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
Add exported onnx model 'model_O4.onnx' (#13)
Browse files- Add exported onnx model 'model_O4.onnx' (48076d8d58445691bbc391e85050be5a5f7bffee)
Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>
- onnx/model_O4.onnx +3 -0
onnx/model_O4.onnx
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size 66575087
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