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
Download 1_Pooling/config.json from Ruthvikkk/MNLP_M2_document_encoder: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/Ruthvikkk/MNLP_M2_document_encoder/resolve/c20abe89ac0cdf484944ebdc26ecaaa1bfc9cf89/1_Pooling/config.json
- Command line
-
hf download hf://Ruthvikkk/MNLP_M2_document_encoder@c20abe89ac0cdf484944ebdc26ecaaa1bfc9cf89/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Ruthvikkk/MNLP_M2_document_encoder/resolve/c20abe89ac0cdf484944ebdc26ecaaa1bfc9cf89/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |