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rabit223
/
vi_MiniLM_v1

Text Ranking
sentence-transformers
Safetensors
bert
cross-encoder
reranker
Generated from Trainer
dataset_size:7491
loss:BinaryCrossEntropyLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use rabit223/vi_MiniLM_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use rabit223/vi_MiniLM_v1 with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("rabit223/vi_MiniLM_v1")
    
    query = "Which planet is known as the Red Planet?"
    passages = [
    	"Venus is often called Earth's twin because of its similar size and proximity.",
    	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
    	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
    ]
    
    scores = model.predict([(query, passage) for passage in passages])
    print(scores)
  • Notebooks
  • Google Colab
  • Kaggle
vi_MiniLM_v1
91.6 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
rabit223's picture
rabit223
Upload Vietnamese MiniLM cross-encoder
5a2d4a4 about 1 month ago
  • .gitattributes
    1.52 kB
    initial commit about 1 month ago
  • README.md
    21.2 kB
    Upload Vietnamese MiniLM cross-encoder about 1 month ago
  • config.json
    957 Bytes
    Upload Vietnamese MiniLM cross-encoder about 1 month ago
  • model.safetensors
    90.9 MB
    xet
    Upload Vietnamese MiniLM cross-encoder about 1 month ago
  • tokenizer.json
    712 kB
    Upload Vietnamese MiniLM cross-encoder about 1 month ago
  • tokenizer_config.json
    453 Bytes
    Upload Vietnamese MiniLM cross-encoder about 1 month ago