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2nugu
/
laya-ko

Text Classification
Transformers
ONNX
Safetensors
Laya
Korean
English
Japanese
decision-model
system-one
calibrated-decisions
korean
klue
classification
routing
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use 2nugu/laya-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use 2nugu/laya-ko with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="2nugu/laya-ko")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("2nugu/laya-ko", device_map="auto")
  • Laya

    How to use 2nugu/laya-ko with Laya:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • Notebooks
  • Google Colab
  • Kaggle
laya-ko / tokenizer
34.4 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
2nugu's picture
2nugu
laya-ko: Korean fine-tune of Laya, with ONNX export, tokenizer and golden vectors
d7dbb06 verified 14 days ago
  • special_tokens_map.json
    1.03 kB
    laya-ko: Korean fine-tune of Laya, with ONNX export, tokenizer and golden vectors 14 days ago
  • tokenizer.json
    34.4 MB
    xet
    laya-ko: Korean fine-tune of Laya, with ONNX export, tokenizer and golden vectors 14 days ago
  • tokenizer_config.json
    46.5 kB
    laya-ko: Korean fine-tune of Laya, with ONNX export, tokenizer and golden vectors 14 days ago