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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Add exported onnx model 'model_qint8_avx512_vnni.onnx' (#12)
Verified signature- Add exported onnx model 'model_qint8_avx512_vnni.onnx' (71741157a8060fd1ffceaf5ea6a84ec1851496ea) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>dbfc33e -
Add exported onnx model 'model_O4.onnx' (#13)
Verified signature- Add exported onnx model 'model_O4.onnx' (48076d8d58445691bbc391e85050be5a5f7bffee) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>a272c09 -
Add exported onnx model 'model.onnx' (#11)
Verified signature- Add exported onnx model 'model.onnx' (b74b4294b326356a9ad49c29e231959aa88a6ed0) Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>90dfdf4