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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: 9.164422988891602 |
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validation_pearson_cosine: -0.10073561135203735 |
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validation_spearman_cosine: -0.05129891760425771 |
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validation_pearson_manhattan: -0.07223520049199797 |
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validation_spearman_manhattan: -0.05129891760425771 |
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validation_pearson_euclidean: -0.056592337170460805 |
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validation_spearman_euclidean: -0.05129891760425771 |
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validation_pearson_dot: -0.1007351930231386 |
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validation_spearman_dot: -0.05129891760425771 |
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validation_pearson_max: -0.056592337170460805 |
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validation_spearman_max: -0.05129891760425771 |
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runtime: 0.1267 |
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samples_per_second: 39.454 |
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steps_per_second: 7.891 |
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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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