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
| { | |
| "test_macro_f1_mean": 0.5866979355548291, | |
| "test_macro_f1_std": 0.020313865855319885, | |
| "test_macro_f1_text": "0.5867 ± 0.0203", | |
| "test_accuracy_mean": 0.5879373848987108, | |
| "test_accuracy_std": 0.017598079975705685, | |
| "test_accuracy_text": "0.5879 ± 0.0176", | |
| "test_macro_precision_mean": 0.593654940648605, | |
| "test_macro_precision_std": 0.01966614206058216, | |
| "test_macro_precision_text": "0.5937 ± 0.0197", | |
| "test_macro_recall_mean": 0.5865456513674486, | |
| "test_macro_recall_std": 0.017548551221113946, | |
| "test_macro_recall_text": "0.5865 ± 0.0175", | |
| "dev_macro_f1_mean": 0.5791650674505112, | |
| "dev_macro_f1_std": 0.011344163758615055, | |
| "dev_macro_f1_text": "0.5792 ± 0.0113", | |
| "task": "ViFactCheck-full-context", | |
| "dataset": "ViFactCheck", | |
| "model_key": "distilbert", | |
| "model_name": "Multilingual DistilBERT", | |
| "base_model": "distilbert-base-multilingual-cased", | |
| "seeds": [ | |
| 22, | |
| 42, | |
| 202 | |
| ], | |
| "representative_seed": 42, | |
| "selection_rule": "maximum development Macro-F1; seed ascending tie-break", | |
| "split_policy": "merged_stratified_80_10_10", | |
| "split_seed": 42, | |
| "max_length": 256, | |
| "epochs": 3, | |
| "effective_batch_size": 8 | |
| } |