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
Improve model card and add paper abstract
#1
by nielsr HF Staff - opened
This PR improves the model card by adding the paper abstract and some additional details about the model's purpose and achievements from the GitHub README. It also adds the hf_hub_url tag for easier access and integration within the Hugging Face ecosystem.
xuandin changed pull request status to merged