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
File size: 663 Bytes
8da4c75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"architectures": [
"XLMRobertaModel"
],
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"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
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"model_type": "xlm-roberta",
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"num_hidden_layers": 24,
"output_past": true,
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"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.52.3",
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"use_cache": true,
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}
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