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": 0.7695852534562212, | |
| "recall": 0.6626984126984127, | |
| "f1-score": 0.7121535181236673, | |
| "support": 252.0 | |
| }, | |
| "refuted": { | |
| "precision": 0.5198237885462555, | |
| "recall": 0.4978902953586498, | |
| "f1-score": 0.5086206896551724, | |
| "support": 237.0 | |
| }, | |
| "not_enough_information": { | |
| "precision": 0.5321428571428571, | |
| "recall": 0.6340425531914894, | |
| "f1-score": 0.5786407766990291, | |
| "support": 235.0 | |
| }, | |
| "accuracy": 0.5994475138121547, | |
| "macro avg": { | |
| "precision": 0.6071839663817779, | |
| "recall": 0.598210420416184, | |
| "f1-score": 0.5998049948259563, | |
| "support": 724.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.6107559298135383, | |
| "recall": 0.5994475138121547, | |
| "f1-score": 0.6021911222924197, | |
| "support": 724.0 | |
| } | |
| } |