Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/xlm-roberta-base-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/xlm-roberta-base-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/xlm-roberta-base-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/xlm-roberta-base-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/xlm-roberta-base-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a8f0b8e927c39f2129a11d121604912646e00d38bcb3e5e4501be609f24a980
- Size of remote file:
- 5.5 kB
- SHA256:
- 604d2ca7c08047a82115153e42cb01d114a720b00dd5f0a909495bee0ed05991
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