Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use Smxldo/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Smxldo/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Smxldo/MNLP_M3_document_encoder") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15c719880f1b410656cb8c21d9f22ec193c58fc15ec3b3afc812a9da5d16dc89
- Size of remote file:
- 2.36 MB
- SHA256:
- f866c945fa2147ce4940c8ead0b4b20af4217e0f4664bd35f41c2a0967c4fe77
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