Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Core ML
ONNX
Safetensors
OpenVINO
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use Ruthvikkk/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ruthvikkk/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ruthvikkk/MNLP_M2_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
Commit History
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Adding `safetensors` variant of this model (#4)
- Adding `safetensors` variant of this model (eed417fa427e680f78590e830cb49f6827f2ee32) Co-authored-by: Andrei Nesterov <manifest@users.noreply.huggingface.co>c20abe8
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