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: 1,212 Bytes
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"added_tokens_decoder": {
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"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": true
},
"1": {
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},
"2": {
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"special": true
},
"3": {
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"250001": {
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"lstrip": true,
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}
},
"additional_special_tokens": [],
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"eos_token": "</s>",
"extra_special_tokens": {},
"mask_token": "<mask>",
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"tokenizer_class": "XLMRobertaTokenizer",
"unk_token": "<unk>"
}
|