|
|
| --- |
| 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 |
| --- |
| |
| # Model Trained Using AutoTrain |
|
|
| - Problem type: Sentence Transformers |
|
|
| ## Validation Metrics |
| loss: 2.767585039138794 |
|
|
| validation_pearson_cosine: 0.8085373520361815 |
|
|
| validation_spearman_cosine: 0.6588712021140699 |
|
|
| runtime: 24.5695 |
|
|
| samples_per_second: 7.815 |
|
|
| steps_per_second: 0.488 |
|
|
| : 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) |
| ``` |
|
|
