Sentence Similarity
sentence-transformers
Safetensors
English
Nepali
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:45199
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use universalml/Nepali_Embedding_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use universalml/Nepali_Embedding_Model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("universalml/Nepali_Embedding_Model") sentences = [ "मैले विचार गर्नुपर्ने कलेजहरू के के हुन्, विचार गर्नुपर्ने कारकहरू: केएमसी म्यानिपल वा केएमसी मंगोलमा?", "मंगलोर शान्त वा हिंस्रक स्थान हो?", "पुरुषहरूको तुलनामा महिलाहरूको लागि यौनिक आनन्द बढी हुन्छ कि हुँदैन?", "के कसैले केएमसी मानिपाल र मंगलोरको संक्षिप्त तुलना गर्न सक्छ?" ] 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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