Text Classification
Transformers
Safetensors
Korean
gemma3
hallucination-detection
faithfulness
korean
sequence-classification
gemma-3
Instructions to use jismsy/ko-hallucheck-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jismsy/ko-hallucheck-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jismsy/ko-hallucheck-4b")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("jismsy/ko-hallucheck-4b") model = AutoModelForSequenceClassification.from_pretrained("jismsy/ko-hallucheck-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3f68c27d8a65fb137853bc4c917d75ff995bee4082cf58a9c4e490b2957fc3d
- Size of remote file:
- 33.4 MB
- SHA256:
- f4708757955e49e5b23494815a523ffa5bdd0a7b67c09d16a093f6151245ec5b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.