Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cff68a22622457cc4ee27743071e45512f865ef38f318b8ff556d0a6696bdbc5
- Size of remote file:
- 5.5 kB
- SHA256:
- 9e0ef3cef8a1b0f47077bd4d82449bcf72e458f0ae1ca45468ac9f83598b117f
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