Text Classification
Transformers
Safetensors
Vietnamese
bert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/vibert-base-cased-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/vibert-base-cased-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/vibert-base-cased-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/vibert-base-cased-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/vibert-base-cased-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6430da80ebef21f43ab4dc72a42912fb9d223144531480cc45b96bbe58ee38e7
- Size of remote file:
- 5.5 kB
- SHA256:
- f5472247db805364f1482c620ae57eb0f5d0ec0b33af372594b832cf50e8d88b
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