sentence-transformers safetensors esm sentence-similarity feature-extraction generated_from_trainer dataset_size:753444 loss:CoSENTLoss arxiv:1908.10084 base_model:facebook/esm2_t6_8M_UR50D base_model:finetune:facebook/esm2_t6_8M_UR50D model-index autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/esm2_t6_8M_UR50D
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:753444
- loss:CoSENTLoss
widget:
- source_sentence: A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E S D Y
    Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A
    A K S F S L K I S D S Q L G D A A M Y F C C A Y R S M S N Y Q L I W W G A G T
    K L I I K P D
  sentences:
  - A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q
    P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K
    I S D S Q L G D T A M Y F C C A F V A N A G G T S Y G K L T F F G Q G T I L T
    V H P N
  - A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E S D Y Y L F W Y K Q
    P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K
    I S D S Q L G D A A M Y F C C A Y R S P N Y G G S Q G N L I F F G K G T K L S
    V K P N
  - A Q S V A Q P E D Q V N V A E G N P L T V K C T Y S V S G N P Y L F W Y V Q Y
    P N R G L Q F L L K Y I T G D N L V K G S Y G F E A E F N K S Q T S F H L K K
    P S A L V S D S A L Y F C A L D Q A G T A L I F G K G T T L S V S S N
- source_sentence: L A K T T Q P I S M D S Y E G Q E V N I T C S H N N I A T N D Y
    I T W Y Q Q F P S Q G P R F I I Q G Y K T K V T N E V A S L F I P A D R K S S
    T L S L P R V S L S D T A V Y Y C C L P S G M N Y G G S Q G N L I F F G K G T
    K L S V K P N
  sentences:
  - I L N V E Q S P Q S L H V Q E G D S T N F T C S F P S S N F Y A L H W Y R W E
    T A K S P E A L F V M T L N G D E K K K G R I S A T L N T K E G Y S Y L Y I K
    G S Q P E D S A T Y L C A F I T G N Q F Y F G T G T S L T V I P N
  - A Q K I T Q T Q P G M F V Q E K E A V T L D C T Y D T S D P S Y G L F W Y K Q
    P S S G E M I F L I Y Q G S Y D Q Q N A T E G R Y S L N F Q K A R K S A N L V
    I S A S Q L G D S A M Y F C C A M R G D A G G T S Y G K L T F F G Q G T I L T
    V H P N
  - Q K E V E Q D P G P L S V P E G A I V S L N C T Y S N S A F Q Y F M W Y R Q Y
    S R K G P E L L M Y T Y S S G N K E D G R F T A Q V D K S S K Y I S L F I R D
    S Q P S D S A T Y L C C A M R V I G S D D K I I F F G K G T R L H I L P N
- source_sentence: T Q L L E Q S P Q F L S I Q E G E N L T V Y C N S S S V F S S L
    Q W Y R Q E P G E G P V L L V T V V T G G E V K K L K R L T F Q F G D A R K D
    S S L H I T A A Q P G D T G L Y L C C A G V P Y N N N D M R F F G A G T R L T
    V K P N
  sentences:
  - T Q L L E Q S P Q F L S I Q E G E N L T V Y C N S S S V F S S L Q W Y R Q E P
    G E G P V L L V T V V T G G E V K K L K R L T F Q F G D A R K D S S L H I T A
    A Q P G D T G L Y L C C A G A A H P L N Y G G S Q G N L I F F G K G T K L S V
    K P N
  - G N S V T Q M E G P V T L S E E A F L T I N C T Y T A T G Y P S L F W Y V Q Y
    P G E G L Q L L L K A T K A D D K G S N K G F E A T Y R K E T T S F H L E K G
    S V Q V S D S A V Y F C C A F N D Y K L S F F G A G T T V T V R A N
  - D A K T T Q P P S M D C A E G R A A N L P C N H S T I S G N E Y V Y W Y R Q I
    H S Q G P Q Y I I H G L K N N E T N E M A S L I I T E D R K S S T L I L P H A
    T L R D T A V Y Y C C I V R A G G G G W S G G G A D G L T F F G K G T H L I I
    Q P Y
- source_sentence: L A K T T Q P I S M D S Y E G Q E V N I T C S H N N I A T N D Y
    I T W Y Q Q F P S Q G P R F I I Q G Y K T K V T N E V A S L F I P A D R K S S
    T L S L P R V S L S D T A V Y Y C C L V G E G P S G G Y Q K V T F F G I G T K
    L Q V I P N
  sentences:
  - A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q
    P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T
    I T A S Q V V D S A V Y F C C A L S D A Y N F N K F Y F F G S G T K L N V K P
    N
  - A Q R V T Q P E K L L S V F K G A P V E L K C N Y S Y S G S P E L F W Y V Q Y
    S R Q R L Q L L L R H I S R E S I K G F T A D L N K G E T S F H L K K P F A Q
    E E D S A M Y Y C A L R A R G S T L G R L Y F G R G T Q L T V W P D
  - Q K E V E Q D P G P L S V P E G A I V S L N C T Y S N S A F Q Y F M W Y R Q Y
    S R K G P E L L M Y T Y S S G N K E D G R F T A Q V D K S S K Y I S L F I R D
    S Q P S D S A T Y L C C A M R G Y Q K V T F F G I G T K L Q V I P N
- source_sentence: A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y
    Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S
    T S S F N F T I T A S Q V V D S A V Y F C C A L L Y N N N D M R F F G A G T R
    L T V K P N
  sentences:
  - A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q
    P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T
    I T A S Q V V D S A V Y F C C A L S E T P R G G G T S Y G K L T F F G Q G T I
    L T V H P N
  - Q K E V E Q N S G P L S V P E G A I A S L N C T Y S D R G S Q S F F W Y R Q Y
    S G K S P E L I M F I Y S N G D K E D G R F T A Q L N K A S Q Y V S L L I R D
    S Q P S D S A T Y L C C A V A D D K I I F F G K G T R L H I L P N
  - G Q S L E Q P S E V T A V E G A I V Q I N C T Y Q T S G F Y G L S W Y Q Q H D
    G G A P T F L S Y N A L D G L E E T G R F S S F L S R S D S Y G Y L L L Q E L
    Q M K D S A S Y F C A V S P Y G Q N F V F G P G T R L S V L P Y
model-index:
- name: SentenceTransformer based on facebook/esm2_t6_8M_UR50D
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: all dev
      type: all-dev
    metrics:
    - type: pearson_cosine
      value: 0.8253873350708476
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8706098612115536
      name: Spearman Cosine
---

# SentenceTransformer based on facebook/esm2_t6_8M_UR50D

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D). It maps sentences & paragraphs to a 320-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:** [facebook/esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) <!-- at revision c731040fcd8d73dceaa04b0a8e6329b345b0f5df -->
- **Maximum Sequence Length:** 1026 tokens
- **Output Dimensionality:** 320 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 1026, 'do_lower_case': False}) with Transformer model: EsmModel 
  (1): Pooling({'word_embedding_dimension': 320, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("HassanCS/TCRa_HLA_peptide_ESM")
# Run inference
sentences = [
    'A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L L Y N N N D M R F F G A G T R L T V K P N',
    'A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L S E T P R G G G T S Y G K L T F F G Q G T I L T V H P N',
    'Q K E V E Q N S G P L S V P E G A I A S L N C T Y S D R G S Q S F F W Y R Q Y S G K S P E L I M F I Y S N G D K E D G R F T A Q L N K A S Q Y V S L L I R D S Q P S D S A T Y L C C A V A D D K I I F F G K G T R L H I L P N',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 320]

# 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

#### Semantic Similarity

* Dataset: `all-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8254     |
| **spearman_cosine** | **0.8706** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 753,444 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                            | sentence2                                                                             | score                                                           |
  |:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|
  | type    | string                                                                               | string                                                                                | float                                                           |
  | details | <ul><li>min: 108 tokens</li><li>mean: 116.0 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 107 tokens</li><li>mean: 116.16 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.38</li><li>max: 0.97</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                                  | sentence2                                                                                                                                                                                                                                                  | score                           |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>T Q L L E Q S P Q F L S I Q E G E N L T V Y C N S S S V F S S L Q W Y R Q E P G E G P V L L V T V V T G G E V K K L K R L T F Q F G D A R K D S S L H I T A A Q P G D T G L Y L C C A G A G G G S Q G N L I F F G K G T K L S V K P N</code>         | <code>T Q L L E Q S P Q F L S I Q E G E N L T V Y C N S S S V F S S L Q W Y R Q E P G E G P V L L V T V V T G G E V K K L K R L T F Q F G D A R K D S S L H I T A A Q P G D T G L Y L C C A G G N G G S Q G N L I F F G K G T K L S V K P N</code>         | <code>0.8347107438016529</code> |
  | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D T A M Y F C A F A E Y G N K L V F G A G T I L R V K S Y</code>           | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E S D Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D A A M Y F C A L F S G S R L T F G E G T Q L T V N P D</code>             | <code>0.0</code>                |
  | <code>A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L L I F S G G Y N K L I F F G A G T R L A V H P Y</code> | <code>A Q K V T Q A Q T E I S V V E K E D V T L D C V Y E T R D T T Y Y L F W Y K Q P P S G E L V F L I R R N S F D E Q N E I S G R Y S W N F Q K S T S S F N F T I T A S Q V V D S A V Y F C C A L S E A G S G Y S T L T F F G K G T M L L V S P D</code> | <code>0.4008264462809917</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 83,716 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                             | sentence2                                                                             | score                                                           |
  |:--------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------|
  | type    | string                                                                                | string                                                                                | float                                                           |
  | details | <ul><li>min: 106 tokens</li><li>mean: 116.08 tokens</li><li>max: 126 tokens</li></ul> | <ul><li>min: 109 tokens</li><li>mean: 116.05 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.39</li><li>max: 0.97</li></ul> |
* Samples:
  | sentence1                                                                                                                                                                                                                                            | sentence2                                                                                                                                                                                                                                            | score                            |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>G E N V E Q H P S T L S V Q E G D S A V I K C T Y S D S A S N Y F P W Y K Q E L G K G P Q L I I D I R S N V G E K K D Q R I A V T L N K T A K H F S L H I T E T Q P E D S A V Y F C A A S M N N Y G Q N F V F G P G T R L S V L P Y</code>     | <code>G E D V E Q S L F L S V R E G D S S V I N C T Y T D S S S T Y L Y W Y K Q E P G A G L Q L L T Y I F S N M D M K Q D Q R L T V L L N K K D K H L S L R I A D T Q T G D S A I Y F C A E R A G A N N L F F G T G T R L T V I P Y</code>           | <code>0.09297520661157023</code> |
  | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D T A M Y F C C A S H M N N A R L M F F G D G T Q L V V K P N</code> | <code>A Q T V T Q S Q P E M S V Q E A E T V T L S C T Y D T S E N N Y Y L F W Y K Q P P S R Q M I L V I R Q E A Y K Q Q N A T E N R F S V N F Q K A A K S F S L K I S D S Q L G D T A M Y F C C S S G G G A D G L T F F G K G T H L I I Q P Y</code> | <code>0.00826446280991735</code> |
  | <code>G Q S L E Q P S E V T A V E G A I V Q I N C T Y Q T S G F Y G L S W Y Q Q H D G G A P T F L S Y N A L D G L E E T G R F S S F L S R S D S Y G Y L L L Q E L Q M K D S A S Y F C C A L A G G G N K L T F F G T G T Q L K V E L N</code>         | <code>K N Q V E Q S P Q S L I I L E G K N C T L Q C N Y T V S P F S N L R W Y K Q D T G R G P V S L T I M T F S E N T K S N G R Y T A T L D A D T K Q S S L H I T A S Q L S D S A S Y I C C V V S S Y S S A S K I I F F G S G T R L S I R P N</code> | <code>0.9690082644628099</code>  |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 0.001
- `weight_decay`: 0.0001
- `num_train_epochs`: 2
- `fp16`: True
- `load_best_model_at_end`: True

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.001
- `weight_decay`: 0.0001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch      | Step      | Training Loss | Validation Loss | all-dev_spearman_cosine |
|:----------:|:---------:|:-------------:|:---------------:|:-----------------------:|
| 0.3397     | 2000      | 8.8932        | 8.8505          | 0.5332                  |
| 0.6795     | 4000      | 8.8096        | 8.7699          | 0.6565                  |
| 1.0192     | 6000      | 8.7188        | 8.6631          | 0.7476                  |
| 1.3589     | 8000      | 8.592         | 8.5352          | 0.8242                  |
| **1.6987** | **10000** | **8.4614**    | **8.4169**      | **0.8706**              |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.3.1
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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