Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/bn-newsbert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/bn-newsbert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/bn-newsbert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/bn-newsbert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/bn-newsbert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,146 Bytes
536ffc3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"test_macro_f1_mean": 0.6822236082683636,
"test_macro_f1_std": 0.020221173156763098,
"test_macro_f1_text": "0.6822 ± 0.0202",
"test_accuracy_mean": 0.682780847145488,
"test_accuracy_std": 0.019840203891726714,
"test_accuracy_text": "0.6828 ± 0.0198",
"test_macro_precision_mean": 0.6844905259101943,
"test_macro_precision_std": 0.020775638611263955,
"test_macro_precision_text": "0.6845 ± 0.0208",
"test_macro_recall_mean": 0.6814768739986444,
"test_macro_recall_std": 0.020071255760093012,
"test_macro_recall_text": "0.6815 ± 0.0201",
"dev_macro_f1_mean": 0.7188106084932735,
"dev_macro_f1_std": 0.0063273577854815344,
"dev_macro_f1_text": "0.7188 ± 0.0063",
"task": "ViFactCheck-full-context",
"dataset": "ViFactCheck",
"model_key": "bn_newsbert",
"model_name": "BN-NewsBERT",
"base_model": "BaoNhan/BN-NewsBERT",
"seeds": [
22,
42,
202
],
"representative_seed": 202,
"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
} |