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--- |
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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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- dense |
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- generated_from_trainer |
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- dataset_size:902990 |
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- loss:MultipleNegativesSymmetricRankingLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: dove deodorant stick fresh |
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sentences: |
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- women's deodorant |
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- antiperspirant deodorant stick |
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- bubbles natur.bottle w.hand pink(6)m280m |
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- source_sentence: avrelle travel size olive oil & rocca set |
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sentences: |
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- haircare set |
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- shampoo |
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- maldives waterfall body splash |
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- source_sentence: not not donner |
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sentences: |
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- mix chocolate pie |
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- stuffed dough with donner, mozzarella cheese, tomatoes, lettuce, onion with ranch |
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sauce. |
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- deli |
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- source_sentence: pink n' yellow |
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sentences: |
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- mirror |
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- yellow mirror |
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- watercolor pan 2.5 ml scarlet semi white nights no 1911318 |
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- source_sentence: ice cream roll machine for kids |
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sentences: |
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- horse pink |
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- pretend play |
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- chocolates game |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9764419794082642 |
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name: Cosine Accuracy |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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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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### 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/huggingface/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) |
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(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}) |
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(2): Normalize() |
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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("LamaDiab/MiniLM-v2-v32-SemanticEngine") |
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# Run inference |
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sentences = [ |
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'ice cream roll machine for kids', |
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'chocolates game', |
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'horse pink', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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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) |
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# tensor([[ 1.0000, 0.3436, -0.0405], |
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# [ 0.3436, 1.0000, 0.0382], |
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# [-0.0405, 0.0382, 1.0000]]) |
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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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#### Triplet |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| **cosine_accuracy** | **0.9764** | |
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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: 902,990 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>itemCategory</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | itemCategory | |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 10.31 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.38 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.01 tokens</li><li>max: 9 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | itemCategory | |
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|:-----------------------------------------------------|:---------------------------|:----------------------------------| |
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| <code>adult skipping rope - petrol</code> | <code>skipping rope</code> | <code>gym equipment</code> | |
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| <code>bambino rainbow gold & sliver sage</code> | <code>shisha</code> | <code>home decor accessory</code> | |
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| <code>joobran’s minimalist clutch – evergreen</code> | <code>crossbody bag</code> | <code>bag</code> | |
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* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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": "cos_sim", |
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"gather_across_devices": false |
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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: 9,466 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, and <code>itemCategory</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | itemCategory | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| |
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| type | string | string | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 9.65 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 5.75 tokens</li><li>max: 131 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.32 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.82 tokens</li><li>max: 9 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | itemCategory | |
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|:-------------------------------------------------------|:--------------------------|:--------------------------------------------------------|:----------------------| |
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| <code>extra bubblemint sugar free chewing gum</code> | <code> gum</code> | <code>dried mangoes</code> | <code>sweet</code> | |
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| <code>golden pothos</code> | <code>indoor plant</code> | <code>fire truck gam</code> | <code>plant</code> | |
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| <code>effortless style slit linen pants - beige</code> | <code>beige pants</code> | <code>light grey keep going stone washed t-shirt</code> | <code>trousers</code> | |
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* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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": "cos_sim", |
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"gather_across_devices": false |
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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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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `gradient_accumulation_steps`: 2 |
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- `learning_rate`: 3e-05 |
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- `weight_decay`: 0.01 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `dataloader_num_workers`: 1 |
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- `dataloader_prefetch_factor`: 2 |
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- `dataloader_persistent_workers`: True |
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- `push_to_hub`: True |
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- `hub_model_id`: LamaDiab/MiniLM-v2-v32-SemanticEngine |
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- `hub_strategy`: all_checkpoints |
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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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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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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`: 2 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 3e-05 |
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- `weight_decay`: 0.01 |
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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`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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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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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: 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`: 0 |
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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`: False |
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- `dataloader_num_workers`: 1 |
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- `dataloader_prefetch_factor`: 2 |
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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_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': 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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- `dataloader_persistent_workers`: 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`: True |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: LamaDiab/MiniLM-v2-v32-SemanticEngine |
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- `hub_strategy`: all_checkpoints |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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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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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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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 |
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- `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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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | |
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|:------:|:----:|:-------------:|:---------------:|:---------------:| |
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| 0.0006 | 1 | 2.7497 | - | - | |
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| 0.5669 | 1000 | 1.9615 | 1.0099 | 0.9696 | |
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| 1.1337 | 2000 | 1.7676 | 0.8953 | 0.9739 | |
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| 1.7003 | 3000 | 1.4662 | 0.8705 | 0.9748 | |
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| 2.2669 | 4000 | 1.3918 | 0.8650 | 0.9761 | |
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| 2.8334 | 5000 | 1.3306 | 0.8527 | 0.9764 | |
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### Framework Versions |
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- Python: 3.11.13 |
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- Sentence Transformers: 5.1.2 |
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- Transformers: 4.53.3 |
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- PyTorch: 2.6.0+cu124 |
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- Accelerate: 1.9.0 |
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- Datasets: 4.4.1 |
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- Tokenizers: 0.21.2 |
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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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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