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egerber1
/
xlm-roberta-crossencoder

Text Ranking
sentence-transformers
Safetensors
multilingual
xlm-roberta
cross-encoder
Generated from Trainer
dataset_size:9632
loss:BinaryCrossEntropyLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use egerber1/xlm-roberta-crossencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use egerber1/xlm-roberta-crossencoder with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("egerber1/xlm-roberta-crossencoder")
    
    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
xlm-roberta-crossencoder
1.13 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
egerber1's picture
egerber1
Add new CrossEncoder model
de55b8c verified about 1 year ago
  • .gitattributes
    1.57 kB
    Add new CrossEncoder model about 1 year ago
  • README.md
    13.1 kB
    Add new CrossEncoder model about 1 year ago
  • config.json
    877 Bytes
    Add new CrossEncoder model about 1 year ago
  • model.safetensors
    1.11 GB
    xet
    Add new CrossEncoder model about 1 year ago
  • special_tokens_map.json
    280 Bytes
    Add new CrossEncoder model about 1 year ago
  • tokenizer.json
    17.1 MB
    xet
    Add new CrossEncoder model about 1 year ago
  • tokenizer_config.json
    1.18 kB
    Add new CrossEncoder model about 1 year ago