Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use l3cube-pune/indic-sentence-similarity-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use l3cube-pune/indic-sentence-similarity-sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("l3cube-pune/indic-sentence-similarity-sbert") sentences = [ "दिवाळी आपण मोठ्या उत्साहाने साजरी करतो", "दिवाळी आपण आनंदाने साजरी करतो", "दिवाळी हा दिव्यांचा सण आहे" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use l3cube-pune/indic-sentence-similarity-sbert with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/indic-sentence-similarity-sbert") model = AutoModel.from_pretrained("l3cube-pune/indic-sentence-similarity-sbert") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f759ed80d659d5e636309411c446d5cfb6ca269ac43c806b94f34a2520da284
- Size of remote file:
- 950 MB
- SHA256:
- 4ebe74695af9bbcf59fc53bee524c34a6e01531c032c0c9daa0ca9bc93f67f31
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