Text Classification
Transformers
Safetensors
Vietnamese
distilbert
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/distilbert-multilingual-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/distilbert-multilingual-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/distilbert-multilingual-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/distilbert-multilingual-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "supported": { | |
| "precision": 0.7297297297297297, | |
| "recall": 0.6428571428571429, | |
| "f1-score": 0.6835443037974683, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.4720812182741117, | |
| "recall": 0.3924050632911392, | |
| "f1-score": 0.42857142857142855, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.5114754098360655, | |
| "recall": 0.6638297872340425, | |
| "f1-score": 0.5777777777777777, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.5676795580110497, | |
| "macro avg": { | |
| "precision": 0.5710954526133024, | |
| "recall": 0.5663639977941082, | |
| "f1-score": 0.5632978367155582, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.5745467706275301, | |
| "recall": 0.5676795580110497, | |
| "f1-score": 0.5657491310858679, | |
| "support": 724.0 | |
| } | |
| } |