---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- 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: T L 2 DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S
  sentences:
  - T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020.5 U625 G-S
  - T L F DUMMY HEAD CG LAT WIDEBAND Static Airbag OOP Test 2025 CX430 G-S
  - T R F DUMMY PELVIS LAT WIDEBAND 90 Deg Frontal Impact Simulation 2026 P800 G-S
- source_sentence: T L F DUMMY CHEST LONG WIDEBAND 90 Deg Front 2022 U553 G-S
  sentences:
  - T R F TORSO BELT AT D RING LOAD WIDEBAND 90 Deg Front 2022 U553 LBF
  - T L F DUMMY L UP TIBIA MY LOAD WIDEBAND 90 Deg Front 2015 P552 IN-LBS
  - T R F DUMMY R UP TIBIA FX LOAD WIDEBAND 30 Deg Front Angular Left 2022 U554 LBF
- source_sentence: T R F DUMMY PELVIS LAT WIDEBAND 90 Deg Front 2019 D544 G-S
  sentences:
  - T L F DUMMY PELVIS LAT WIDEBAND 90 Deg Front 2015 P552 G-S
  - T L LOWER CONTROL ARM VERT WIDEBAND Left Side Drop Test 2024.5 P702 G-S
  - F BARRIER PLATE 11030 SZ D FX LOAD WIDEBAND 90 Deg Front 2015 P552 LBF
- source_sentence: T ENGINE ENGINE TOP LAT WIDEBAND 90 Deg Front 2015 P552 G-S
  sentences:
  - T R ENGINE TRANS BOTTOM LAT WIDEBAND 90 Deg Front 2015 P552 G-S
  - F BARRIER PLATE 09030 SZ D FX LOAD WIDEBAND 90 Deg Front 2015 P552 LBF
  - T R F DUMMY NECK UPPER MX LOAD WIDEBAND 90 Deg Front 2022 U554 IN-LBS
- source_sentence: T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S
  sentences:
  - T R F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2025 V363N G-S
  - T R F DUMMY HEAD CG VERT WIDEBAND VIA Linear Impact Test 2021 C727 G-S
  - T L F DUMMY T1 VERT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2026 P800 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: 0.27051173706186693
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.2798593637893599
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.228702027931258
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.25353345676390787
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.23018017587211453
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.2550481010151111
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.2125353301405465
      name: Pearson Dot
    - type: spearman_dot
      value: 0.1902748420981738
      name: Spearman Dot
    - type: pearson_max
      value: 0.27051173706186693
      name: Pearson Max
    - type: spearman_max
      value: 0.2798593637893599
      name: Spearman Max
    - type: pearson_cosine
      value: 0.26319176781258086
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.2721909587247752
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.21766215319708615
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.2439514548051345
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.2195389492634635
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.24629153092425862
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.21073878591545503
      name: Pearson Dot
    - type: spearman_dot
      value: 0.1864889259868287
      name: Spearman Dot
    - type: pearson_max
      value: 0.26319176781258086
      name: Pearson Max
    - type: spearman_max
      value: 0.2721909587247752
      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 L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S',
    'T R F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2025 V363N G-S',
    'T R F DUMMY HEAD CG VERT WIDEBAND VIA Linear Impact Test 2021 C727 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]
```

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## 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      | 0.2705     |
| **spearman_cosine** | **0.2799** |
| pearson_manhattan   | 0.2287     |
| spearman_manhattan  | 0.2535     |
| pearson_euclidean   | 0.2302     |
| spearman_euclidean  | 0.255      |
| pearson_dot         | 0.2125     |
| spearman_dot        | 0.1903     |
| pearson_max         | 0.2705     |
| spearman_max        | 0.2799     |

#### 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      | 0.2632     |
| **spearman_cosine** | **0.2722** |
| pearson_manhattan   | 0.2177     |
| spearman_manhattan  | 0.244      |
| pearson_euclidean   | 0.2195     |
| spearman_euclidean  | 0.2463     |
| pearson_dot         | 0.2107     |
| spearman_dot        | 0.1865     |
| pearson_max         | 0.2632     |
| spearman_max        | 0.2722     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 481,114 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: 16 tokens</li><li>mean: 32.14 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.62 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                   | sentence2                                                                                                         | score                           |
  |:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:--------------------------------|
  | <code>T L C PLR SM SCS L2 HY REF 053 LAT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2018 P558 G-S</code> | <code>T PCM PWR POWER TO PCM VOLT 2 SEC WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2020 V363N VOLTS</code>     | <code>0.5198143220305642</code> |
  | <code>T L F DUMMY L_FEMUR MX LOAD WIDEBAND 90 Deg Frontal Impact Simulation MY2025 U717 IN-LBS</code>       | <code>B L FRAME AT No 1 X MEM LAT WIDEBAND Inline 25% Left Front Offset Vehicle to Vehicle 2021 P702 G-S</code>   | <code>0.5214072221695696</code> |
  | <code>T R F DOOR REAR OF SEAT H PT LAT WIDEBAND 75 Deg Oblique Right Side 10 in. Pole 2015 P552 G-S</code>  | <code>T SCS R2 HY BOS A12 008 TAP RIGHT C PILLAR VOLT WIDEBAND 30 Deg Front Angular Right 2021 CX727 VOLTS</code> | <code>0.322173496575591</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: 103,097 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: 17 tokens</li><li>mean: 31.98 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 31.96 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                                                 | sentence2                                                                                                                      | score                            |
  |:----------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------------------------|
  | <code>T R F DUMMY NECK UPPER MZ LOAD WIDEBAND 90 Deg Frontal Impact Simulation 2026 GENERIC IN-LBS</code> | <code>T R ROCKER AT C PILLAR LAT WIDEBAND 90 Deg Front 2021 P702 G-S</code>                                                    | <code>0.5234504780172093</code>  |
  | <code>T L ROCKER AT B_PILLAR VERT WIDEBAND 90 Deg Front 2024.5 P702 G-S</code>                            | <code>T RCM BTWN SEATS LOW G Z RCM C1 LZ ALV RC7 003 VOLT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2018 P558 VOLTS</code> | <code>0.36805699821563936</code> |
  | <code>T R FRAME AT C_PILLAR LONG WIDEBAND 90 Deg Left Side IIHS MDB to Vehicle 2024.5 P702 G-S</code>     | <code>T L F LAP BELT AT ANCHOR LOAD WIDEBAND 90 DEG / LEFT SIDE DECEL-3G 2021 P702 LBF</code>                                  | <code>0.5309750606095435</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`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 32
- `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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 32
- `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`: 7
- `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
| Epoch   | Step  | Training Loss | loss   | sts-dev_spearman_cosine |
|:-------:|:-----:|:-------------:|:------:|:-----------------------:|
| 1.0650  | 1000  | 7.6111        | 7.5503 | 0.4087                  |
| 2.1299  | 2000  | 7.5359        | 7.5420 | 0.4448                  |
| 3.1949  | 3000  | 7.5232        | 7.5292 | 0.4622                  |
| 4.2599  | 4000  | 7.5146        | 7.5218 | 0.4779                  |
| 5.3248  | 5000  | 7.5045        | 7.5200 | 0.4880                  |
| 6.3898  | 6000  | 7.4956        | 7.5191 | 0.4934                  |
| 7.4547  | 7000  | 7.4873        | 7.5170 | 0.4967                  |
| 8.5197  | 8000  | 7.4781        | 7.5218 | 0.4931                  |
| 9.5847  | 9000  | 7.4686        | 7.5257 | 0.4961                  |
| 10.6496 | 10000 | 7.4596        | 7.5327 | 0.4884                  |
| 11.7146 | 11000 | 7.4498        | 7.5403 | 0.4860                  |
| 12.7796 | 12000 | 7.4386        | 7.5507 | 0.4735                  |
| 13.8445 | 13000 | 7.4253        | 7.5651 | 0.4660                  |
| 14.9095 | 14000 | 7.4124        | 7.5927 | 0.4467                  |
| 15.9744 | 15000 | 7.3989        | 7.6054 | 0.4314                  |
| 17.0394 | 16000 | 7.3833        | 7.6654 | 0.4163                  |
| 18.1044 | 17000 | 7.3669        | 7.7186 | 0.3967                  |
| 19.1693 | 18000 | 7.3519        | 7.7653 | 0.3779                  |
| 20.2343 | 19000 | 7.3349        | 7.8356 | 0.3651                  |
| 21.2993 | 20000 | 7.3191        | 7.8772 | 0.3495                  |
| 22.3642 | 21000 | 7.3032        | 7.9346 | 0.3412                  |
| 23.4292 | 22000 | 7.2873        | 7.9624 | 0.3231                  |
| 24.4941 | 23000 | 7.2718        | 8.0169 | 0.3161                  |
| 25.5591 | 24000 | 7.2556        | 8.0633 | 0.3050                  |
| 26.6241 | 25000 | 7.2425        | 8.1021 | 0.2958                  |
| 27.6890 | 26000 | 7.2278        | 8.1563 | 0.2954                  |
| 28.7540 | 27000 | 7.2124        | 8.1955 | 0.2882                  |
| 29.8190 | 28000 | 7.2014        | 8.2234 | 0.2821                  |
| 30.8839 | 29000 | 7.1938        | 8.2447 | 0.2792                  |
| 31.9489 | 30000 | 7.1811        | 8.2609 | 0.2799                  |
| 32.0    | 30048 | -             | -      | 0.2722                  |


### 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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