Text Classification
Transformers
Safetensors
Vietnamese
electra
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
gold-evidence
eacl-2027
Instructions to use BaoNhan/velectra-base-ViFactCheck-GE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/velectra-base-ViFactCheck-GE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/velectra-base-ViFactCheck-GE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/velectra-base-ViFactCheck-GE") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/velectra-base-ViFactCheck-GE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb364f9827949c0cee7bdf92b8c0640b0df846e3e6d906bc1e8f0b503ca1953b
- Size of remote file:
- 5.5 kB
- SHA256:
- f66f88e7471a6c493a3ede77ced16faa76226a302d85bfc84575258dfe83e1f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.