Text Classification
Transformers
Safetensors
Vietnamese
distilbert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/distilbert-multilingual-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/distilbert-multilingual-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/distilbert-multilingual-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df8328d8030ea9f6dc76dbd0ef25bbc770ce52f73a8b2159e0abee86ba16bb7e
- Size of remote file:
- 5.5 kB
- SHA256:
- 528c3fc9e2c396560373d87eb94d14468a9f7ed6152f23d795a914914b2860fe
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