Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:256886
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Bheri/ithasa-mmbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bheri/ithasa-mmbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bheri/ithasa-mmbert") sentences = [ "\"Now, take a look at the URL.\"", "Sanskrit: जीवाणु: पृथिव्यां विद्यमान: सर्वापेक्षया लघिष्ठ: जीव:।\nEnglish: Bacteria are the simplest and smallest organisms found on earth.", "Sanskrit: अहमस्य चित्रस्य विषये भवते किमपि वक्तुमिच्छामि ।\nEnglish: I want to tell you something about this image .", "Sanskrit: \"अधुना, URL पश्याम ।\"\nEnglish: \"Now, take a look at the URL.\"" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K