| { | |
| "backbone": "xlm-roberta-base", | |
| "num_labels": 2, | |
| "max_length": 160, | |
| "dropout": 0.25, | |
| "id2label": { | |
| "0": "FAKE", | |
| "1": "REAL" | |
| }, | |
| "label2id": { | |
| "FAKE": 0, | |
| "REAL": 1 | |
| }, | |
| "decision_threshold": 0.52, | |
| "task": "binary-classification", | |
| "problem_type": "Multilingual Fake News Detection", | |
| "datasets": [ | |
| "LIAR", | |
| "utahnlp/x-fact" | |
| ], | |
| "languages": "25 languages", | |
| "metrics": { | |
| "val_f1_macro": 0.6825, | |
| "test_accuracy": 0.6843, | |
| "test_f1_macro": 0.6818, | |
| "test_f1_tuned": 0.6894, | |
| "test_f1_weighted": 0.6798 | |
| }, | |
| "architecture": { | |
| "base": "xlm-roberta-base", | |
| "pooling": "AttentionPooling (all tokens, not just CLS)", | |
| "head": "Linear(768\u2192384) + GELU + Dropout + Linear(384\u21922)" | |
| } | |
| } |