vi-ner-videberta

vi-ner-videberta

ViDeBERTa token-classification model for Vietnamese bank-statement OCR. It extracts PERSON, ORGANIZATION, and ADDRESS; the address label is deliberately retained to disambiguate names inside administrative addresses (for example, a person-like street or ward name) from actual people and organizations.

Evaluation

Trainer token/window BIO metrics

These legacy Trainer summaries are computed over tokenized windows. They are reported separately from final decoded document-span exact-match metrics.

Per-entity F1

The training run did not record a public dashboard URL. Local TensorBoard event files are included under tensorboard/ when present.

Training data

Split sizes: test: 29,587, train: 331,468, validation: 31,809.

This checkpoint was trained on a mixed-source dataset that includes restricted or non-redistributable examples. The source rows are not uploaded with this model. Review every upstream license and usage condition before commercial use or redistribution.

Reproducibility

• Base checkpoint: Fsoft-AIC/videberta-base
• Base revision: d72e04d6e5065b2babb6bccad6f86960701f0572
• Seed: 20260927
• Labels: O, B-PERSON, I-PERSON, B-ORGANIZATION, I-ORGANIZATION, B-ADDRESS, I-ADDRESS

Intended use and limitations

The intended unit is one OCR-extracted bank-statement transaction line. OCR errors, abbreviations, unseen bank formats, and names that are also locations can reduce accuracy. Predictions should support downstream analysis, not replace review for legal, compliance, identity, or payment decisions.