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--- |
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language: [] |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dataset_size:100K<n<1M |
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- loss:CoSENTLoss |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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base_model: distilbert/distilbert-base-uncased |
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widget: |
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- source_sentence: T L 2 DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S |
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sentences: |
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- T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020.5 U625 G-S |
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- T L F DUMMY HEAD CG LAT WIDEBAND Static Airbag OOP Test 2025 CX430 G-S |
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- T R F DUMMY PELVIS LAT WIDEBAND 90 Deg Frontal Impact Simulation 2026 P800 G-S |
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- source_sentence: T L F DUMMY CHEST LONG WIDEBAND 90 Deg Front 2022 U553 G-S |
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sentences: |
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- T R F TORSO BELT AT D RING LOAD WIDEBAND 90 Deg Front 2022 U553 LBF |
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- T L F DUMMY L UP TIBIA MY LOAD WIDEBAND 90 Deg Front 2015 P552 IN-LBS |
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- T R F DUMMY R UP TIBIA FX LOAD WIDEBAND 30 Deg Front Angular Left 2022 U554 LBF |
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- source_sentence: T R F DUMMY PELVIS LAT WIDEBAND 90 Deg Front 2019 D544 G-S |
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sentences: |
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- T L F DUMMY PELVIS LAT WIDEBAND 90 Deg Front 2015 P552 G-S |
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- T L LOWER CONTROL ARM VERT WIDEBAND Left Side Drop Test 2024.5 P702 G-S |
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- F BARRIER PLATE 11030 SZ D FX LOAD WIDEBAND 90 Deg Front 2015 P552 LBF |
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- source_sentence: T ENGINE ENGINE TOP LAT WIDEBAND 90 Deg Front 2015 P552 G-S |
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sentences: |
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- T R ENGINE TRANS BOTTOM LAT WIDEBAND 90 Deg Front 2015 P552 G-S |
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- F BARRIER PLATE 09030 SZ D FX LOAD WIDEBAND 90 Deg Front 2015 P552 LBF |
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- T R F DUMMY NECK UPPER MX LOAD WIDEBAND 90 Deg Front 2022 U554 IN-LBS |
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- source_sentence: T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S |
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sentences: |
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- T R F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2025 V363N G-S |
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- T R F DUMMY HEAD CG VERT WIDEBAND VIA Linear Impact Test 2021 C727 G-S |
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- T L F DUMMY T1 VERT WIDEBAND 75 Deg Oblique Left Side 10 in. Pole 2026 P800 G-S |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on distilbert/distilbert-base-uncased |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.27051173706186693 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.2798593637893599 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.228702027931258 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.25353345676390787 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.23018017587211453 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.2550481010151111 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.2125353301405465 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.1902748420981738 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.27051173706186693 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.2798593637893599 |
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name: Spearman Max |
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- type: pearson_cosine |
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value: 0.26319176781258086 |
|
name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.2721909587247752 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.21766215319708615 |
|
name: Pearson Manhattan |
|
- type: spearman_manhattan |
|
value: 0.2439514548051345 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
|
value: 0.2195389492634635 |
|
name: Pearson Euclidean |
|
- type: spearman_euclidean |
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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 |
|
--- |
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|
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# SentenceTransformer based on distilbert/distilbert-base-uncased |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'T L F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2020 CX482 G-S', |
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'T R F DUMMY CHEST LAT WIDEBAND 90 Deg Front 2025 V363N G-S', |
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'T R F DUMMY HEAD CG VERT WIDEBAND VIA Linear Impact Test 2021 C727 G-S', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.2705 | |
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| **spearman_cosine** | **0.2799** | |
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| pearson_manhattan | 0.2287 | |
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| spearman_manhattan | 0.2535 | |
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| pearson_euclidean | 0.2302 | |
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| spearman_euclidean | 0.255 | |
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| pearson_dot | 0.2125 | |
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| spearman_dot | 0.1903 | |
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| pearson_max | 0.2705 | |
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| spearman_max | 0.2799 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.2632 | |
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| **spearman_cosine** | **0.2722** | |
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| pearson_manhattan | 0.2177 | |
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| spearman_manhattan | 0.244 | |
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| pearson_euclidean | 0.2195 | |
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| spearman_euclidean | 0.2463 | |
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| pearson_dot | 0.2107 | |
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| spearman_dot | 0.1865 | |
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| pearson_max | 0.2632 | |
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| spearman_max | 0.2722 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 481,114 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 103,097 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:----------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 32 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 32 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 7 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: True |
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- `dataloader_num_workers`: 0 |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
|
- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: False |
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- `include_tokens_per_second`: False |
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- `neftune_noise_alpha`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | |
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|:-------:|:-----:|:-------------:|:------:|:-----------------------:| |
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| 1.0650 | 1000 | 7.6111 | 7.5503 | 0.4087 | |
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| 2.1299 | 2000 | 7.5359 | 7.5420 | 0.4448 | |
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| 3.1949 | 3000 | 7.5232 | 7.5292 | 0.4622 | |
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| 4.2599 | 4000 | 7.5146 | 7.5218 | 0.4779 | |
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| 5.3248 | 5000 | 7.5045 | 7.5200 | 0.4880 | |
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| 6.3898 | 6000 | 7.4956 | 7.5191 | 0.4934 | |
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| 7.4547 | 7000 | 7.4873 | 7.5170 | 0.4967 | |
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| 8.5197 | 8000 | 7.4781 | 7.5218 | 0.4931 | |
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| 9.5847 | 9000 | 7.4686 | 7.5257 | 0.4961 | |
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| 10.6496 | 10000 | 7.4596 | 7.5327 | 0.4884 | |
|
| 11.7146 | 11000 | 7.4498 | 7.5403 | 0.4860 | |
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| 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 | |
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| 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 | |
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| 32.0 | 30048 | - | - | 0.2722 | |
|
|
|
|
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### Framework Versions |
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- Python: 3.10.6 |
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- Sentence Transformers: 3.0.0 |
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- Transformers: 4.35.0 |
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- PyTorch: 2.1.0a0+4136153 |
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- Accelerate: 0.30.1 |
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- Datasets: 2.14.1 |
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- Tokenizers: 0.14.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
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``` |
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