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nlpai-lab
/
LAMAR-600m

Text Ranking
sentence-transformers
Safetensors
multilingual
xlm-roberta
cross-encoder
reranker
cross-lingual
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use nlpai-lab/LAMAR-600m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use nlpai-lab/LAMAR-600m with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("nlpai-lab/LAMAR-600m")
    
    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
LAMAR-600m
2.29 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
hongst's picture
hongst
Add paper citation
3af1f97 verified 2 days ago
  • assets
    init 5 days ago
  • .gitattributes
    1.57 kB
    init 5 days ago
  • README.md
    14.5 kB
    Add paper citation 2 days ago
  • config.json
    842 Bytes
    init 5 days ago
  • config_sentence_transformers.json
    263 Bytes
    init 5 days ago
  • model.safetensors
    2.27 GB
    xet
    init 5 days ago
  • modules.json
    131 Bytes
    init 5 days ago
  • sentence_bert_config.json
    225 Bytes
    init 5 days ago
  • tokenizer.json
    17.1 MB
    xet
    init 5 days ago
  • tokenizer_config.json
    575 Bytes
    init 5 days ago