---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets:
- youssefkhalil320/pairs_three_scores_v5
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80000003
- loss:CoSENTLoss
widget:
- source_sentence: durable pvc swim ring
  sentences:
  - flaky croissant
  - urban shoes
  - warm drinks mug
- source_sentence: iso mak retard capsules
  sentences:
  - savory baguette
  - shea butter body cream
  - softwheeled cruiser
- source_sentence: love sandra potty
  sentences:
  - utensil holder
  - olive pants
  - headwear
- source_sentence: dusky hair brush
  sentences:
  - back compartment laptop
  - rubber feet platter
  - honed blade knife
- source_sentence: nkd skn
  sentences:
  - fruit fragrances nail polish remover
  - panini salmon
  - hand drawing bag
---

# all-MiniLM-L6-v8-pair_score

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) on the [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) dataset. 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 c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5)
- **Language:** en
- **License:** apache-2.0

### Model Sources

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

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'nkd skn',
    'hand drawing bag',
    'panini salmon',
]
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.*
-->

<!--
## 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

#### pairs_three_scores_v5

* Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
* Size: 80,000,003 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: 6.06 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.71 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                            | sentence2                              | score            |
  |:-------------------------------------|:---------------------------------------|:-----------------|
  | <code>vanilla hair cream</code>      | <code>free of paraben hair mask</code> | <code>0.5</code> |
  | <code>nourishing shampoo</code>      | <code>cumin lemon tea</code>           | <code>0.0</code> |
  | <code>safe materials pacifier</code> | <code>facial serum</code>              | <code>0.5</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

#### pairs_three_scores_v5

* Dataset: [pairs_three_scores_v5](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5) at [3d8c457](https://huggingface.co/datasets/youssefkhalil320/pairs_three_scores_v5/tree/3d8c45703846bd2adfaaf422abafbc389b283de1)
* Size: 20,000,001 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: 6.21 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.75 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.11</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                               | sentence2                          | score            |
  |:----------------------------------------|:-----------------------------------|:-----------------|
  | <code>teddy bear toy</code>             | <code>long lasting cat food</code> | <code>0.0</code> |
  | <code>eva hair treatment</code>         | <code>fresh pineapple</code>       | <code>0.0</code> |
  | <code>soft wave hair conditioner</code> | <code>hybrid seat bike</code>      | <code>0.0</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
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: 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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0002 | 100   | 10.8792       |
| 0.0003 | 200   | 10.9284       |
| 0.0005 | 300   | 10.6466       |
| 0.0006 | 400   | 10.841        |
| 0.0008 | 500   | 10.8094       |
| 0.0010 | 600   | 10.4323       |
| 0.0011 | 700   | 10.3032       |
| 0.0013 | 800   | 10.4006       |
| 0.0014 | 900   | 10.4743       |
| 0.0016 | 1000  | 10.2334       |
| 0.0018 | 1100  | 10.0135       |
| 0.0019 | 1200  | 9.7874        |
| 0.0021 | 1300  | 9.7419        |
| 0.0022 | 1400  | 9.7412        |
| 0.0024 | 1500  | 9.4585        |
| 0.0026 | 1600  | 9.5339        |
| 0.0027 | 1700  | 9.4345        |
| 0.0029 | 1800  | 9.1733        |
| 0.0030 | 1900  | 8.9952        |
| 0.0032 | 2000  | 8.9669        |
| 0.0034 | 2100  | 8.8152        |
| 0.0035 | 2200  | 8.7936        |
| 0.0037 | 2300  | 8.6771        |
| 0.0038 | 2400  | 8.4648        |
| 0.0040 | 2500  | 8.5764        |
| 0.0042 | 2600  | 8.4587        |
| 0.0043 | 2700  | 8.2966        |
| 0.0045 | 2800  | 8.2329        |
| 0.0046 | 2900  | 8.1415        |
| 0.0048 | 3000  | 8.0404        |
| 0.0050 | 3100  | 7.9698        |
| 0.0051 | 3200  | 7.9205        |
| 0.0053 | 3300  | 7.8314        |
| 0.0054 | 3400  | 7.8369        |
| 0.0056 | 3500  | 7.6403        |
| 0.0058 | 3600  | 7.5842        |
| 0.0059 | 3700  | 7.5812        |
| 0.0061 | 3800  | 7.4335        |
| 0.0062 | 3900  | 7.4917        |
| 0.0064 | 4000  | 7.3204        |
| 0.0066 | 4100  | 7.2971        |
| 0.0067 | 4200  | 7.2233        |
| 0.0069 | 4300  | 7.2081        |
| 0.0070 | 4400  | 7.1364        |
| 0.0072 | 4500  | 7.0663        |
| 0.0074 | 4600  | 6.9601        |
| 0.0075 | 4700  | 6.9546        |
| 0.0077 | 4800  | 6.9019        |
| 0.0078 | 4900  | 6.8801        |
| 0.0080 | 5000  | 6.7734        |
| 0.0082 | 5100  | 6.7648        |
| 0.0083 | 5200  | 6.7498        |
| 0.0085 | 5300  | 6.6872        |
| 0.0086 | 5400  | 6.6264        |
| 0.0088 | 5500  | 6.579         |
| 0.0090 | 5600  | 6.6001        |
| 0.0091 | 5700  | 6.5971        |
| 0.0093 | 5800  | 6.4694        |
| 0.0094 | 5900  | 6.3983        |
| 0.0096 | 6000  | 6.4477        |
| 0.0098 | 6100  | 6.4308        |
| 0.0099 | 6200  | 6.4248        |
| 0.0101 | 6300  | 6.2642        |
| 0.0102 | 6400  | 6.2763        |
| 0.0104 | 6500  | 6.3878        |
| 0.0106 | 6600  | 6.2601        |
| 0.0107 | 6700  | 6.1789        |
| 0.0109 | 6800  | 6.1773        |
| 0.0110 | 6900  | 6.1439        |
| 0.0112 | 7000  | 6.1863        |
| 0.0114 | 7100  | 6.0513        |
| 0.0115 | 7200  | 6.0671        |
| 0.0117 | 7300  | 6.0212        |
| 0.0118 | 7400  | 6.0043        |
| 0.0120 | 7500  | 6.0166        |
| 0.0122 | 7600  | 5.9754        |
| 0.0123 | 7700  | 5.9211        |
| 0.0125 | 7800  | 5.7867        |
| 0.0126 | 7900  | 5.8534        |
| 0.0128 | 8000  | 5.7708        |
| 0.0130 | 8100  | 5.8328        |
| 0.0131 | 8200  | 5.7417        |
| 0.0133 | 8300  | 5.8097        |
| 0.0134 | 8400  | 5.7578        |
| 0.0136 | 8500  | 5.643         |
| 0.0138 | 8600  | 5.6401        |
| 0.0139 | 8700  | 5.6627        |
| 0.0141 | 8800  | 5.6167        |
| 0.0142 | 8900  | 5.6539        |
| 0.0144 | 9000  | 5.4513        |
| 0.0146 | 9100  | 5.4132        |
| 0.0147 | 9200  | 5.4714        |
| 0.0149 | 9300  | 5.4786        |
| 0.0150 | 9400  | 5.3928        |
| 0.0152 | 9500  | 5.4774        |
| 0.0154 | 9600  | 5.2881        |
| 0.0155 | 9700  | 5.3699        |
| 0.0157 | 9800  | 5.1483        |
| 0.0158 | 9900  | 5.3051        |
| 0.0160 | 10000 | 5.2546        |
| 0.0162 | 10100 | 5.2314        |
| 0.0163 | 10200 | 5.1783        |
| 0.0165 | 10300 | 5.2074        |
| 0.0166 | 10400 | 5.2825        |
| 0.0168 | 10500 | 5.1715        |
| 0.0170 | 10600 | 5.087         |
| 0.0171 | 10700 | 5.082         |
| 0.0173 | 10800 | 4.9111        |
| 0.0174 | 10900 | 5.0213        |
| 0.0176 | 11000 | 4.9898        |
| 0.0178 | 11100 | 4.7734        |
| 0.0179 | 11200 | 4.9511        |
| 0.0181 | 11300 | 5.0481        |
| 0.0182 | 11400 | 4.8441        |
| 0.0184 | 11500 | 4.873         |
| 0.0186 | 11600 | 4.9988        |
| 0.0187 | 11700 | 4.7653        |
| 0.0189 | 11800 | 4.804         |
| 0.0190 | 11900 | 4.8288        |
| 0.0192 | 12000 | 4.7053        |
| 0.0194 | 12100 | 4.6887        |
| 0.0195 | 12200 | 4.7832        |
| 0.0197 | 12300 | 4.6817        |
| 0.0198 | 12400 | 4.6252        |
| 0.0200 | 12500 | 4.5936        |
| 0.0202 | 12600 | 4.7452        |
| 0.0203 | 12700 | 4.5321        |
| 0.0205 | 12800 | 4.4964        |
| 0.0206 | 12900 | 4.4421        |
| 0.0208 | 13000 | 4.3782        |
| 0.0210 | 13100 | 4.5169        |
| 0.0211 | 13200 | 4.533         |
| 0.0213 | 13300 | 4.3725        |
| 0.0214 | 13400 | 4.2911        |
| 0.0216 | 13500 | 4.2261        |
| 0.0218 | 13600 | 4.2467        |
| 0.0219 | 13700 | 4.1558        |
| 0.0221 | 13800 | 4.2794        |
| 0.0222 | 13900 | 4.2383        |
| 0.0224 | 14000 | 4.1654        |
| 0.0226 | 14100 | 4.158         |
| 0.0227 | 14200 | 4.1299        |
| 0.0229 | 14300 | 4.1902        |
| 0.0230 | 14400 | 3.7853        |
| 0.0232 | 14500 | 4.0514        |
| 0.0234 | 14600 | 4.1655        |
| 0.0235 | 14700 | 4.051         |
| 0.0237 | 14800 | 4.078         |
| 0.0238 | 14900 | 4.1193        |
| 0.0240 | 15000 | 4.1536        |
| 0.0242 | 15100 | 3.935         |
| 0.0243 | 15200 | 3.9535        |
| 0.0245 | 15300 | 3.7051        |
| 0.0246 | 15400 | 3.8329        |
| 0.0248 | 15500 | 3.9412        |
| 0.0250 | 15600 | 3.6668        |
| 0.0251 | 15700 | 3.7758        |
| 0.0253 | 15800 | 3.8805        |
| 0.0254 | 15900 | 3.8848        |
| 0.0256 | 16000 | 3.75          |
| 0.0258 | 16100 | 3.5685        |
| 0.0259 | 16200 | 3.7016        |
| 0.0261 | 16300 | 4.0955        |
| 0.0262 | 16400 | 3.7577        |
| 0.0264 | 16500 | 3.7485        |
| 0.0266 | 16600 | 3.8263        |
| 0.0267 | 16700 | 3.6922        |
| 0.0269 | 16800 | 3.6568        |
| 0.0270 | 16900 | 3.7317        |
| 0.0272 | 17000 | 3.5089        |
| 0.0274 | 17100 | 3.7377        |
| 0.0275 | 17200 | 3.6206        |
| 0.0277 | 17300 | 3.3702        |
| 0.0278 | 17400 | 3.5126        |
| 0.0280 | 17500 | 3.4841        |
| 0.0282 | 17600 | 3.1464        |
| 0.0283 | 17700 | 3.7012        |
| 0.0285 | 17800 | 3.5802        |
| 0.0286 | 17900 | 3.4952        |
| 0.0288 | 18000 | 3.1174        |
| 0.0290 | 18100 | 3.3134        |
| 0.0291 | 18200 | 3.3578        |
| 0.0293 | 18300 | 3.0209        |
| 0.0294 | 18400 | 3.3796        |
| 0.0296 | 18500 | 3.2287        |
| 0.0298 | 18600 | 3.1537        |
| 0.0299 | 18700 | 2.9073        |
| 0.0301 | 18800 | 3.3444        |
| 0.0302 | 18900 | 3.1341        |
| 0.0304 | 19000 | 2.8862        |
| 0.0306 | 19100 | 3.2033        |
| 0.0307 | 19200 | 3.2764        |
| 0.0309 | 19300 | 3.0725        |
| 0.0310 | 19400 | 3.0436        |
| 0.0312 | 19500 | 3.3493        |
| 0.0314 | 19600 | 3.0141        |
| 0.0315 | 19700 | 2.779         |
| 0.0317 | 19800 | 3.3543        |
| 0.0318 | 19900 | 3.1526        |
| 0.0320 | 20000 | 2.7896        |
| 0.0322 | 20100 | 2.9398        |
| 0.0323 | 20200 | 3.1254        |
| 0.0325 | 20300 | 2.8832        |
| 0.0326 | 20400 | 3.0542        |
| 0.0328 | 20500 | 2.9722        |
| 0.0330 | 20600 | 2.9321        |
| 0.0331 | 20700 | 2.6448        |
| 0.0333 | 20800 | 3.4006        |
| 0.0334 | 20900 | 3.0022        |
| 0.0336 | 21000 | 2.6366        |
| 0.0338 | 21100 | 3.0112        |
| 0.0339 | 21200 | 2.7856        |
| 0.0341 | 21300 | 3.0967        |
| 0.0342 | 21400 | 2.8754        |
| 0.0344 | 21500 | 3.1269        |
| 0.0346 | 21600 | 2.8235        |
| 0.0347 | 21700 | 2.4912        |
| 0.0349 | 21800 | 2.5079        |
| 0.0350 | 21900 | 3.2942        |
| 0.0352 | 22000 | 2.4184        |
| 0.0354 | 22100 | 2.782         |
| 0.0355 | 22200 | 2.7652        |
| 0.0357 | 22300 | 3.113         |
| 0.0358 | 22400 | 2.7451        |
| 0.0360 | 22500 | 2.7473        |
| 0.0362 | 22600 | 2.5116        |
| 0.0363 | 22700 | 2.8531        |
| 0.0365 | 22800 | 2.9171        |
| 0.0366 | 22900 | 2.7954        |
| 0.0368 | 23000 | 2.5376        |
| 0.0370 | 23100 | 3.2488        |
| 0.0371 | 23200 | 2.6131        |
| 0.0373 | 23300 | 3.1343        |
| 0.0374 | 23400 | 2.3159        |
| 0.0376 | 23500 | 2.4225        |
| 0.0378 | 23600 | 2.5034        |
| 0.0379 | 23700 | 3.0067        |
| 0.0381 | 23800 | 2.313         |
| 0.0382 | 23900 | 2.5363        |
| 0.0384 | 24000 | 2.7929        |
| 0.0386 | 24100 | 2.617         |
| 0.0387 | 24200 | 2.9711        |
| 0.0389 | 24300 | 2.7726        |
| 0.0390 | 24400 | 2.5849        |
| 0.0392 | 24500 | 2.3231        |
| 0.0394 | 24600 | 2.2477        |
| 0.0395 | 24700 | 2.5487        |
| 0.0397 | 24800 | 2.5175        |
| 0.0398 | 24900 | 2.6758        |
| 0.0400 | 25000 | 2.7313        |
| 0.0402 | 25100 | 2.4846        |
| 0.0403 | 25200 | 2.8697        |
| 0.0405 | 25300 | 2.5289        |
| 0.0406 | 25400 | 2.235         |
| 0.0408 | 25500 | 2.5028        |
| 0.0410 | 25600 | 2.6295        |
| 0.0411 | 25700 | 2.6159        |
| 0.0413 | 25800 | 2.4447        |
| 0.0414 | 25900 | 2.7233        |
| 0.0416 | 26000 | 2.5651        |
| 0.0418 | 26100 | 2.1317        |
| 0.0419 | 26200 | 2.6157        |
| 0.0421 | 26300 | 2.7385        |
| 0.0422 | 26400 | 2.4642        |
| 0.0424 | 26500 | 2.0621        |

</details>

### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.4.1+cu118
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.3

## Citation

### BibTeX

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

#### 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.*
-->