Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:257886
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Bheri/ithasa-mmbert-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bheri/ithasa-mmbert-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bheri/ithasa-mmbert-v2") sentences = [ "Then bring the needle back to the back side of the fabric.", "अधिभूतं क्षरो भावः पुरुषश्चाधिदैवतम्। अधियज्ञोऽहमेवात्र देहे देहभृतां वर॥", "मराठी-चलच्चित्रे \"साव्ळी प्रेमाची\" इत्यस्मिन् सः मुख्यपात्रं निरवहत्।\n", "तदनन्तरं वस्त्रस्य तटे सूचिकां पृष्टे क्रियते ।" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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