Sentence Similarity
sentence-transformers
Safetensors
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use OmarIDK/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use OmarIDK/MNLP_M3_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("OmarIDK/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:
- 39de10494895cfb7b6cbdfb640152909d5f48964a6492b2c6cd52cd8f32348e1
- Size of remote file:
- 133 MB
- SHA256:
- 772487fa98b86cf51ec61e86b82e441b7ffe27b2a62179dae487bba07da68c76
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