06-19
|
morturr
| 2025-06-19T01:45:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T01:45:30Z |
---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-amazon-seed-42-2025-06-19
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. -->
# Mistral-7B-v0.1-amazon-seed-42-2025-06-19
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
hardlyworking/4BTestRC
|
hardlyworking
| 2025-06-19T01:30:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1",
"base_model:hardlyworking/HoldMy4BKTO",
"base_model:finetune:hardlyworking/HoldMy4BKTO",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T20:50:46Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: hardlyworking/HoldMy4BKTO
tags:
- axolotl
- generated_from_trainer
datasets:
- PocketDoc/Dans-Prosemaxx-RepRemover-1
model-index:
- name: RepRemove4B
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: `0.11.0.dev0`
```yaml
base_model: hardlyworking/HoldMy4BKTO
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: PocketDoc/Dans-Prosemaxx-RepRemover-1
type: dan-chat-advanced
val_set_size: 0
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true
hub_model_id: hardlyworking/RepRemove4B
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
sequence_len: 32768
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: Xgen4Brep
wandb_entity:
wandb_watch:
wandb_name: Xgen4Brep
wandb_log_model:
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
deepspeed:
warmup_ratio: 0.05
saves_per_epoch: 1
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
pad_token:
```
</details><br>
# RepRemove4B
This model is a fine-tuned version of [hardlyworking/HoldMy4BKTO](https://huggingface.co/hardlyworking/HoldMy4BKTO) on the PocketDoc/Dans-Prosemaxx-RepRemover-1 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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: 22
- training_steps: 456
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
luyotw/openfun-ivod-whisper-small-HuangKuoChang-11-185
|
luyotw
| 2025-06-19T01:22:12Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-06-19T00:59:11Z |
# Fine-tune 資訊
- 原始模型: `openai/whisper-small`
- 使用音訊數量: 35047
- 使用音訊總長: 21.07 小時
- 音訊平均長度: 2.16 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 05:20:34
- 模型大小: 0.90 GB
---
# Model Card
|
rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_global_step821
|
rosieyzh
| 2025-06-19T01:17:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T01:15:28Z |
---
library_name: transformers
tags: []
---
# 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. -->
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]
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### Direct Use
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### 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]
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#### Training Hyperparameters
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#### 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
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[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]
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[More Information Needed]
#### Hardware
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|
rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_global_step291
|
rosieyzh
| 2025-06-19T01:05:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T01:03:26Z |
---
library_name: transformers
tags: []
---
# 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. -->
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
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[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]
|
trhgquan/phobert-finetune-freezed-seg-6969
|
trhgquan
| 2025-06-19T00:59:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"vi",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T08:59:00Z |
---
license: gpl-3.0
language:
- vi
metrics:
- accuracy
- f1
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
library_name: transformers
---
|
trhgquan/phobert-finetune-freezed-seg-24
|
trhgquan
| 2025-06-19T00:59:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"vi",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T08:58:16Z |
---
license: gpl-3.0
language:
- vi
metrics:
- accuracy
- f1
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
library_name: transformers
---
|
trhgquan/phobert-finetune-from-scratch-seg-42
|
trhgquan
| 2025-06-19T00:58:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"vi",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T08:59:35Z |
---
license: gpl-3.0
language:
- vi
metrics:
- accuracy
- f1
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
library_name: transformers
---
|
chiruan/qwen2.5-7b-coder_V2-100steps
|
chiruan
| 2025-06-19T00:58:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T00:32:26Z |
---
library_name: transformers
tags: []
---
# 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. -->
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 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]
|
trhgquan/phobert-finetune-freezed-24
|
trhgquan
| 2025-06-19T00:57:02Z | 1 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"vi",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-18T08:52:25Z |
---
license: gpl-3.0
language:
- vi
metrics:
- accuracy
- f1
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
library_name: transformers
---
|
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