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Bheri
/
ithasa-mmbert-v3

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:3749530
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Bheri/ithasa-mmbert-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Bheri/ithasa-mmbert-v3 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Bheri/ithasa-mmbert-v3")
    
    sentences = [
        "The false promise of sovereignty.",
        "२ जिगीषोः प्रयाणे च अमरः।",
        "सत्त्वहेतु सुदृढकृत प्रतिज्ञा।",
        "उम्र क्रमशः 16 वर्ष व 15 वर्ष है।"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
ithasa-mmbert-v3 / eval
477 Bytes
Ctrl+K
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  • 1 contributor
History: 1 commit
saikasyap's picture
saikasyap
Initial commit
ca55df8 verified 4 months ago
  • translation_evaluation_eval-en-sa_results.csv
    477 Bytes
    Initial commit 4 months ago