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.

Metric	Validation	Test
precision	0.895407	-
recall	0.908239	-
f1	0.901777	-
accuracy	0.984443	-

Per-entity F1

Entity	Validation	Test
PERSON	0.891723	-
ORGANIZATION	0.889339	-
ADDRESS	0.922978	-

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.

Source	Rows
curated_ambiguity	54
curated_statement_patterns	10
llm_statement_synthetic	1,955
llm_statement_wave03	324
llm_statement_wave04	410
llm_statement_wave05	63
llm_statement_wave06	58
llm_statement_wave07	102
llm_statement_wave09	32
llm_statement_wave10	33
llm_statement_wave11	29
llm_statement_wave12	32
llm_statement_wave13	24
llm_statement_wave14	16
llm_statement_wave15	3
llm_statement_wave16	15
llm_statement_wave17	10
llm_statement_wave18	8
llm_statement_wave19	11
llm_statement_wave20	3
llm_statement_wave21	12
llm_statement_wave22	4
llm_statement_wave23	12
llm_statement_wave24	3
llm_statement_wave25	11
llm_statement_wave26	1
llm_statement_wave27	12
llm_statement_wave28	1
llm_statement_wave29	2
llm_statement_wave30	1
llm_statement_wave31	6
llm_statement_wave33	2
llm_statement_wave34	4
llm_statement_wave35	2
llm_statement_wave37	3
llm_statement_wave38	5
llm_statement_wave39	13
llm_statement_wave40	4
llm_statement_wave42	4
llm_statement_wave44	3
llm_statement_wave45	3
llm_statement_wave46	2
masothue_llm_wave03	107
masothue_llm_wave04	72
masothue_statement_augmented	34,998
masothue_synthetic	119,794
meddies_pii_vi	15,212
news_ner	2,973
pap_ner	34,103
phoner_covid19	9,955
private_statement_csv_llm	7,778
private_statement_ocr_llm	5,769
statement_hard_cases	344
vietnamnet_gold	8,396
vlsp2016	16,763
vlsp2021_ndtands	19,329
wikiann_vi	27,364

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.