Text Classification
Transformers
Safetensors
Vietnamese
distilbert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/distilbert-multilingual-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/distilbert-multilingual-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/distilbert-multilingual-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 196c164c87ca30c831f2dd80148658f5158c8ac9b8fdd58d59e7293c4a56b959
- Size of remote file:
- 5.5 kB
- SHA256:
- 8ecaef320dbe9839a8e147a7f33e68f6fca0a44bfcbbc0650bd8c5d93b92f68d
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