diacnet-1.0 restores diacritics/accents to text that's been typed or scraped
without them, across 10 languages. Fine-tuned from google/byt5-small —
character/byte-level rather than word-level, so it handles Yoruba tone marks,
Vietnamese combining diacritics, and Polish/Turkish special characters through
the same mechanism, no per-language vocabulary needed.
Single joint model, all 10 languages — a language tag prefix (<yor>,
<vie>, etc.) tells the model which diacritic inventory to apply, no
separate models or an upstream language-ID step required.
Median CER of ~0.02 across most languages (see Benchmarks) — near-perfect
restoration on well-formed input.
Fully self-supervised training — no manual annotation. Clean, already-
diacritized text is the target; diacritics are deterministically stripped to
create the training input.
Languages: yo vi ig ha pl tr pt es fr it (ISO 639-1; ISO 639-3: yor vie ibo hau pol tur por spa fra ita)
Scoped deliberately to languages where diacritics are lexically meaningful —
not applied to the other 15 languages in the source corpus (e.g. Swahili,
Zulu, Amharic, Japanese), where diacritic restoration either doesn't apply or
isn't the right frame for the script.
🏃 Usage
python
1from transformers import AutoTokenizer, T5ForConditionalGeneration
23tok = AutoTokenizer.from_pretrained("olaverse/diacnet-1.0")4model = T5ForConditionalGeneration.from_pretrained("olaverse/diacnet-1.0")56text ="<yor> se eranko naa si gbo o?"7inputs = tok(text, return_tensors="pt")8output_ids = model.generate(**inputs, max_new_tokens=256)9print(tok.decode(output_ids[0], skip_special_tokens=True))10# ṣé ẹranko náà sì gbọ́ ọ?
Prefix input text with the target language tag (<yor>, <vie>, <ibo>,
<hau>, <pol>, <tur>, <por>, <spa>, <fra>, <ita>). Works best on
single sentences or short passages — see Known Limitations for longer text.
📊 Benchmarks
External evaluation on out-of-distribution test data — not our training
distribution. ~1,000 diacritic-bearing test sentences per language:
Tatoeba (es/fr/it/pt/pl/tr/vi), MasakhaNEWS (ig/ha), MENYO-20k test split
(yo). Sentences were diacritic-stripped to form inputs; the original text
is the reference.
Systems: diacnet-1.0 (this model — one joint model, all 10 languages)
vs. copy-input (the undiacritized text unchanged — the floor).
WER — word error rate (%, lower is better)
system
es
fr
it
pt
pl
tr
vi
ig
yo
ha
copy-input (floor)
17.38
18.19
14.16
19.47
40.01
42.76
86.25
34.11
87.31
7.80
diacnet-1.0
2.07
1.45
0.62
1.61
13.18
19.49
21.60
8.00
29.08
8.27 ⚠️
CER — character error rate (%, lower is better)
system
es
fr
it
pt
pl
tr
vi
ig
yo
ha
copy-input (floor)
3.13
3.61
2.49
3.65
7.51
9.57
23.01
9.28
36.37
1.58
diacnet-1.0
0.38
0.28
0.11
0.30
2.53
4.04
8.02
2.16
12.44
2.13 ⚠️
ChrF (higher is better)
system
es
fr
it
pt
pl
tr
vi
ig
yo
ha
copy-input (floor)
87.08
85.51
89.80
85.43
72.40
69.45
27.21
69.10
16.93
93.49
diacnet-1.0
98.41
98.89
99.53
98.75
91.11
85.87
77.65
92.61
68.90
93.28 ⚠️
Reading the results honestly
diacnet-1.0 beats the do-nothing floor on 9 of 10 languages, often
by a wide margin: Italian WER 14.2 → 0.6, French 18.2 → 1.5,
Vietnamese 86.3 → 21.6.
Yorùbá remains the hardest language at 29.1 WER — still well
above the Romance-language scores. A dedicated Yorùbá-only specialist,
Davlan/mt5_base_yoruba_adr
(roughly 2× diacnet's size, trained on human-authored text), scored
16.4 WER on an earlier run of this same test set — still ahead of us.
Yorùbá tone restoration is largely a semantic disambiguation problem:
the same base letters can spell several different words (e.g. ogun
war / ògùn medicine / ogún twenty), so closing this gap needs more
human-authored Yorùbá training data, not just more parameters.
Hausa: restoration currently does not work (⚠️). diacnet scores
worse than doing nothing (8.3 vs 7.8 WER). With only ~15 Hausa examples
in training, the model never learned the hooked-letter inventory
(ɓ ɗ ƙ) and largely passes text through unrestored. Do not use
diacnet-1.0 for Hausa; fixing this is the top priority for 1.1.
The earlier ~0.02 median CER figure came from a held-out slice of our
own training distribution; the numbers above, on external text, are
the ones to trust.
Evaluation code and test sets are open — see DiacBench
(olaverse/diacbench)
— so these numbers are reproducible and other models can be compared on
the same footing.
🔜 Toward diacnet-1.1
Hausa — train on adequate Hausa data (Wura, MasakhaNEWS); target:
clearly beat the copy-input floor.
Yorùbá — add human-authored training text (e.g. MENYO-20k train
split) to close the gap to the monolingual specialist.
Turkish/Vietnamese — remaining errors concentrate in lexically
ambiguous cases; larger-capacity variants under evaluation.
Training data & licensing
Fine-tuned from google/byt5-small (Apache-2.0) on
olaverse/qg-passages-multi
(Apache-2.0), split into sentences and paired with a diacritic-stripped copy
of each sentence as the self-supervised training input. Released under
Apache-2.0.
Citation
@misc{diacnet-1.0,
title = {diacnet-1.0},
author = {Olaverse},
year = {2026},
url = {https://huggingface.co/olaverse/diacnet-1.0}
}
