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 config_sentence_transformers.json from Sakil/sentence_similarity_semantic_search: direct link, hf CLI and curl.
- Browser
- Download file 122 Bytes
-
https://huggingface.co/Sakil/sentence_similarity_semantic_search/resolve/8ee643a3f3d1a4ec4eb8b22321cc9152a2776e19/config_sentence_transformers.json
- Command line
-
hf download hf://Sakil/sentence_similarity_semantic_search@8ee643a3f3d1a4ec4eb8b22321cc9152a2776e19/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/Sakil/sentence_similarity_semantic_search/resolve/8ee643a3f3d1a4ec4eb8b22321cc9152a2776e19/config_sentence_transformers.json
122 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "2.0.0", | |
| "transformers": "4.7.0", | |
| "pytorch": "1.9.0+cu102" | |
| } | |
| } |