Publish VFC-GE representative seed 22

README.md

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+---
+language:
+- vi
+library_name: transformers
+pipeline_tag: text-classification
+base_model: "xlm-roberta-base"
+datasets:
+- tranthaihoa/vifactcheck
+tags:
+- vietnamese
+- text-classification
+- fact-checking
+- claim-verification
+- natural-language-inference
+- vifactcheck
+- gold-evidence
+- eacl-2027
+metrics:
+- f1
+- accuracy
+---
+
+# xlm-roberta-base-ViFactCheck-GE
+
+This model is `xlm-roberta-base` fine-tuned for **VFC-GE** on ViFactCheck using the claim paired with **gold evidence**.
+
+## Evaluation protocol
+
+- Dataset size: 7,232 examples.
+- Shared fixed stratified splits for FC and GE: 5,785 train / 723 development / 724 test.
+- Labels: Supported, Refuted, and Not Enough Information.
+- Fine-tuning seeds: [42, 22, 202].
+- Training: 3 epoch(s), AdamW, learning rate 2e-05, weight decay 0.01, warmup ratio 0.1.
+- Effective train batch size: **8** (hard-validated against every published run).
+- Maximum sequence length: 256.
+- Input mode: raw Vietnamese claim and passage.
+- The claim is always preserved; only the second sequence (gold evidence) is truncated when the pair exceeds the encoder limit.
+- Topic, author, outlet, URL and other source metadata are excluded from model inputs.
+- No class weighting, resampling, retrieval model, sentence ranking, test-time model selection or external evidence is used.
+- Checkpoints are selected by development Macro-F1. The representative published checkpoint is seed **22**, selected only by development Macro-F1.
+
+## Results
+
+Test metrics are reported as mean ± sample standard deviation over seeds [42, 22, 202].
+
+| Metric | Mean ± std |
+|---|---:|
+| Test Macro-F1 | 0.4307 ± 0.3173 |
+| Test accuracy | 0.5124 ± 0.2384 |
+| Test macro precision | 0.4408 ± 0.3344 |
+| Test macro recall | 0.5053 ± 0.2451 |
+| Development Macro-F1 | 0.4253 ± 0.3091 |
+
+### Per-seed results
+
+|       seed |   dev_macro_f1 |   test_macro_f1 |   test_accuracy |   micro_batch_size |   gradient_accumulation_steps |
+|-----------:|---------------:|----------------:|----------------:|-------------------:|------------------------------:|
+|  22.000000 |       0.769609 |        0.784757 |        0.785912 |           8.000000 |                      1.000000 |
+|  42.000000 |       0.171800 |        0.172131 |        0.348066 |           8.000000 |                      1.000000 |
+| 202.000000 |       0.334518 |        0.335286 |        0.403315 |           8.000000 |                      1.000000 |
+
+## Label mapping
+
+```json
+{
+  "0": "supported",
+  "1": "refuted",
+  "2": "not_enough_information"
+}
+```
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_id = "BaoNhan/xlm-roberta-base-ViFactCheck-GE"
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+claim = "Thông tin này đã được cơ quan chức năng xác nhận."
+evidence = "Bài báo cung cấp bằng chứng liên quan đến phát biểu trên."
+inputs = tokenizer(
+    claim,
+    evidence,
+    return_tensors="pt",
+    truncation="only_second",
+    max_length=256,
+)
+with torch.no_grad():
+    probabilities = model(**inputs).logits.softmax(dim=-1)[0]
+predicted_id = int(probabilities.argmax())
+print(model.config.id2label[predicted_id], probabilities.tolist())
+```
+
+## Files
+
+- `aggregate_metrics.json`: aggregate metrics and training manifest.
+- `artifacts/per_seed_results.csv`: one row per fine-tuning seed.
+- `artifacts/seed_*_confusion_matrix.csv`: confusion matrix for each seed.
+- `artifacts/seed_*_classification_report.json`: per-class metrics.
+- `artifacts/seed_*_test_predictions.csv`: IDs, gold/predicted labels and probabilities; raw claims and passages are excluded.
+
+## Limitations
+
+ViFactCheck supplies the correct source article and therefore does not evaluate open-web evidence retrieval. **VFC-FC** can truncate relevant information in long articles and jointly measures verification plus robustness to irrelevant context. **VFC-GE** uses oracle gold evidence and must not be presented as a realistic end-to-end deployment setting. This model is a research classifier, not an automated arbiter of truth, and may produce confidently incorrect predictions.
+
+## Dataset citation
+
+```bibtex
+@inproceedings{hoa2025vifactcheck,
+  title={ViFactCheck: A New Benchmark Dataset and Methods for Multi-domain News Fact-Checking in Vietnamese},
+  author={Hoa, Tran Thai and Duy, Tran Quang and Tran, Khanh Quoc and Nguyen, Kiet Van},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={39},
+  number={1},
+  pages={308--316},
+  year={2025},
+  doi={10.1609/aaai.v39i1.32008}
+}
+```

aggregate_metrics.json

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+{
+  "test_macro_f1_mean": 0.43072497219901523,
+  "test_macro_f1_std": 0.3172682160043997,
+  "test_macro_f1_text": "0.4307 ± 0.3173",
+  "test_accuracy_mean": 0.5124309392265193,
+  "test_accuracy_std": 0.23844675975737228,
+  "test_accuracy_text": "0.5124 ± 0.2384",
+  "test_macro_precision_mean": 0.4407776401346372,
+  "test_macro_precision_std": 0.3344139011625868,
+  "test_macro_precision_text": "0.4408 ± 0.3344",
+  "test_macro_recall_mean": 0.5052765890947056,
+  "test_macro_recall_std": 0.24511154523363937,
+  "test_macro_recall_text": "0.5053 ± 0.2451",
+  "dev_macro_f1_mean": 0.42530896473146323,
+  "dev_macro_f1_std": 0.3090730750099172,
+  "dev_macro_f1_text": "0.4253 ± 0.3091",
+  "task": "ViFactCheck-gold-evidence",
+  "dataset": "ViFactCheck",
+  "model_key": "xlmr",
+  "model_name": "XLM-R-base",
+  "base_model": "xlm-roberta-base",
+  "seeds": [
+    22,
+    42,
+    202
+  ],
+  "representative_seed": 22,
+  "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
+}

artifacts/per_seed_results.csv

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artifacts/seed_202_classification_report.json

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+{
+  "supported": {
+    "precision": 0.44854881266490765,
+    "recall": 0.6746031746031746,
+    "f1-score": 0.5388272583201268,
+    "support": 252.0
+  },
+  "refuted": {
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+    "recall": 0.4810126582278481,
+    "f1-score": 0.40353982300884955,
+    "support": 237.0
+  },
+  "not_enough_information": {
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+    "recall": 0.03404255319148936,
+    "f1-score": 0.06349206349206349,
+    "support": 235.0
+  },
+  "accuracy": 0.40331491712707185,
+  "macro avg": {
+    "precision": 0.4222326745229272,
+    "recall": 0.3965527953408374,
+    "f1-score": 0.3352863816070133,
+    "support": 724.0
+  },
+  "weighted avg": {
+    "precision": 0.4226443194823019,
+    "recall": 0.40331491712707185,
+    "f1-score": 0.34025420175470195,
+    "support": 724.0
+  }
+}

artifacts/seed_202_confusion_matrix.csv

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+,pred_0,pred_1,pred_2
+true_0,170,80,2
+true_1,116,114,7
+true_2,93,134,8

artifacts/seed_202_run_metadata.json

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+{
+  "status": "completed",
+  "task": "ViFactCheck-gold-evidence",
+  "dataset": "ViFactCheck",
+  "model_key": "xlmr",
+  "model_name": "XLM-R-base",
+  "model_id": "xlm-roberta-base",
+  "base_revision": "e73636d4f797dec63c3081bb6ed5c7b0bb3f2089",
+  "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": 16,
+  "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-gold-evidence/xlmr/seed-202/trainer_mb8_ga1/checkpoint-2172",
+  "best_metric": 0.3345177021784036,
+  "best_model_dir": "/content/EACL_2027_ViFactCheck/runs_ml256_bs8_a10080_fast3/vifactcheck-gold-evidence/xlmr/seed-202/best_model",
+  "train_loss": 1.1024757792716966,
+  "wall_seconds": 419.8790616989136,
+  "dev_accuracy": 0.4107883817427386,
+  "dev_macro_precision": 0.40196078431372556,
+  "dev_macro_recall": 0.40320191538362216,
+  "dev_macro_f1": 0.3345177021784036,
+  "dev_weighted_f1": 0.33976344316063956,
+  "test_accuracy": 0.40331491712707185,
+  "test_macro_precision": 0.4222326745229272,
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+  "test_macro_f1": 0.3352863816070133,
+  "test_weighted_f1": 0.34025420175470195,
+  "completed_at_utc": "2026-07-23T08:15:03.489810+00:00"
+}

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