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
Commit History
9 days ago
-
Update README.md
Verified signatureb68ecaf
-
Fix the external-data reference: the graph named final.onnx.data, the file is model.onnx.data
Verified signature3865611 -
Credit the upstream source repository alongside the weights
Verified signature0bf0aea -
laya-ko: Korean fine-tune of Laya, with ONNX export, tokenizer and golden vectors
Verified signatured7dbb06 -
initial commit
Verified signatureeb900e7