---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1M<n<10M
- loss:CoSENTLoss
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
base_model: distilbert/distilbert-base-uncased
widget:
- source_sentence: B C C_L CENTER TUNNEL VERT Other XXXX GENERIC G-S
  sentences:
  - T L ENG TO RAD SWITCH 90 Deg Front 2015 P552 VOLTS
  - T RCM ENS 071 RCM ENS EFPR VOLT 90 Deg Front 2021 CX430 VOLTS
  - T L ROCKER AT B PILLAR LONG 90 Deg Front 2020 V363N G-S
- source_sentence: T L F DUMMY PELVIS LAT 90 Deg Front 2021 P702 G-S
  sentences:
  - T L F DUMMY PELVIS LAT 90 Deg Front 2021 CX727 G-S
  - T FIXTURE BASE FRONT ACCEL VERT ACCEL Linear Test 2025 U717 G-S
  - T R ROCKER AT B_PILLAR LONG 30 Deg Front Angular Right 2025 CX430 G-S
- source_sentence: T L F DUMMY PELVIS LAT 90 Deg Front 2021 CX727 G-S
  sentences:
  - T R F DUMMY PELVIS LAT 90 Deg Front 2021 P702 G-S
  - T L F DUMMY PELVIS LONG 30 Deg Front Angular Left 2020 P558 G-S
  - T R F DUMMY L LOWER TIBIA MY LOAD 90 Deg Front 2022 U553 IN-LBS
- source_sentence: T R F DUMMY CHEST VERT 90 Deg Front 2021 P702 G-S
  sentences:
  - T R F DUMMY CHEST VERT 90 Deg Front 2015 P552 G-S
  - T L F DUMMY R LOWER TIBIA MX LOAD 90 Deg Front 2021 CX727 IN-LBS
  - T REAR DIFFERENTIAL LONG 30 Deg Front Angular Left 2020 P558 G-S
- source_sentence: T ENGINE TRANS TOP LAT 90 Deg Front 2025 U717 G-S
  sentences:
  - T R F ACTIVE VENT SQUIB VOLT 90 Deg Front 2021 P702 VOLTS
  - T ENGINE TRANS TOP LAT 30 Deg Front Angular Left 2020 P558 G-S
  - T R F DUMMY CHEST VERT 90 Deg Frontal Impact Simulation 2024 CX727 G-S
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-uncased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev
      type: sts-dev
    metrics:
    - type: pearson_cosine
      value: 0.4517523751963131
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4761555869182568
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.42531457338882206
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.46381946353811704
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.4261708588640235
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.4651666003446995
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3897944292190218
      name: Pearson Dot
    - type: spearman_dot
      value: 0.37404050621023377
      name: Spearman Dot
    - type: pearson_max
      value: 0.4517523751963131
      name: Pearson Max
    - type: spearman_max
      value: 0.4761555869182568
      name: Spearman Max
    - type: pearson_cosine
      value: 0.4412143708585779
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.4670631031564122
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.4156386809751022
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.4559676784726118
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.41671687323124873
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.45746069501329756
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.37528926047569405
      name: Pearson Dot
    - type: spearman_dot
      value: 0.36286227520562186
      name: Spearman Dot
    - type: pearson_max
      value: 0.4412143708585779
      name: Pearson Max
    - type: spearman_max
      value: 0.4670631031564122
      name: Spearman Max
---

# SentenceTransformer based on distilbert/distilbert-base-uncased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'T ENGINE TRANS TOP LAT 90 Deg Front 2025 U717 G-S',
    'T R F ACTIVE VENT SQUIB VOLT 90 Deg Front 2021 P702 VOLTS',
    'T ENGINE TRANS TOP LAT 30 Deg Front Angular Left 2020 P558 G-S',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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

</details>
-->

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

You can finetune this model on your own dataset.

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

</details>
-->

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

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

## Evaluation

### Metrics

#### 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      | 0.4518     |
| **spearman_cosine** | **0.4762** |
| pearson_manhattan   | 0.4253     |
| spearman_manhattan  | 0.4638     |
| pearson_euclidean   | 0.4262     |
| spearman_euclidean  | 0.4652     |
| pearson_dot         | 0.3898     |
| spearman_dot        | 0.374      |
| pearson_max         | 0.4518     |
| spearman_max        | 0.4762     |

#### 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      | 0.4412     |
| **spearman_cosine** | **0.4671** |
| pearson_manhattan   | 0.4156     |
| spearman_manhattan  | 0.456      |
| pearson_euclidean   | 0.4167     |
| spearman_euclidean  | 0.4575     |
| pearson_dot         | 0.3753     |
| spearman_dot        | 0.3629     |
| pearson_max         | 0.4412     |
| spearman_max        | 0.4671     |

<!--
## 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: 8,081,275 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: 23 tokens</li><li>mean: 31.48 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 30.06 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                 | sentence2                                                                                         | score                            |
  |:--------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code> | <code>T SCS R2 HY REF 059 R C PLR REF Y SM LAT 90 Deg / Left Side Decel-4g 2020 CX483 G-S</code>  | <code>0.21129386503072142</code> |
  | <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code> | <code>T R F DUMMY PELVIS VERT 75 Deg Oblique Right Side 10 in. Pole 2015 P552 G-S</code>          | <code>0.4972955033248179</code>  |
  | <code>T L F DUMMY PELVIS VERT Dynamic Seat Sled Test 2025 U718 G-S</code> | <code>T SCS L1 HY REF 053 L B PLR REF Y SM LAT 90 Deg Front Bumper Override 2021 CX727 G-S</code> | <code>0.5701051768787058</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: 1,726,581 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: 22 tokens</li><li>mean: 25.0 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 31.04 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                        | sentence2                                                                                         | score                            |
  |:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code> | <code>T L F DUMMY T12 LONG 27 Deg Crabbed Left Side NHTSA 214 MDB to vehicle 2015 P552 G-S</code> | <code>0.6835618484879796</code>  |
  | <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code> | <code>T L F DUMMY R FEMUR LONG 90 Deg Front 2022 U553 G-S</code>                                  | <code>0.666531064739</code>      |
  | <code>T R F ADAPTIVE TETHER VENT SQUIB VOLT 30 Deg Front Angular Right 20xx GENERIC VOLTS</code> | <code>T R F DUMMY NECK UPPER MZ LOAD 90 Deg Front 2019 P375ICA IN-LBS</code>                      | <code>0.46391834212079874</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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 3e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 3e-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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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
- `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`: 4
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `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_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: 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
- `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
- `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`: False
- `include_tokens_per_second`: False
- `neftune_noise_alpha`: None
- `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 |
|:------:|:------:|:-------------:|:------:|:-----------------------:|
| 0.0317 | 1000   | 6.3069        | -      | -                       |
| 0.0634 | 2000   | 6.1793        | -      | -                       |
| 0.0950 | 3000   | 6.1607        | -      | -                       |
| 0.1267 | 4000   | 6.1512        | -      | -                       |
| 0.1584 | 5000   | 6.1456        | -      | -                       |
| 0.1901 | 6000   | 6.1419        | -      | -                       |
| 0.2218 | 7000   | 6.1398        | -      | -                       |
| 0.2534 | 8000   | 6.1377        | -      | -                       |
| 0.2851 | 9000   | 6.1352        | -      | -                       |
| 0.3168 | 10000  | 6.1338        | -      | -                       |
| 0.3485 | 11000  | 6.1332        | -      | -                       |
| 0.3801 | 12000  | 6.1309        | -      | -                       |
| 0.4118 | 13000  | 6.1315        | -      | -                       |
| 0.4435 | 14000  | 6.1283        | -      | -                       |
| 0.4752 | 15000  | 6.129         | -      | -                       |
| 0.5069 | 16000  | 6.1271        | -      | -                       |
| 0.5385 | 17000  | 6.1265        | -      | -                       |
| 0.5702 | 18000  | 6.1238        | -      | -                       |
| 0.6019 | 19000  | 6.1234        | -      | -                       |
| 0.6336 | 20000  | 6.1225        | -      | -                       |
| 0.6653 | 21000  | 6.1216        | -      | -                       |
| 0.6969 | 22000  | 6.1196        | -      | -                       |
| 0.7286 | 23000  | 6.1198        | -      | -                       |
| 0.7603 | 24000  | 6.1178        | -      | -                       |
| 0.7920 | 25000  | 6.117         | -      | -                       |
| 0.8236 | 26000  | 6.1167        | -      | -                       |
| 0.8553 | 27000  | 6.1165        | -      | -                       |
| 0.8870 | 28000  | 6.1149        | -      | -                       |
| 0.9187 | 29000  | 6.1146        | -      | -                       |
| 0.9504 | 30000  | 6.113         | -      | -                       |
| 0.9820 | 31000  | 6.1143        | -      | -                       |
| 1.0    | 31567  | -             | 6.1150 | 0.4829                  |
| 1.0137 | 32000  | 6.1115        | -      | -                       |
| 1.0454 | 33000  | 6.111         | -      | -                       |
| 1.0771 | 34000  | 6.1091        | -      | -                       |
| 1.1088 | 35000  | 6.1094        | -      | -                       |
| 1.1404 | 36000  | 6.1078        | -      | -                       |
| 1.1721 | 37000  | 6.1095        | -      | -                       |
| 1.2038 | 38000  | 6.106         | -      | -                       |
| 1.2355 | 39000  | 6.1071        | -      | -                       |
| 1.2671 | 40000  | 6.1073        | -      | -                       |
| 1.2988 | 41000  | 6.1064        | -      | -                       |
| 1.3305 | 42000  | 6.1047        | -      | -                       |
| 1.3622 | 43000  | 6.1054        | -      | -                       |
| 1.3939 | 44000  | 6.1048        | -      | -                       |
| 1.4255 | 45000  | 6.1053        | -      | -                       |
| 1.4572 | 46000  | 6.1058        | -      | -                       |
| 1.4889 | 47000  | 6.1037        | -      | -                       |
| 1.5206 | 48000  | 6.1041        | -      | -                       |
| 1.5523 | 49000  | 6.1023        | -      | -                       |
| 1.5839 | 50000  | 6.1018        | -      | -                       |
| 1.6156 | 51000  | 6.104         | -      | -                       |
| 1.6473 | 52000  | 6.1004        | -      | -                       |
| 1.6790 | 53000  | 6.1027        | -      | -                       |
| 1.7106 | 54000  | 6.1017        | -      | -                       |
| 1.7423 | 55000  | 6.1011        | -      | -                       |
| 1.7740 | 56000  | 6.1002        | -      | -                       |
| 1.8057 | 57000  | 6.0994        | -      | -                       |
| 1.8374 | 58000  | 6.0985        | -      | -                       |
| 1.8690 | 59000  | 6.0986        | -      | -                       |
| 1.9007 | 60000  | 6.1006        | -      | -                       |
| 1.9324 | 61000  | 6.0983        | -      | -                       |
| 1.9641 | 62000  | 6.0983        | -      | -                       |
| 1.9958 | 63000  | 6.0973        | -      | -                       |
| 2.0    | 63134  | -             | 6.1193 | 0.4828                  |
| 2.0274 | 64000  | 6.0943        | -      | -                       |
| 2.0591 | 65000  | 6.0941        | -      | -                       |
| 2.0908 | 66000  | 6.0936        | -      | -                       |
| 2.1225 | 67000  | 6.0909        | -      | -                       |
| 2.1541 | 68000  | 6.0925        | -      | -                       |
| 2.1858 | 69000  | 6.0932        | -      | -                       |
| 2.2175 | 70000  | 6.0939        | -      | -                       |
| 2.2492 | 71000  | 6.0919        | -      | -                       |
| 2.2809 | 72000  | 6.0932        | -      | -                       |
| 2.3125 | 73000  | 6.0916        | -      | -                       |
| 2.3442 | 74000  | 6.0919        | -      | -                       |
| 2.3759 | 75000  | 6.0919        | -      | -                       |
| 2.4076 | 76000  | 6.0911        | -      | -                       |
| 2.4393 | 77000  | 6.0924        | -      | -                       |
| 2.4709 | 78000  | 6.0911        | -      | -                       |
| 2.5026 | 79000  | 6.0922        | -      | -                       |
| 2.5343 | 80000  | 6.0926        | -      | -                       |
| 2.5660 | 81000  | 6.0911        | -      | -                       |
| 2.5976 | 82000  | 6.0897        | -      | -                       |
| 2.6293 | 83000  | 6.0922        | -      | -                       |
| 2.6610 | 84000  | 6.0908        | -      | -                       |
| 2.6927 | 85000  | 6.0884        | -      | -                       |
| 2.7244 | 86000  | 6.0907        | -      | -                       |
| 2.7560 | 87000  | 6.0904        | -      | -                       |
| 2.7877 | 88000  | 6.0881        | -      | -                       |
| 2.8194 | 89000  | 6.0902        | -      | -                       |
| 2.8511 | 90000  | 6.088         | -      | -                       |
| 2.8828 | 91000  | 6.0888        | -      | -                       |
| 2.9144 | 92000  | 6.0884        | -      | -                       |
| 2.9461 | 93000  | 6.0881        | -      | -                       |
| 2.9778 | 94000  | 6.0896        | -      | -                       |
| 3.0    | 94701  | -             | 6.1225 | 0.4788                  |
| 3.0095 | 95000  | 6.0857        | -      | -                       |
| 3.0412 | 96000  | 6.0838        | -      | -                       |
| 3.0728 | 97000  | 6.0843        | -      | -                       |
| 3.1045 | 98000  | 6.0865        | -      | -                       |
| 3.1362 | 99000  | 6.0827        | -      | -                       |
| 3.1679 | 100000 | 6.0836        | -      | -                       |
| 3.1995 | 101000 | 6.0837        | -      | -                       |
| 3.2312 | 102000 | 6.0836        | -      | -                       |
| 3.2629 | 103000 | 6.0837        | -      | -                       |
| 3.2946 | 104000 | 6.084         | -      | -                       |
| 3.3263 | 105000 | 6.0836        | -      | -                       |
| 3.3579 | 106000 | 6.0808        | -      | -                       |
| 3.3896 | 107000 | 6.0821        | -      | -                       |
| 3.4213 | 108000 | 6.0817        | -      | -                       |
| 3.4530 | 109000 | 6.082         | -      | -                       |
| 3.4847 | 110000 | 6.083         | -      | -                       |
| 3.5163 | 111000 | 6.0829        | -      | -                       |
| 3.5480 | 112000 | 6.0832        | -      | -                       |
| 3.5797 | 113000 | 6.0829        | -      | -                       |
| 3.6114 | 114000 | 6.0837        | -      | -                       |
| 3.6430 | 115000 | 6.082         | -      | -                       |
| 3.6747 | 116000 | 6.0823        | -      | -                       |
| 3.7064 | 117000 | 6.082         | -      | -                       |
| 3.7381 | 118000 | 6.0833        | -      | -                       |
| 3.7698 | 119000 | 6.0831        | -      | -                       |
| 3.8014 | 120000 | 6.0814        | -      | -                       |
| 3.8331 | 121000 | 6.0813        | -      | -                       |
| 3.8648 | 122000 | 6.0797        | -      | -                       |
| 3.8965 | 123000 | 6.0793        | -      | -                       |
| 3.9282 | 124000 | 6.0818        | -      | -                       |
| 3.9598 | 125000 | 6.0806        | -      | -                       |
| 3.9915 | 126000 | 6.08          | -      | -                       |
| 4.0    | 126268 | -             | 6.1266 | 0.4671                  |

</details>

### Framework Versions
- Python: 3.10.6
- Sentence Transformers: 3.0.0
- Transformers: 4.35.0
- PyTorch: 2.1.0a0+4136153
- Accelerate: 0.30.1
- Datasets: 2.14.1
- Tokenizers: 0.14.1

## 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},
}
```

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