Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use devgupta/sbert-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use devgupta/sbert-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("devgupta/sbert-test") 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 devgupta/sbert-test with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("devgupta/sbert-test") model = AutoModel.from_pretrained("devgupta/sbert-test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0eba14fd106583f764bb8ff6bb80b9fc501eddd42fdf381b4fb575c45a79730
- Size of remote file:
- 438 MB
- SHA256:
- 03fe177407e2585deaa948e422cd0e5f5a96c86c3a1f9e8799acc1bc573484d1
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