| |
|
| | --- |
| | library_name: sentence-transformers |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - autotrain |
| | base_model: sentence-transformers/all-MiniLM-L6-v2 |
| | widget: |
| | - source_sentence: 'search_query: i love autotrain' |
| | sentences: |
| | - 'search_query: huggingface auto train' |
| | - 'search_query: hugging face auto train' |
| | - 'search_query: i love autotrain' |
| | pipeline_tag: sentence-similarity |
| | datasets: |
| | - sentence-transformers/all-nli |
| | --- |
| | |
| | # Model Trained Using AutoTrain |
| |
|
| | - Problem type: Sentence Transformers |
| |
|
| | ## Validation Metrics |
| | loss: 0.20218822360038757 |
| |
|
| | cosine_accuracy: 0.9600546780072904 |
| | |
| | runtime: 246.062 |
| | |
| | samples_per_second: 26.757 |
| | |
| | steps_per_second: 1.674 |
| | |
| | : 3.0 |
| | |
| | ## Usage |
| | |
| | ### Direct Usage (Sentence Transformers) |
| | |
| | First install the Sentence Transformers library: |
| | |
| | ```bash |
| | pip install -U sentence-transformers |
| | ``` |
| | |
| | Then you can load this model and run inference. |
| | ```python |
| | from sentence_transformers import SentenceTransformer |
| |
|
| | # Download from the Hugging Face Hub |
| | model = SentenceTransformer("sentence_transformers_model_id") |
| | # Run inference |
| | sentences = [ |
| | 'search_query: autotrain', |
| | 'search_query: auto train', |
| | 'search_query: i love autotrain', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities.shape) |
| | ``` |
| | |
