sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:724 loss:CoSENTLoss arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:724
- loss:CoSENTLoss
widget:
- source_sentence: Financials
  sentences:
  - What is the financial performance of ABC?
  - What companies operate in the same space as ABC?
  - What standards are used to evaluate the industry?
- source_sentence: Research
  sentences:
  - What recent studies have been conducted on ABC?
  - What are the key factors considered in rating ABC?
  - How is the rating framework applied to the sector?
- source_sentence: Criteria
  sentences:
  - What are the projected economic impacts of inflation on the technology industry?
  - What is the process for assessing the creditworthiness of ABC?
  - What are the primary ESG challenges faced by ABC?
- source_sentence: Financials
  sentences:
  - Can you list the strengths and weaknesses of ABC?
  - What is understood by the term sovereign risk?
  - Can you provide the financial history of ABC?
- source_sentence: Research
  sentences:
  - What macroeconomic trends are influencing the credit ratings of the automotive
    industry?
  - Who are the main rivals of ABC?
  - Can you provide the latest research insights on ABC?
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
    - type: pearson_manhattan
      value: .nan
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: .nan
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: .nan
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: .nan
      name: Spearman Euclidean
    - type: pearson_dot
      value: .nan
      name: Pearson Dot
    - type: spearman_dot
      value: .nan
      name: Spearman Dot
    - type: pearson_max
      value: .nan
      name: Pearson Max
    - type: spearman_max
      value: .nan
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

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

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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("ManishThota/QueryRouter")
# Run inference
sentences = [
    'Research',
    'Can you provide the latest research insights on ABC?',
    'Who are the main rivals of ABC?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

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

| Metric              | Value   |
|:--------------------|:--------|
| pearson_cosine      | nan     |
| **spearman_cosine** | **nan** |
| pearson_manhattan   | nan     |
| spearman_manhattan  | nan     |
| pearson_euclidean   | nan     |
| spearman_euclidean  | nan     |
| pearson_dot         | nan     |
| spearman_dot        | nan     |
| pearson_max         | nan     |
| spearman_max        | nan     |

<!--
## 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: 724 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: 3 tokens</li><li>mean: 3.27 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 14.23 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1           | sentence2                                        | score            |
  |:--------------------|:-------------------------------------------------|:-----------------|
  | <code>Rating</code> | <code>What rating does XYZ have?</code>          | <code>1.0</code> |
  | <code>Rating</code> | <code>Can you provide the rating for XYZ?</code> | <code>1.0</code> |
  | <code>Rating</code> | <code>How is XYZ rated?</code>                   | <code>1.0</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: 60 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: 3 tokens</li><li>mean: 3.25 tokens</li><li>max: 4 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.48 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1           | sentence2                                        | score            |
  |:--------------------|:-------------------------------------------------|:-----------------|
  | <code>Rating</code> | <code>What is the current rating of ABC?</code>  | <code>1.0</code> |
  | <code>Rating</code> | <code>Can you tell me the rating for ABC?</code> | <code>1.0</code> |
  | <code>Rating</code> | <code>What rating has ABC been assigned?</code>  | <code>1.0</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
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `seed`: 33
- `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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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`: True
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 33
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch      | Step    | Training Loss | loss    | sts-dev_spearman_cosine |
|:----------:|:-------:|:-------------:|:-------:|:-----------------------:|
| 0.0220     | 2       | -             | 0.0     | nan                     |
| 0.0440     | 4       | -             | 0.0     | nan                     |
| 0.0659     | 6       | -             | 0.0     | nan                     |
| 0.0879     | 8       | -             | 0.0     | nan                     |
| 0.1099     | 10      | -             | 0.0     | nan                     |
| 0.1319     | 12      | -             | 0.0     | nan                     |
| 0.1538     | 14      | -             | 0.0     | nan                     |
| 0.1758     | 16      | -             | 0.0     | nan                     |
| 0.1978     | 18      | -             | 0.0     | nan                     |
| 0.2198     | 20      | -             | 0.0     | nan                     |
| 0.2418     | 22      | -             | 0.0     | nan                     |
| 0.2637     | 24      | -             | 0.0     | nan                     |
| 0.2857     | 26      | -             | 0.0     | nan                     |
| 0.3077     | 28      | -             | 0.0     | nan                     |
| 0.3297     | 30      | -             | 0.0     | nan                     |
| 0.3516     | 32      | -             | 0.0     | nan                     |
| 0.3736     | 34      | -             | 0.0     | nan                     |
| 0.3956     | 36      | -             | 0.0     | nan                     |
| 0.4176     | 38      | -             | 0.0     | nan                     |
| 0.4396     | 40      | -             | 0.0     | nan                     |
| 0.4615     | 42      | -             | 0.0     | nan                     |
| 0.4835     | 44      | -             | 0.0     | nan                     |
| 0.5055     | 46      | -             | 0.0     | nan                     |
| 0.5275     | 48      | -             | 0.0     | nan                     |
| 0.5495     | 50      | -             | 0.0     | nan                     |
| 0.5714     | 52      | -             | 0.0     | nan                     |
| 0.5934     | 54      | -             | 0.0     | nan                     |
| 0.6154     | 56      | -             | 0.0     | nan                     |
| 0.6374     | 58      | -             | 0.0     | nan                     |
| 0.6593     | 60      | -             | 0.0     | nan                     |
| 0.6813     | 62      | -             | 0.0     | nan                     |
| 0.7033     | 64      | -             | 0.0     | nan                     |
| 0.7253     | 66      | -             | 0.0     | nan                     |
| 0.7473     | 68      | -             | 0.0     | nan                     |
| 0.7692     | 70      | -             | 0.0     | nan                     |
| 0.7912     | 72      | -             | 0.0     | nan                     |
| 0.8132     | 74      | -             | 0.0     | nan                     |
| 0.8352     | 76      | -             | 0.0     | nan                     |
| 0.8571     | 78      | -             | 0.0     | nan                     |
| 0.8791     | 80      | -             | 0.0     | nan                     |
| 0.9011     | 82      | -             | 0.0     | nan                     |
| 0.9231     | 84      | -             | 0.0     | nan                     |
| 0.9451     | 86      | -             | 0.0     | nan                     |
| 0.9670     | 88      | -             | 0.0     | nan                     |
| 0.9890     | 90      | -             | 0.0     | nan                     |
| 1.0110     | 92      | -             | 0.0     | nan                     |
| 1.0330     | 94      | -             | 0.0     | nan                     |
| 1.0549     | 96      | -             | 0.0     | nan                     |
| 1.0769     | 98      | -             | 0.0     | nan                     |
| 1.0989     | 100     | -             | 0.0     | nan                     |
| 1.1209     | 102     | -             | 0.0     | nan                     |
| 1.1429     | 104     | -             | 0.0     | nan                     |
| 1.1648     | 106     | -             | 0.0     | nan                     |
| 1.1868     | 108     | -             | 0.0     | nan                     |
| 1.2088     | 110     | -             | 0.0     | nan                     |
| 1.2308     | 112     | -             | 0.0     | nan                     |
| 1.2527     | 114     | -             | 0.0     | nan                     |
| 1.2747     | 116     | -             | 0.0     | nan                     |
| 1.2967     | 118     | -             | 0.0     | nan                     |
| 1.3187     | 120     | -             | 0.0     | nan                     |
| 1.3407     | 122     | -             | 0.0     | nan                     |
| 1.3626     | 124     | -             | 0.0     | nan                     |
| 1.3846     | 126     | -             | 0.0     | nan                     |
| 1.4066     | 128     | -             | 0.0     | nan                     |
| 1.4286     | 130     | -             | 0.0     | nan                     |
| 1.4505     | 132     | -             | 0.0     | nan                     |
| 1.4725     | 134     | -             | 0.0     | nan                     |
| 1.4945     | 136     | -             | 0.0     | nan                     |
| 1.5165     | 138     | -             | 0.0     | nan                     |
| 1.5385     | 140     | -             | 0.0     | nan                     |
| 1.5604     | 142     | -             | 0.0     | nan                     |
| 1.5824     | 144     | -             | 0.0     | nan                     |
| 1.6044     | 146     | -             | 0.0     | nan                     |
| 1.6264     | 148     | -             | 0.0     | nan                     |
| 1.6484     | 150     | -             | 0.0     | nan                     |
| 1.6703     | 152     | -             | 0.0     | nan                     |
| 1.6923     | 154     | -             | 0.0     | nan                     |
| 1.7143     | 156     | -             | 0.0     | nan                     |
| 1.7363     | 158     | -             | 0.0     | nan                     |
| 1.7582     | 160     | -             | 0.0     | nan                     |
| 1.7802     | 162     | -             | 0.0     | nan                     |
| 1.8022     | 164     | -             | 0.0     | nan                     |
| 1.8242     | 166     | -             | 0.0     | nan                     |
| 1.8462     | 168     | -             | 0.0     | nan                     |
| 1.8681     | 170     | -             | 0.0     | nan                     |
| 1.8901     | 172     | -             | 0.0     | nan                     |
| 1.9121     | 174     | -             | 0.0     | nan                     |
| 1.9341     | 176     | -             | 0.0     | nan                     |
| 1.9560     | 178     | -             | 0.0     | nan                     |
| 1.9780     | 180     | -             | 0.0     | nan                     |
| 2.0        | 182     | -             | 0.0     | nan                     |
| 2.0220     | 184     | -             | 0.0     | nan                     |
| 2.0440     | 186     | -             | 0.0     | nan                     |
| 2.0659     | 188     | -             | 0.0     | nan                     |
| 2.0879     | 190     | -             | 0.0     | nan                     |
| 2.1099     | 192     | -             | 0.0     | nan                     |
| 2.1319     | 194     | -             | 0.0     | nan                     |
| 2.1538     | 196     | -             | 0.0     | nan                     |
| 2.1758     | 198     | -             | 0.0     | nan                     |
| 2.1978     | 200     | -             | 0.0     | nan                     |
| 2.2198     | 202     | -             | 0.0     | nan                     |
| 2.2418     | 204     | -             | 0.0     | nan                     |
| 2.2637     | 206     | -             | 0.0     | nan                     |
| 2.2857     | 208     | -             | 0.0     | nan                     |
| 2.3077     | 210     | -             | 0.0     | nan                     |
| 2.3297     | 212     | -             | 0.0     | nan                     |
| 2.3516     | 214     | -             | 0.0     | nan                     |
| 2.3736     | 216     | -             | 0.0     | nan                     |
| 2.3956     | 218     | -             | 0.0     | nan                     |
| 2.4176     | 220     | -             | 0.0     | nan                     |
| 2.4396     | 222     | -             | 0.0     | nan                     |
| 2.4615     | 224     | -             | 0.0     | nan                     |
| 2.4835     | 226     | -             | 0.0     | nan                     |
| 2.5055     | 228     | -             | 0.0     | nan                     |
| 2.5275     | 230     | -             | 0.0     | nan                     |
| 2.5495     | 232     | -             | 0.0     | nan                     |
| 2.5714     | 234     | -             | 0.0     | nan                     |
| 2.5934     | 236     | -             | 0.0     | nan                     |
| 2.6154     | 238     | -             | 0.0     | nan                     |
| 2.6374     | 240     | -             | 0.0     | nan                     |
| 2.6593     | 242     | -             | 0.0     | nan                     |
| 2.6813     | 244     | -             | 0.0     | nan                     |
| 2.7033     | 246     | -             | 0.0     | nan                     |
| 2.7253     | 248     | -             | 0.0     | nan                     |
| 2.7473     | 250     | -             | 0.0     | nan                     |
| 2.7692     | 252     | -             | 0.0     | nan                     |
| 2.7912     | 254     | -             | 0.0     | nan                     |
| 2.8132     | 256     | -             | 0.0     | nan                     |
| 2.8352     | 258     | -             | 0.0     | nan                     |
| 2.8571     | 260     | -             | 0.0     | nan                     |
| 2.8791     | 262     | -             | 0.0     | nan                     |
| 2.9011     | 264     | -             | 0.0     | nan                     |
| 2.9231     | 266     | -             | 0.0     | nan                     |
| 2.9451     | 268     | -             | 0.0     | nan                     |
| 2.9670     | 270     | -             | 0.0     | nan                     |
| 2.9890     | 272     | -             | 0.0     | nan                     |
| 3.0110     | 274     | -             | 0.0     | nan                     |
| 3.0330     | 276     | -             | 0.0     | nan                     |
| 3.0549     | 278     | -             | 0.0     | nan                     |
| 3.0769     | 280     | -             | 0.0     | nan                     |
| 3.0989     | 282     | -             | 0.0     | nan                     |
| 3.1209     | 284     | -             | 0.0     | nan                     |
| 3.1429     | 286     | -             | 0.0     | nan                     |
| 3.1648     | 288     | -             | 0.0     | nan                     |
| 3.1868     | 290     | -             | 0.0     | nan                     |
| 3.2088     | 292     | -             | 0.0     | nan                     |
| 3.2308     | 294     | -             | 0.0     | nan                     |
| 3.2527     | 296     | -             | 0.0     | nan                     |
| 3.2747     | 298     | -             | 0.0     | nan                     |
| 3.2967     | 300     | -             | 0.0     | nan                     |
| 3.3187     | 302     | -             | 0.0     | nan                     |
| 3.3407     | 304     | -             | 0.0     | nan                     |
| 3.3626     | 306     | -             | 0.0     | nan                     |
| 3.3846     | 308     | -             | 0.0     | nan                     |
| 3.4066     | 310     | -             | 0.0     | nan                     |
| 3.4286     | 312     | -             | 0.0     | nan                     |
| 3.4505     | 314     | -             | 0.0     | nan                     |
| 3.4725     | 316     | -             | 0.0     | nan                     |
| 3.4945     | 318     | -             | 0.0     | nan                     |
| 3.5165     | 320     | -             | 0.0     | nan                     |
| 3.5385     | 322     | -             | 0.0     | nan                     |
| 3.5604     | 324     | -             | 0.0     | nan                     |
| 3.5824     | 326     | -             | 0.0     | nan                     |
| 3.6044     | 328     | -             | 0.0     | nan                     |
| 3.6264     | 330     | -             | 0.0     | nan                     |
| 3.6484     | 332     | -             | 0.0     | nan                     |
| 3.6703     | 334     | -             | 0.0     | nan                     |
| 3.6923     | 336     | -             | 0.0     | nan                     |
| 3.7143     | 338     | -             | 0.0     | nan                     |
| 3.7363     | 340     | -             | 0.0     | nan                     |
| 3.7582     | 342     | -             | 0.0     | nan                     |
| 3.7802     | 344     | -             | 0.0     | nan                     |
| 3.8022     | 346     | -             | 0.0     | nan                     |
| 3.8242     | 348     | -             | 0.0     | nan                     |
| 3.8462     | 350     | -             | 0.0     | nan                     |
| 3.8681     | 352     | -             | 0.0     | nan                     |
| 3.8901     | 354     | -             | 0.0     | nan                     |
| 3.9121     | 356     | -             | 0.0     | nan                     |
| 3.9341     | 358     | -             | 0.0     | nan                     |
| 3.9560     | 360     | -             | 0.0     | nan                     |
| 3.9780     | 362     | -             | 0.0     | nan                     |
| 4.0        | 364     | -             | 0.0     | nan                     |
| 4.0220     | 366     | -             | 0.0     | nan                     |
| 4.0440     | 368     | -             | 0.0     | nan                     |
| 4.0659     | 370     | -             | 0.0     | nan                     |
| 4.0879     | 372     | -             | 0.0     | nan                     |
| 4.1099     | 374     | -             | 0.0     | nan                     |
| 4.1319     | 376     | -             | 0.0     | nan                     |
| 4.1538     | 378     | -             | 0.0     | nan                     |
| 4.1758     | 380     | -             | 0.0     | nan                     |
| 4.1978     | 382     | -             | 0.0     | nan                     |
| 4.2198     | 384     | -             | 0.0     | nan                     |
| 4.2418     | 386     | -             | 0.0     | nan                     |
| 4.2637     | 388     | -             | 0.0     | nan                     |
| 4.2857     | 390     | -             | 0.0     | nan                     |
| 4.3077     | 392     | -             | 0.0     | nan                     |
| 4.3297     | 394     | -             | 0.0     | nan                     |
| 4.3516     | 396     | -             | 0.0     | nan                     |
| 4.3736     | 398     | -             | 0.0     | nan                     |
| 4.3956     | 400     | -             | 0.0     | nan                     |
| 4.4176     | 402     | -             | 0.0     | nan                     |
| 4.4396     | 404     | -             | 0.0     | nan                     |
| 4.4615     | 406     | -             | 0.0     | nan                     |
| 4.4835     | 408     | -             | 0.0     | nan                     |
| 4.5055     | 410     | -             | 0.0     | nan                     |
| 4.5275     | 412     | -             | 0.0     | nan                     |
| 4.5495     | 414     | -             | 0.0     | nan                     |
| 4.5714     | 416     | -             | 0.0     | nan                     |
| 4.5934     | 418     | -             | 0.0     | nan                     |
| 4.6154     | 420     | -             | 0.0     | nan                     |
| 4.6374     | 422     | -             | 0.0     | nan                     |
| 4.6593     | 424     | -             | 0.0     | nan                     |
| 4.6813     | 426     | -             | 0.0     | nan                     |
| 4.7033     | 428     | -             | 0.0     | nan                     |
| 4.7253     | 430     | -             | 0.0     | nan                     |
| 4.7473     | 432     | -             | 0.0     | nan                     |
| 4.7692     | 434     | -             | 0.0     | nan                     |
| 4.7912     | 436     | -             | 0.0     | nan                     |
| 4.8132     | 438     | -             | 0.0     | nan                     |
| 4.8352     | 440     | -             | 0.0     | nan                     |
| 4.8571     | 442     | -             | 0.0     | nan                     |
| 4.8791     | 444     | -             | 0.0     | nan                     |
| 4.9011     | 446     | -             | 0.0     | nan                     |
| 4.9231     | 448     | -             | 0.0     | nan                     |
| 4.9451     | 450     | -             | 0.0     | nan                     |
| 4.9670     | 452     | -             | 0.0     | nan                     |
| 4.9890     | 454     | -             | 0.0     | nan                     |
| 5.0110     | 456     | -             | 0.0     | nan                     |
| 5.0330     | 458     | -             | 0.0     | nan                     |
| 5.0549     | 460     | -             | 0.0     | nan                     |
| 5.0769     | 462     | -             | 0.0     | nan                     |
| 5.0989     | 464     | -             | 0.0     | nan                     |
| 5.1209     | 466     | -             | 0.0     | nan                     |
| 5.1429     | 468     | -             | 0.0     | nan                     |
| 5.1648     | 470     | -             | 0.0     | nan                     |
| 5.1868     | 472     | -             | 0.0     | nan                     |
| 5.2088     | 474     | -             | 0.0     | nan                     |
| 5.2308     | 476     | -             | 0.0     | nan                     |
| 5.2527     | 478     | -             | 0.0     | nan                     |
| 5.2747     | 480     | -             | 0.0     | nan                     |
| 5.2967     | 482     | -             | 0.0     | nan                     |
| 5.3187     | 484     | -             | 0.0     | nan                     |
| 5.3407     | 486     | -             | 0.0     | nan                     |
| 5.3626     | 488     | -             | 0.0     | nan                     |
| 5.3846     | 490     | -             | 0.0     | nan                     |
| 5.4066     | 492     | -             | 0.0     | nan                     |
| 5.4286     | 494     | -             | 0.0     | nan                     |
| 5.4505     | 496     | -             | 0.0     | nan                     |
| 5.4725     | 498     | -             | 0.0     | nan                     |
| **5.4945** | **500** | **0.0**       | **0.0** | **nan**                 |
| 5.5165     | 502     | -             | 0.0     | nan                     |
| 5.5385     | 504     | -             | 0.0     | nan                     |
| 5.5604     | 506     | -             | 0.0     | nan                     |
| 5.5824     | 508     | -             | 0.0     | nan                     |
| 5.6044     | 510     | -             | 0.0     | nan                     |
| 5.6264     | 512     | -             | 0.0     | nan                     |
| 5.6484     | 514     | -             | 0.0     | nan                     |
| 5.6703     | 516     | -             | 0.0     | nan                     |
| 5.6923     | 518     | -             | 0.0     | nan                     |
| 5.7143     | 520     | -             | 0.0     | nan                     |
| 5.7363     | 522     | -             | 0.0     | nan                     |
| 5.7582     | 524     | -             | 0.0     | nan                     |
| 5.7802     | 526     | -             | 0.0     | nan                     |
| 5.8022     | 528     | -             | 0.0     | nan                     |
| 5.8242     | 530     | -             | 0.0     | nan                     |
| 5.8462     | 532     | -             | 0.0     | nan                     |
| 5.8681     | 534     | -             | 0.0     | nan                     |
| 5.8901     | 536     | -             | 0.0     | nan                     |
| 5.9121     | 538     | -             | 0.0     | nan                     |
| 5.9341     | 540     | -             | 0.0     | nan                     |
| 5.9560     | 542     | -             | 0.0     | nan                     |
| 5.9780     | 544     | -             | 0.0     | nan                     |
| 6.0        | 546     | -             | 0.0     | nan                     |
| 6.0220     | 548     | -             | 0.0     | nan                     |
| 6.0440     | 550     | -             | 0.0     | nan                     |
| 6.0659     | 552     | -             | 0.0     | nan                     |
| 6.0879     | 554     | -             | 0.0     | nan                     |
| 6.1099     | 556     | -             | 0.0     | nan                     |
| 6.1319     | 558     | -             | 0.0     | nan                     |
| 6.1538     | 560     | -             | 0.0     | nan                     |
| 6.1758     | 562     | -             | 0.0     | nan                     |
| 6.1978     | 564     | -             | 0.0     | nan                     |
| 6.2198     | 566     | -             | 0.0     | nan                     |
| 6.2418     | 568     | -             | 0.0     | nan                     |
| 6.2637     | 570     | -             | 0.0     | nan                     |
| 6.2857     | 572     | -             | 0.0     | nan                     |
| 6.3077     | 574     | -             | 0.0     | nan                     |
| 6.3297     | 576     | -             | 0.0     | nan                     |
| 6.3516     | 578     | -             | 0.0     | nan                     |
| 6.3736     | 580     | -             | 0.0     | nan                     |
| 6.3956     | 582     | -             | 0.0     | nan                     |
| 6.4176     | 584     | -             | 0.0     | nan                     |
| 6.4396     | 586     | -             | 0.0     | nan                     |
| 6.4615     | 588     | -             | 0.0     | nan                     |
| 6.4835     | 590     | -             | 0.0     | nan                     |
| 6.5055     | 592     | -             | 0.0     | nan                     |
| 6.5275     | 594     | -             | 0.0     | nan                     |
| 6.5495     | 596     | -             | 0.0     | nan                     |
| 6.5714     | 598     | -             | 0.0     | nan                     |
| 6.5934     | 600     | -             | 0.0     | nan                     |
| 6.6154     | 602     | -             | 0.0     | nan                     |
| 6.6374     | 604     | -             | 0.0     | nan                     |
| 6.6593     | 606     | -             | 0.0     | nan                     |
| 6.6813     | 608     | -             | 0.0     | nan                     |
| 6.7033     | 610     | -             | 0.0     | nan                     |
| 6.7253     | 612     | -             | 0.0     | nan                     |
| 6.7473     | 614     | -             | 0.0     | nan                     |
| 6.7692     | 616     | -             | 0.0     | nan                     |
| 6.7912     | 618     | -             | 0.0     | nan                     |
| 6.8132     | 620     | -             | 0.0     | nan                     |
| 6.8352     | 622     | -             | 0.0     | nan                     |
| 6.8571     | 624     | -             | 0.0     | nan                     |
| 6.8791     | 626     | -             | 0.0     | nan                     |
| 6.9011     | 628     | -             | 0.0     | nan                     |
| 6.9231     | 630     | -             | 0.0     | nan                     |
| 6.9451     | 632     | -             | 0.0     | nan                     |
| 6.9670     | 634     | -             | 0.0     | nan                     |
| 6.9890     | 636     | -             | 0.0     | nan                     |
| 7.0110     | 638     | -             | 0.0     | nan                     |
| 7.0330     | 640     | -             | 0.0     | nan                     |
| 7.0549     | 642     | -             | 0.0     | nan                     |
| 7.0769     | 644     | -             | 0.0     | nan                     |
| 7.0989     | 646     | -             | 0.0     | nan                     |
| 7.1209     | 648     | -             | 0.0     | nan                     |
| 7.1429     | 650     | -             | 0.0     | nan                     |
| 7.1648     | 652     | -             | 0.0     | nan                     |
| 7.1868     | 654     | -             | 0.0     | nan                     |
| 7.2088     | 656     | -             | 0.0     | nan                     |
| 7.2308     | 658     | -             | 0.0     | nan                     |
| 7.2527     | 660     | -             | 0.0     | nan                     |
| 7.2747     | 662     | -             | 0.0     | nan                     |
| 7.2967     | 664     | -             | 0.0     | nan                     |
| 7.3187     | 666     | -             | 0.0     | nan                     |
| 7.3407     | 668     | -             | 0.0     | nan                     |
| 7.3626     | 670     | -             | 0.0     | nan                     |
| 7.3846     | 672     | -             | 0.0     | nan                     |
| 7.4066     | 674     | -             | 0.0     | nan                     |
| 7.4286     | 676     | -             | 0.0     | nan                     |
| 7.4505     | 678     | -             | 0.0     | nan                     |
| 7.4725     | 680     | -             | 0.0     | nan                     |
| 7.4945     | 682     | -             | 0.0     | nan                     |
| 7.5165     | 684     | -             | 0.0     | nan                     |
| 7.5385     | 686     | -             | 0.0     | nan                     |
| 7.5604     | 688     | -             | 0.0     | nan                     |
| 7.5824     | 690     | -             | 0.0     | nan                     |
| 7.6044     | 692     | -             | 0.0     | nan                     |
| 7.6264     | 694     | -             | 0.0     | nan                     |
| 7.6484     | 696     | -             | 0.0     | nan                     |
| 7.6703     | 698     | -             | 0.0     | nan                     |
| 7.6923     | 700     | -             | 0.0     | nan                     |
| 7.7143     | 702     | -             | 0.0     | nan                     |
| 7.7363     | 704     | -             | 0.0     | nan                     |
| 7.7582     | 706     | -             | 0.0     | nan                     |
| 7.7802     | 708     | -             | 0.0     | nan                     |
| 7.8022     | 710     | -             | 0.0     | nan                     |
| 7.8242     | 712     | -             | 0.0     | nan                     |
| 7.8462     | 714     | -             | 0.0     | nan                     |
| 7.8681     | 716     | -             | 0.0     | nan                     |
| 7.8901     | 718     | -             | 0.0     | nan                     |
| 7.9121     | 720     | -             | 0.0     | nan                     |
| 7.9341     | 722     | -             | 0.0     | nan                     |
| 7.9560     | 724     | -             | 0.0     | nan                     |
| 7.9780     | 726     | -             | 0.0     | nan                     |
| 8.0        | 728     | -             | 0.0     | nan                     |
| 8.0220     | 730     | -             | 0.0     | nan                     |
| 8.0440     | 732     | -             | 0.0     | nan                     |
| 8.0659     | 734     | -             | 0.0     | nan                     |
| 8.0879     | 736     | -             | 0.0     | nan                     |
| 8.1099     | 738     | -             | 0.0     | nan                     |
| 8.1319     | 740     | -             | 0.0     | nan                     |
| 8.1538     | 742     | -             | 0.0     | nan                     |
| 8.1758     | 744     | -             | 0.0     | nan                     |
| 8.1978     | 746     | -             | 0.0     | nan                     |
| 8.2198     | 748     | -             | 0.0     | nan                     |
| 8.2418     | 750     | -             | 0.0     | nan                     |
| 8.2637     | 752     | -             | 0.0     | nan                     |
| 8.2857     | 754     | -             | 0.0     | nan                     |
| 8.3077     | 756     | -             | 0.0     | nan                     |
| 8.3297     | 758     | -             | 0.0     | nan                     |
| 8.3516     | 760     | -             | 0.0     | nan                     |
| 8.3736     | 762     | -             | 0.0     | nan                     |
| 8.3956     | 764     | -             | 0.0     | nan                     |
| 8.4176     | 766     | -             | 0.0     | nan                     |
| 8.4396     | 768     | -             | 0.0     | nan                     |
| 8.4615     | 770     | -             | 0.0     | nan                     |
| 8.4835     | 772     | -             | 0.0     | nan                     |
| 8.5055     | 774     | -             | 0.0     | nan                     |
| 8.5275     | 776     | -             | 0.0     | nan                     |
| 8.5495     | 778     | -             | 0.0     | nan                     |
| 8.5714     | 780     | -             | 0.0     | nan                     |
| 8.5934     | 782     | -             | 0.0     | nan                     |
| 8.6154     | 784     | -             | 0.0     | nan                     |
| 8.6374     | 786     | -             | 0.0     | nan                     |
| 8.6593     | 788     | -             | 0.0     | nan                     |
| 8.6813     | 790     | -             | 0.0     | nan                     |
| 8.7033     | 792     | -             | 0.0     | nan                     |
| 8.7253     | 794     | -             | 0.0     | nan                     |
| 8.7473     | 796     | -             | 0.0     | nan                     |
| 8.7692     | 798     | -             | 0.0     | nan                     |
| 8.7912     | 800     | -             | 0.0     | nan                     |
| 8.8132     | 802     | -             | 0.0     | nan                     |
| 8.8352     | 804     | -             | 0.0     | nan                     |
| 8.8571     | 806     | -             | 0.0     | nan                     |
| 8.8791     | 808     | -             | 0.0     | nan                     |
| 8.9011     | 810     | -             | 0.0     | nan                     |
| 8.9231     | 812     | -             | 0.0     | nan                     |
| 8.9451     | 814     | -             | 0.0     | nan                     |
| 8.9670     | 816     | -             | 0.0     | nan                     |
| 8.9890     | 818     | -             | 0.0     | nan                     |
| 9.0110     | 820     | -             | 0.0     | nan                     |
| 9.0330     | 822     | -             | 0.0     | nan                     |
| 9.0549     | 824     | -             | 0.0     | nan                     |
| 9.0769     | 826     | -             | 0.0     | nan                     |
| 9.0989     | 828     | -             | 0.0     | nan                     |
| 9.1209     | 830     | -             | 0.0     | nan                     |
| 9.1429     | 832     | -             | 0.0     | nan                     |
| 9.1648     | 834     | -             | 0.0     | nan                     |
| 9.1868     | 836     | -             | 0.0     | nan                     |
| 9.2088     | 838     | -             | 0.0     | nan                     |
| 9.2308     | 840     | -             | 0.0     | nan                     |
| 9.2527     | 842     | -             | 0.0     | nan                     |
| 9.2747     | 844     | -             | 0.0     | nan                     |
| 9.2967     | 846     | -             | 0.0     | nan                     |
| 9.3187     | 848     | -             | 0.0     | nan                     |
| 9.3407     | 850     | -             | 0.0     | nan                     |
| 9.3626     | 852     | -             | 0.0     | nan                     |
| 9.3846     | 854     | -             | 0.0     | nan                     |
| 9.4066     | 856     | -             | 0.0     | nan                     |
| 9.4286     | 858     | -             | 0.0     | nan                     |
| 9.4505     | 860     | -             | 0.0     | nan                     |
| 9.4725     | 862     | -             | 0.0     | nan                     |
| 9.4945     | 864     | -             | 0.0     | nan                     |
| 9.5165     | 866     | -             | 0.0     | nan                     |
| 9.5385     | 868     | -             | 0.0     | nan                     |
| 9.5604     | 870     | -             | 0.0     | nan                     |
| 9.5824     | 872     | -             | 0.0     | nan                     |
| 9.6044     | 874     | -             | 0.0     | nan                     |
| 9.6264     | 876     | -             | 0.0     | nan                     |
| 9.6484     | 878     | -             | 0.0     | nan                     |
| 9.6703     | 880     | -             | 0.0     | nan                     |
| 9.6923     | 882     | -             | 0.0     | nan                     |
| 9.7143     | 884     | -             | 0.0     | nan                     |
| 9.7363     | 886     | -             | 0.0     | nan                     |
| 9.7582     | 888     | -             | 0.0     | nan                     |
| 9.7802     | 890     | -             | 0.0     | nan                     |
| 9.8022     | 892     | -             | 0.0     | nan                     |
| 9.8242     | 894     | -             | 0.0     | nan                     |
| 9.8462     | 896     | -             | 0.0     | nan                     |
| 9.8681     | 898     | -             | 0.0     | nan                     |
| 9.8901     | 900     | -             | 0.0     | nan                     |
| 9.9121     | 902     | -             | 0.0     | nan                     |
| 9.9341     | 904     | -             | 0.0     | nan                     |
| 9.9560     | 906     | -             | 0.0     | nan                     |
| 9.9780     | 908     | -             | 0.0     | nan                     |
| 10.0       | 910     | -             | 0.0     | nan                     |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.0.1+cu118
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

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

#### 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},
}
```

<!--
## 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.*
-->