Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:51368
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
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
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