Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
fact-checking
claim-verification
natural-language-inference
vifactcheck
full-context
eacl-2027
Instructions to use BaoNhan/cafebert-ViFactCheck-FC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-ViFactCheck-FC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-ViFactCheck-FC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-ViFactCheck-FC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,587 Bytes
2ee384f | 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 35 36 37 38 39 40 41 42 | {
"status": "completed",
"task": "ViFactCheck-full-context",
"dataset": "ViFactCheck",
"model_key": "cafebert",
"model_name": "CafeBERT",
"model_id": "uitnlp/CafeBERT",
"base_revision": "af76fcf2a04096b2b54b348a3e4eb48253c93c5d",
"seed": 202,
"split_seed": 42,
"smoke_test": false,
"epochs": 3,
"num_labels": 3,
"max_length": 256,
"micro_batch_size": 8,
"gradient_accumulation_steps": 1,
"effective_batch_size": 8,
"pair_truncation": "only_second",
"per_device_eval_batch_size": 8,
"bf16": true,
"tf32": true,
"text_mode_resolved": "raw",
"tokenizer_class": "XLMRobertaTokenizer",
"model_class": "XLMRobertaForSequenceClassification",
"model_type": "xlm-roberta",
"best_checkpoint": "/content/EACL_2027_ViFactCheck/runs_ml256_bs8_a10080_fast3/vifactcheck-full-context/cafebert/seed-202/trainer_mb8_ga1/checkpoint-2172",
"best_metric": 0.7278703385563059,
"best_model_dir": "/content/EACL_2027_ViFactCheck/runs_ml256_bs8_a10080_fast3/vifactcheck-full-context/cafebert/seed-202/best_model",
"train_loss": 0.6598229030658307,
"wall_seconds": 717.3174591064453,
"dev_accuracy": 0.7275242047026279,
"dev_macro_precision": 0.7322237480270842,
"dev_macro_recall": 0.7266974237293874,
"dev_macro_f1": 0.7278703385563059,
"dev_weighted_f1": 0.7293308299955444,
"test_accuracy": 0.6906077348066298,
"test_macro_precision": 0.6893027313027313,
"test_macro_recall": 0.6886096611790121,
"test_macro_f1": 0.6885226255450633,
"test_weighted_f1": 0.6905789769345113,
"completed_at_utc": "2026-07-23T11:52:29.652051+00:00"
} |