Text Classification
Transformers
Safetensors
Vietnamese
claim_verification
SemViQA
binary-classification
fact-checking
Instructions to use SemViQA/bc-xlmr-isedsc01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SemViQA/bc-xlmr-isedsc01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SemViQA/bc-xlmr-isedsc01")# Load model directly from transformers import ClaimModelForClassification model = ClaimModelForClassification.from_pretrained("SemViQA/bc-xlmr-isedsc01", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02737b46a4049ef5ca85e251c8df17a4e1992bcc6bcf20e838a914a9b27ca167
- Size of remote file:
- 2.24 GB
- SHA256:
- 09964c3be6777c3a4af94046dd94cfb274201be52aac9d9cf0c39404b2b07d81
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