Text Classification
Transformers
Safetensors
Vietnamese
claim_verification
SemViQA
binary-classification
fact-checking
Instructions to use SemViQA/bc-xlmr-viwikifc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SemViQA/bc-xlmr-viwikifc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SemViQA/bc-xlmr-viwikifc")# Load model directly from transformers import ClaimModelForClassification model = ClaimModelForClassification.from_pretrained("SemViQA/bc-xlmr-viwikifc", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ClaimModelForClassification" | |
| ], | |
| "dropout": 0.3, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "loss_type": "ce", | |
| "model_name": "FacebookAI/xlm-roberta-large", | |
| "model_type": "claim_verification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.0" | |
| } | |