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
Download tokenizer.json from Bheri/ithasa-mmbert: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/Bheri/ithasa-mmbert/resolve/main/tokenizer.json
- Command line
-
hf download hf://Bheri/ithasa-mmbert/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Bheri/ithasa-mmbert/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 444ca40ca3ac65b5e9d1e509a622ba6358d3d04973bc8ec535d7ba0419fb572b
- Size of remote file:
- 34.4 MB
- SHA256:
- 8bd47075711f75a143d1b78e01a41cc65c1c591b00d3cfeffc23db07adce1392
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