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
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README.md
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base_model: intfloat/multilingual-e5-large-instruct
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license: apache-2.0
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# SentenceTransformer based on intfloat/multilingual-e5-large-instruct
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the universalml0/nepali_embedding_dataset dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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### Model Description
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language:
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license: apache-2.0
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## Model Details
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### Model Description
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