Sentence Similarity
sentence-transformers
PyTorch
English
distilbert
sentence similarity
text-embeddings-inference
Instructions to use Sakil/sentence_similarity_semantic_search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Sakil/sentence_similarity_semantic_search with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sakil/sentence_similarity_semantic_search") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sakil/sentence_similarity_semantic_search: direct link, hf CLI and curl.
- Browser
- Download file 28 Bytes
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https://huggingface.co/Sakil/sentence_similarity_semantic_search/resolve/d4fafd0557bc4dc29ff4199da3f01ef6c1c2e5e5/README.md
- Command line
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hf download hf://Sakil/sentence_similarity_semantic_search@d4fafd0557bc4dc29ff4199da3f01ef6c1c2e5e5/README.md
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curl -L -o README.md https://huggingface.co/Sakil/sentence_similarity_semantic_search/resolve/d4fafd0557bc4dc29ff4199da3f01ef6c1c2e5e5/README.md
28 Bytes
metadata
license: apache-2.0