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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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 1024 tokens
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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## Usage
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 1024 tokens
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- **Similarity Function:** Cosine Similarity
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## Usage
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