Sentence Similarity
sentence-transformers
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use viols/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use viols/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("viols/MNLP_M3_document_encoder") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccd3206faeaa6a1928af83315c8869f0ba30218a8d73c4b7de9b49f4f79e76ed
- Size of remote file:
- 1.34 GB
- SHA256:
- 899a39031aa9a6b636e59ffad35ebbcff6a78a2431d761b275f1b5dbab180ac1
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