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Ganaraj
/
rgveda-embedding-gemma

Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:51368
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Ganaraj/rgveda-embedding-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Ganaraj/rgveda-embedding-gemma with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Ganaraj/rgveda-embedding-gemma")
    
    sentences = [
        "task: search result | query: Poetic rendition of a chariot being made ready for a god.",
        "title: none | text: युञ्जन्ति हरी इषिरस्य गाथयोरौ रथ उरुयुगे\nइन्द्रवाहा वचोयुजा",
        "title: none | text: न यं रिपवो न रिषण्यवो गर्भे सन्तं रेषणा रेषयन्ति\nअन्धा अपश्या न दभन्न् अभिख्या नित्यास ईम् प्रेतारो अरक्षन्",
        "title: none | text: यथा पूर्वेभ्यो जरितृभ्य इन्द्र मय इवापो न तृष्यते बभूथ\nताम् अनु त्वा निविदं जोहवीमि विद्यामेषं वृजनं जीरदानुम्"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
rgveda-embedding-gemma
1.26 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Ganaraj's picture
Ganaraj
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35b314a verified 7 months ago
  • 1_Pooling
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  • 2_Dense
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  • 3_Dense
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  • eval
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
    1.02 kB
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  • model.safetensors
    1.21 GB
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  • modules.json
    604 Bytes
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  • sentence_bert_config.json
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  • special_tokens_map.json
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  • tokenizer.json
    33.4 MB
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  • tokenizer_config.json
    1.21 MB
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