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:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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