Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/xlm-roberta-base-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/xlm-roberta-base-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/xlm-roberta-base-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/xlm-roberta-base-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/xlm-roberta-base-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0cc0d545c564755f1fec3090823d8b355795c8c3a151fe9ab49f6b01770db7e
- Size of remote file:
- 5.5 kB
- SHA256:
- 72955872087cefa884e90f9e8086275fdd89c303b55ebcb45eb61eaab4c5d01a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.