Vintern_1B_v2_finetune_lora_viet_chart_vqa

Results

The Vintern-1B-v2 model was fine-tuned on the maianh511/vi_chart_dataset dataset, showing clear improvements across all metrics compared to the base (pretrain) model.

1. Metric Comparison — Pretrain vs Fine-tuned

The fine-tuned model outperforms the base model on every metric, most notably on BLEU (+52.9%) and METEOR (+27.0%) — indicating a substantially better ability to generate Vietnamese answers that closely match the ground truth, both lexically and semantically. BERTScore also improves despite already starting from a high baseline (0.85 → 0.90), showing further gains in semantic quality even though the pretrain model already had a solid foundation.

2. Training Loss Curve

Conclusion

Fine-tuning Vintern-1B-v2 on maianh511/vi_chart_dataset yields consistent improvements across all evaluation metrics, demonstrating the effectiveness of the Vietnamese chart dataset in enhancing the model's ability to understand and answer chart-related questions in Vietnamese.