Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:256886
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Bheri/ithasa-embeddinggemma-300m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bheri/ithasa-embeddinggemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bheri/ithasa-embeddinggemma-300m") sentences = [ "David Bradbury had served as the Mayor of Penrith.\n", "डेविड् ब्राड्बरी इत्ययं पेन्रित्-नगरस्य महापौरत्वेन कार्यम् अकरोत्।\n", "\"वयं विद्यार्थिनां रेकोर्ड्स् एकस्यैव फोर्मेट् इत्यस्य, बह्वीषु सञ्चिकासु , नाम awkdemo_mod.txt अपि च awkdemo2.txt इत्येतयोः प्राप्तवन्तः इति भावयामः ।\"", "ततो देवाः सगन्धर्वाः सिद्धाश्च परमर्षयः। चिन्तामापेदिरे सर्वे सकिंनरमहोरगाः॥" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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