Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:477170
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sanganaka/bge-m3-sanskritFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sanganaka/bge-m3-sanskritFT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sanganaka/bge-m3-sanskritFT") sentences = [ "These bodhisattvas were named", "अमुष्मै त्वा वज्रम् प्रहरामीति यद्यभिचरेद्वज्रो वै स्फ्य स्तृणुते हैवैनेन ॥", "तद्यत्स्रुचः सम्मार्ष्टि यथा वै देवानां चरणं तद्वा अनु मनुष्याणां तस्माद्यदामनुष्याणाम् परिवेषणमुपक्ल्प्तम् भवति ॥", "सुमतिना च । सुजातेन च ।" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d3db542763e340a07f86fdffca27ff1f1c1808181bc0b86f1f4c91daaa46c7f
- Size of remote file:
- 2.27 GB
- SHA256:
- b4728396ee8f11350c2c148f62bc671134db51d2833021c8a921397a3f5a0e92
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