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bge-base-en-v1.5-mnrl-cl-multi

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:200000
loss:MultipleNegativesRankingLoss
loss:ContrastiveLoss
Eval Results
text-embeddings-inference
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 ---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:200000
- loss:MultipleNegativesRankingLoss
- loss:ContrastiveLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: What is the best sushi restaurant in Los Angeles, aside from Urasawa
which is impractical for regular visits?
sentences:
- How do I stop feeling sorry for ignorant and arrogant people?
- What are the best sushi restaurants in Los Angeles?
- Why do people flirt on Quora?
- source_sentence: Why are many Quora writers lonely and/ or unemployed?
sentences:
- Are writers on Quora mostly lonely or have no job (unemployed)?
- What are the attributes of monkeys belongs to Japanese-macaque monkey Family?
- I want to change the education system in India. How can I have such power?
- source_sentence: What is the best, and painless way to kill myself?
sentences:
- What is a way to commit suicide and not damaging your organs so that they can
be donated?
- How do I beat insomnia?
- What is the most painless way to commit suicide?
- source_sentence: What are ETF'S and what is the difference between ETF'S and mutual
funds?
sentences:
- What is the difference between ETF and mutual funds?
- What's better, an index ETF or an index mutual fund?
- 'Income Tax: How to check pan card status?'
- source_sentence: For what reasons can't the Olympics be held in India?
sentences:
- What are the best hotels to stay in Goa?
- When will Olympics be held in India?
- When will India qualify for the FIFA World Cup?
datasets:
- sentence-transformers/quora-duplicates
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
- average_precision
- f1
- precision
- recall
- threshold
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: quora duplicates
type: quora-duplicates
metrics:
- type: cosine_accuracy
value: 0.833
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8065301179885864
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.7630522088353413
name: Cosine F1
- type: cosine_f1_threshold
value: 0.745335042476654
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.6705882352941176
name: Cosine Precision
- type: cosine_recall
value: 0.8850931677018633
name: Cosine Recall
- type: cosine_ap
value: 0.8120519897128382
name: Cosine Ap
- type: cosine_mcc
value: 0.641402259734116
name: Cosine Mcc
- task:
type: paraphrase-mining
name: Paraphrase Mining
dataset:
name: quora duplicates dev
type: quora-duplicates-dev
metrics:
- type: average_precision
value: 0.6286866338232051
name: Average Precision
- type: f1
value: 0.6032452480296708
name: F1
- type: precision
value: 0.5627297495999654
name: Precision
- type: recall
value: 0.6500474596592896
name: Recall
- type: threshold
value: 0.7944510877132416
name: Threshold
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9732
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9944
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9958
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9732
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.432
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.27652
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14606
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8392449568046333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9654790046130339
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9826052435636259
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9955256342023989
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9852328208350886
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.983879365079365
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9794253454223505
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("manestay/bge-base-en-v1.5-mnrl-cl-multi")
# Run inference
sentences = [
"For what reasons can't the Olympics be held in India?",
'When will Olympics be held in India?',
'When will India qualify for the FIFA World Cup?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `quora-duplicates`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.833 |
| cosine_accuracy_threshold | 0.8065 |
| cosine_f1 | 0.7631 |
| cosine_f1_threshold | 0.7453 |
| cosine_precision | 0.6706 |
| cosine_recall | 0.8851 |
| **cosine_ap** | **0.8121** |
| cosine_mcc | 0.6414 |
#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator) with these parameters:
```json
{'add_transitive_closure': <function ParaphraseMiningEvaluator.add_transitive_closure at 0x7f26a89802c0>, 'max_pairs': 500000, 'top_k': 100}
```
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.6287** |
| f1 | 0.6032 |
| precision | 0.5627 |
| recall | 0.65 |
| threshold | 0.7945 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9732 |
| cosine_accuracy@3 | 0.9944 |
| cosine_accuracy@5 | 0.9958 |
| cosine_accuracy@10 | 0.9994 |
| cosine_precision@1 | 0.9732 |
| cosine_precision@3 | 0.432 |
| cosine_precision@5 | 0.2765 |
| cosine_precision@10 | 0.1461 |
| cosine_recall@1 | 0.8392 |
| cosine_recall@3 | 0.9655 |
| cosine_recall@5 | 0.9826 |
| cosine_recall@10 | 0.9955 |
| **cosine_ndcg@10** | **0.9852** |
| cosine_mrr@10 | 0.9839 |
| cosine_map@100 | 0.9794 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### mnrl
* Dataset: [mnrl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 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: 13.85 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.76 tokens</li><li>max: 64 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|
| <code>Why in India do we not have one on one political debate as in USA?</code> | <code>Why cant we have a public debate between politicians in India like the one in US?</code> | <code>Can people on Quora stop India Pakistan debate? We are sick and tired seeing this everyday in bulk?</code> |
| <code>What is OnePlus One?</code> | <code>How is oneplus one?</code> | <code>Why is OnePlus One so good?</code> |
| <code>Does our mind control our emotions?</code> | <code>How do smart and successful people control their emotions?</code> | <code>How can I control my positive emotions for the people whom I love but they don't care about me?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### cl
* Dataset: [cl](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.3 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.66 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~62.00%</li><li>1: ~38.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------|
| <code>What is the step by step guide to invest in share market in india?</code> | <code>What is the step by step guide to invest in share market?</code> | <code>0</code> |
| <code>What is the story of Kohinoor (Koh-i-Noor) Diamond?</code> | <code>What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?</code> | <code>0</code> |
| <code>How can I increase the speed of my internet connection while using a VPN?</code> | <code>How can Internet speed be increased by hacking through DNS?</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Evaluation Datasets
#### mnrl
* Dataset: [mnrl](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: 7 tokens</li><li>mean: 13.84 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.8 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.71 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Which programming language is best for developing low-end games?</code> | <code>What coding language should I learn first for making games?</code> | <code>I am entering the world of video game programming and want to know what language I should learn? Because there are so many languages ​​I do not know which one to start with. Can you recommend a language that's easy to learn and can be used with many platforms?</code> |
| <code>Was it appropriate for Meryl Streep to use her Golden Globes speech to attack Donald Trump?</code> | <code>Should Meryl Streep be using her position to attack the president?</code> | <code>Why did Kelly Ann Conway say that Meryl Streep incited peoples worst feelings?</code> |
| <code>Where can I found excellent commercial fridges in Sydney?</code> | <code>Where can I found impressive range of commercial fridges in Sydney?</code> | <code>What is the best grocery delivery service in Sydney?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### cl
* Dataset: [cl](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>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.59 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.65 tokens</li><li>max: 76 tokens</li></ul> | <ul><li>0: ~63.40%</li><li>1: ~36.60%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:---------------|
| <code>What should I ask my friend to get from UK to India?</code> | <code>What is the process of getting a surgical residency in UK after completing MBBS from India?</code> | <code>0</code> |
| <code>How can I learn hacking for free?</code> | <code>How can I learn to hack seriously?</code> | <code>1</code> |
| <code>Which is the best website to learn programming language C++?</code> | <code>Which is the best website to learn C++ Programming language for free?</code> | <code>0</code> |
* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
```json
{
"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
"margin": 0.5,
"size_average": true
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 400
- `per_device_eval_batch_size`: 400
- `num_train_epochs`: 100
- `warmup_ratio`: 0.1
- `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`: 400
- `per_device_eval_batch_size`: 400
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 100
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: 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
</details>
### Training Logs
| Epoch | Step | Training Loss | mnrl loss | cl loss | quora-duplicates_cosine_ap | quora-duplicates-dev_average_precision | cosine_ndcg@10 |
|:-------:|:-------:|:-------------:|:----------:|:----------:|:--------------------------:|:--------------------------------------:|:--------------:|
| 0 | 0 | - | - | - | 0.7461 | 0.5988 | 0.9831 |
| 0.2 | 100 | 0.2804 | - | - | - | - | - |
| 0.4 | 200 | 0.2006 | - | - | - | - | - |
| **0.5** | **250** | **-** | **0.1153** | **0.0157** | **0.7661** | **0.6165** | **0.9839** |
| 0.6 | 300 | 0.1704 | - | - | - | - | - |
| 0.8 | 400 | 0.1459 | - | - | - | - | - |
| 1.0 | 500 | 0.1296 | 0.0835 | 0.0146 | 0.7860 | 0.6238 | 0.9843 |
| 1.2 | 600 | 0.1344 | - | - | - | - | - |
| 1.4 | 700 | 0.1181 | - | - | - | - | - |
| 1.5 | 750 | - | 0.0737 | 0.0139 | 0.7983 | 0.6263 | 0.9847 |
| 1.6 | 800 | 0.1176 | - | - | - | - | - |
| 1.8 | 900 | 0.119 | - | - | - | - | - |
| 2.0 | 1000 | 0.1127 | 0.0682 | 0.0133 | 0.8121 | 0.6287 | 0.9852 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.0+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- 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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
```
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