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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: google-bert/bert-base-multilingual-cased |
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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: 1.0433918237686157 |
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runtime: 63.0935 |
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samples_per_second: 2.599 |
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steps_per_second: 0.174 |
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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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