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--- |
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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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- autotrain |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: 'search_query: i love autotrain' |
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sentences: |
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- 'search_query: huggingface auto train' |
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- 'search_query: hugging face auto train' |
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- 'search_query: i love autotrain' |
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pipeline_tag: sentence-similarity |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Sentence Transformers |
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## Validation Metrics |
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loss: 6.586054801940918 |
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validation_pearson_cosine: 0.15590647163663807 |
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validation_spearman_cosine: 0.28867513459481287 |
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validation_pearson_manhattan: 0.20874094632850035 |
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validation_spearman_manhattan: 0.28867513459481287 |
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validation_pearson_euclidean: 0.21989747670451043 |
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validation_spearman_euclidean: 0.28867513459481287 |
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validation_pearson_dot: 0.15590640231031966 |
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validation_spearman_dot: 0.28867513459481287 |
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validation_pearson_max: 0.21989747670451043 |
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validation_spearman_max: 0.28867513459481287 |
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runtime: 0.1469 |
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samples_per_second: 34.037 |
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steps_per_second: 6.807 |
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: 3.0 |
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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 Hugging Face 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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'search_query: autotrain', |
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'search_query: auto train', |
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'search_query: i love autotrain', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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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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``` |
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