Text Classification
Transformers
Safetensors
Vietnamese
claim_verification
SemViQA
three-class-classification
fact-checking
Instructions to use SemViQA/tc-infoxlm-isedsc01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SemViQA/tc-infoxlm-isedsc01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SemViQA/tc-infoxlm-isedsc01")# Load model directly from transformers import ClaimModelForClassification model = ClaimModelForClassification.from_pretrained("SemViQA/tc-infoxlm-isedsc01", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12b82cb84833d90af18ed79c644f37f6f8a25a9eef40291231b43946db1f4100
- Size of remote file:
- 2.24 GB
- SHA256:
- 4be752521b15b16e2e9202dbb3f1155cfe0a0dfc9ad66561755d5e8a7cb6ae4e
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