Text Classification
Transformers
ONNX
Safetensors
Laya
Korean
English
Japanese
decision-model
system-one
calibrated-decisions
korean
klue
classification
routing
Eval Results (legacy)
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
Update README.md
Browse files
README.md
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@@ -81,7 +81,7 @@ Korean fine-tune of [Laya](https://huggingface.co/convaiinnovations/laya). Same
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architecture, same 322 M parameters, same single forward pass โ it just answers Korean
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typed decisions far better.
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**Code, benchmarks and the Rust/ONNX client:** https://github.com/
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> ๋ผ์ผ(Laya)๊ฐ ํ๊ตญ์ด๋ฅผ ์ ๋ชปํ๊ธธ๋ ํ๋ํด ๋ณด์์ต๋๋ค. ํ๊ตญ์ด ์ด์ฉ์๋ถ๋ค ํ์ดํ
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architecture, same 322 M parameters, same single forward pass โ it just answers Korean
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typed decisions far better.
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**Code, benchmarks and the Rust/ONNX client:** https://github.com/2nugu/laya-ko-decision-onnx/tree/main
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> ๋ผ์ผ(Laya)๊ฐ ํ๊ตญ์ด๋ฅผ ์ ๋ชปํ๊ธธ๋ ํ๋ํด ๋ณด์์ต๋๋ค. ํ๊ตญ์ด ์ด์ฉ์๋ถ๋ค ํ์ดํ
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