Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:594028
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use DoDucAnh/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DoDucAnh/MNLP_M2_document_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DoDucAnh/MNLP_M2_document_encoder") sentences = [ "'আমি' শব্দটি কোন লিঙ্গ?\nA. উভয় লিঙ্গ\nB. ক্লীব লিঙ্গ\nC. পুংলিঙ্গ\nD. স্ত্রী লিঙ্গ", "F.P. Dobroslavin, tibbin müxtəlif sahələri üzrə tanınmış bir alimdir, ancaq daha çox baş vermiş tədqiqatlara görə seçilir. Onun əməyinin sanitar-gigiyenik sahəyə təsiri əhəmiyyətlidir.", "বাংলা ভাষায় শব্দগুলোর লিঙ্গ সাধারণত তিনটি মূল শ্রেণিতে ভাগ হয়: পুংলিঙ্গ (পুরুষ), স্ত্রী লিঙ্গ (মহিলা), এবং ক্লীব লিঙ্গ (যার কোনো লিঙ্গ নেই)।", "Waves are disturbances that transfer energy from one place to another without transferring matter. Think of a ripple on a pond – the water molecules don't travel across the pond with the ripple; they mostly move up and down as the energy passes through them." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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