Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use embedingHF/Sentence_Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embedingHF/Sentence_Transformer with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embedingHF/Sentence_Transformer") 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] - Transformers
How to use embedingHF/Sentence_Transformer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("embedingHF/Sentence_Transformer") model = AutoModel.from_pretrained("embedingHF/Sentence_Transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from embedingHF/Sentence_Transformer: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/embedingHF/Sentence_Transformer/resolve/main/model.safetensors
- Command line
-
hf download hf://embedingHF/Sentence_Transformer/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/embedingHF/Sentence_Transformer/resolve/main/model.safetensors
90.9 MB
- Xet hash:
- 25b14486b3597a0c07240d49c6845a731064a62b74f20fae077038c6021641cb
- Size of remote file:
- 90.9 MB
- SHA256:
- 675124690d56e46f07b91be24721b00328dcf35c9962067d94d2894dc6970402
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