vn-register-phobert-base

4-class Vietnamese text register classifier — labels each input as one of formal (administrative / legal) / business (news / encyclopaedic) / conversational (chat / forum) / literary (classical narrative). Use the predicted register to route a text through the right downstream checkpoint — VN diacritic / summarization / OCR-rerank checkpoints all spread 5-10 pp accuracy across registers, so a cheap router lifts every other tool automatically.

PhoBERT-base backbone (135 M params, MIT, VinAI lab) fine-tuned with a 4-way sequence-classification head. Word-segmented input via nom.text.word_tokenize per the BKai gotcha (raw text drops accuracy ≥ 15 pp on PhoBERT-style models).

Measured on test split

In-house bench, 2026-05-03, n=1234 stratified test split (CUDA, BF16):

Class	Precision	Recall	F1	Support
formal	0.889	0.941	0.914	34
business	0.885	0.928	0.906	400
conversational	0.887	0.945	0.915	400
literary	0.924	0.815	0.866	400
macro avg	0.896	0.907	0.900	1234

Both adoption gates clear: macro F1 ≥ 0.85, every per-class F1 ≥ 0.75.

Source benchmarks/accuracy/register_phobert_base_baseline.json re-runnable from a clean clone via training/register/train.py.

Corpus

Source-provenance labelling — no human annotators needed. Every sentence inherits its register label from the source corpus it came from:

Label	Source(s)	License
formal	UDHR-vi (PD) + UDHR diacritic-eval slice (PD)	Public Domain
business	wiki_vi (Wikipedia VN extracts)	CC-BY-SA-4.0
conversational	tatoeba_vi 3k + tatoeba diacritic-eval-300	CC-BY 2.0 FR
literary	wikisource_vi (PD) + UD-VTB train/dev/test	PD + CC-BY-SA-4.0

Class imbalance — formal only has 169 unique sentences (UDHR is naturally short), the others cap at 2 000 each. The 134 / 169 / 169 / 169 split per register survives the imbalance (model still hits 0.91 F1 on formal — class weighting wasn't needed). Future v2: add a permissive VN legal corpus to grow formal.

Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tok = AutoTokenizer.from_pretrained("nrl-ai/vn-register-phobert-base")
model = AutoModelForSequenceClassification.from_pretrained("nrl-ai/vn-register-phobert-base")

# IMPORTANT: PhoBERT requires word-segmented input (multi-syllable words
# joined with underscores). Use VnCoreNLP's RDRSegmenter or nom.text:
from nom.text import normalize, word_tokenize
text = "Doanh thu công ty quý 2 năm 2026 tăng 18 %."
segmented = " ".join(t.replace(" ", "_") for t in word_tokenize(normalize(text)))

import torch
ids = tok(segmented, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    logits = model(**ids).logits
probs = torch.softmax(logits, dim=-1).squeeze().tolist()
labels = ["formal", "business", "conversational", "literary"]
for label, p in sorted(zip(labels, probs), key=lambda x: -x[1]):
    print(f"  {label:<15} {p:.3f}")
# →   business        0.952
#     formal          0.029
#     conversational  0.012
#     literary        0.007

Or via the nom-vn wrapper:

from nom.classify import PhoBertRegisterClassifier
clf = PhoBertRegisterClassifier()  # default model_id = this repo
result = clf.predict("Doanh thu công ty quý 2 năm 2026 tăng 18 %.")
print(result.label, result.score, result.distribution)

Honesty notes

• Tested only on held-out 20 % of source-provenance corpus. A sentence labelled "formal" because it came from UDHR is a single data-distribution slice — real-world legal prose may shift. A genre classifier this scale needs cross-corpus eval (e.g. legal_vi from th1nhng0) before adoption claims beyond routing.
• literary recall 0.815 < precision 0.924. When the model says "literary," it's usually right; when something IS literary, the model misses ~18 % to other classes (mostly to formal because of shared archaic vocab). Acceptable for routing — false negatives fall back to a sensible default.
• Word-segmentation matters. Skip word_tokenize and accuracy drops 10-15 pp. The wrapper handles this; if you call the model directly, you must segment.

Citation

@misc{nguyen_vn_register_phobert_base_2026,
  author = {Nguyen, Viet-Anh and {Neural Research Lab}},
  title  = {{vn-register-phobert-base: A 4-class Vietnamese text-register
             classifier (formal / business / conversational / literary)}},
  year   = {2026},
  url    = {https://huggingface.co/nrl-ai/vn-register-phobert-base}
}

License: MIT (matches PhoBERT-base backbone).

Maintained as part of the nom-vn project by Viet-Anh Nguyen (vietanh@nrl.ai) and Neural Research Lab.