Sentence Similarity
sentence-transformers
PyTorch
Transformers
distilbert
feature-extraction
text-embeddings-inference
Instructions to use Hoax0930/sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hoax0930/sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hoax0930/sbert") 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 Hoax0930/sbert with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Hoax0930/sbert") model = AutoModel.from_pretrained("Hoax0930/sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Hoax0930/sbert: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/Hoax0930/sbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Hoax0930/sbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hoax0930/sbert/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- 16a82fc49747495079dc09f0c67ba305fad0000acc97d39b38c0bb04b3d5db05
- Size of remote file:
- 265 MB
- SHA256:
- d205bd040eab1d5c484feee7b723cd227805c74a082841d922c8920b82ba28bb
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