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
Download pytorch_model.bin from Sakil/sentence_similarity_semantic_search: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/Sakil/sentence_similarity_semantic_search/resolve/8ee643a3f3d1a4ec4eb8b22321cc9152a2776e19/pytorch_model.bin
- Command line
-
hf download hf://Sakil/sentence_similarity_semantic_search@8ee643a3f3d1a4ec4eb8b22321cc9152a2776e19/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sakil/sentence_similarity_semantic_search/resolve/8ee643a3f3d1a4ec4eb8b22321cc9152a2776e19/pytorch_model.bin
265 MB
- Xet hash:
- 7212394282b52648a1e0f3f875d41de560970ab5cf6f374fdf79fc765f14571a
- Size of remote file:
- 265 MB
- SHA256:
- 259e08c3dab8b1fb779582435b732199f73e30ab38943bdf4f584554217f8243
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