pandakingpunc/megazeka-tr-spellfix-pairs

Megazeka · Turkish Spelling Correction Pairs

40,313 (misspelled → correct) Turkish sentence pairs with a full record of every corruption that was applied, generated from the CC0 Common Voice Turkish Sentence Collector and from project-authored everyday first/second-person sentences.

The distinguishing feature is the operations field: each pair carries the exact sequence of noise transformations that produced it, with before/after text at each step. That makes it possible to evaluate a corrector per error type — how well does it handle diacritic loss versus merged words versus chat-style spellings like gidicem? — rather than only in aggregate.

Used to train pandakingpunc/megazeka-byt5-tr-spellfix v2. Generation code: github.com/pandakingpunc/megazeka (training/prepare.py, training/everyday.py, src/megazeka/noise.py, src/megazeka/colloquial.py).

What's new in v2

v1 (tag v1) had 33,280 pairs from Common Voice only. Those sentences are literary and mostly third person — gideceğim appeared 8 times in 32,000 training pairs — so a model trained on them never learned colloquial first-person forms. v2 adds:

• Everyday sentences (CC0-1.0, written for this project): template sentences from a 50-verb table and a small conjugator (future, present continuous, negative, question suffix; vowel harmony and the git/et/ye/de special cases) plus ~250 hand-written short sentences, ~1,470 in total.
• Suffix-based colloquial noise, applied to any verb rather than a fixed word list (-eceğim → -icem/-cem, -yorum → -yom, gideceğim → gitcem, burada → burda, bir şey → bişey, …). The gündelik operation count in train went from 1,001 to 8,178.
• Two-sentence chunks in train: two independent pairs of the same difficulty joined with a space, at most 170 bytes.
• Larger validation and test splits (1,128 pairs each) with an everyday subset.

The 160 Common Voice test target sentences are the same as in v1, but the noise generator changed, so most corrupted inputs differ.

Splits

Split	Pairs	Distinct targets	Common Voice	Everyday	Two-sentence
train	38,057	9,226	32,000	4,904	1,153
validation	1,128	282	640	488	—
test	1,128	282	640	488	—

Splitting happens on canonical target form (NFC + Turkish lowercase + alphanumeric key) before noise is applied, so no variant of the same sentence crosses splits. Of the ~1,470 everyday sentences, 1/12 go to validation, 1/12 to test and the rest to train. Seed 42.

Schema

{
  "target": "Yarın sabah ben evde kalıyorum.",
  "source": "Megazeka gündelik şablon / CC0-1.0",
  "input": "Yarın sabah benevde kalıyom.",
  "difficulty": "orta",
  "operations": [
    {"operation": "gündelik",  "before": "Yarın sabah ben evde kalıyorum.",
                               "after":  "Yarın sabah ben evde kalıyom."},
    {"operation": "boşluk_sil", "before": "Yarın sabah ben evde kalıyom.",
                               "after":  "Yarın sabah benevde kalıyom."}
  ],
  "group_id": "5864952e584f92f7"
}

• target — correct sentence
• input — corrupted sentence, identical to target when difficulty is temiz
• difficulty — temiz (clean) / kolay (easy) / orta (medium) / zor (hard), targeting 0 / 1 / 2 / 4 successfully applied operations
• source — Common Voice / CC0-1.0, Megazeka gündelik şablon / CC0-1.0 (everyday) or Birleşik iki cümle (two-sentence chunk)
• group_id — canonical target hash; shared by the four variants of one sentence
• group_id_second — only on two-sentence chunks: the group_id of the second sentence
• operations — ordered list of applied corruptions with before/after text; on two-sentence chunks the operations of both halves are listed, each with the text of its own half

Difficulty levels are balanced (≈9,500 pairs each in train). 25% of pairs are clean, so a model trained on this data learns to leave correct text alone. For easy and medium everyday pairs the first operation is always gündelik. Rare multi-operation corruptions can cancel each other out — when measuring overcorrection, count every example where input == target (9,574 in train, 282 in test) rather than trusting the temiz label.

Noise operations

Counts over the training split, including two-sentence chunks (a pair may apply several):

Operation	Count	What it does
türkçe_karakter	14,515	Diacritic loss: ı→i, ş→s, ğ→g, ç→c, ö→o, ü→u
gündelik	8,178	Colloquial spellings (gidicem, yapcaz, bilmiyom, burda, bişey, …)
küçük_harf	7,950	Capitalization errors
klavye	4,353	Turkish Q-keyboard neighbour key
fazla_boşluk	4,334	Extra whitespace
boşluk_sil	4,265	Merged words
noktalama_ekle	4,260	Spurious punctuation
boşluk_ekle	4,245	Split word
harf_sil	4,178	Dropped letter
harf_tekrar	4,165	Repeated letter
noktalama_sil	3,924	Missing punctuation
harf_yer_değiştir	3,434	Transposed adjacent letters
soru_eki	355	Question suffix wrongly attached
de_da	199	de/da clitic merged with the word
ki	130	ki clitic merged with the word
kesme	103	Missing apostrophe before a suffix

Source and filtering

• Common Voice Turkish Sentence Collector at pinned commit b12cde653757295103c5948c1cd601b166e1b905 (1,557,129 bytes, 47,781 lines, SHA-256 28e2d095…4058ece). No audio or dev corpus was downloaded. Sentences were kept if they are 3–24 words, 18–160 UTF-8 bytes, start with a capital letter and end with punctuation, then deduplicated on a canonical key.
• Everyday sentences, generated by training/everyday.py in the GitHub repository and released here under CC0-1.0.

Per-file SHA-256 checksums for the generated splits are in metadata.json.

Limitations

• Synthetic. These are programmatically corrupted sentences, not errors real people made. A model that scores well here may do noticeably worse on genuine user text.
• Operation frequencies reflect the generator's weights, not measured Turkish error frequencies.
• The everyday sentences come from 50 verbs and a limited set of complements; they do not represent real chat language in full, and the test everyday subset is from the same template family as train.
• Common Voice sentences may themselves contain overlooked mistakes; passing the filter is not evidence of grammatical correctness.
• Sentence-length only (plus two-sentence chunks). No long documents, lists, code, or mixed-language text.
• Split separation is on canonical sentence form. Semantically similar sentences — including template sentences that share a verb or complement — may still be split across sets, and overlap with any given model's pretraining corpus is unknown.

Versions

Version	Revision	Pairs (train / val / test)	Sources
v2	main, tag v2	38,057 / 1,128 / 1,128	Common Voice + everyday + two-sentence chunks
v1	tag v1	32,000 / 640 / 640	Common Voice only

Load a specific version with load_dataset("pandakingpunc/megazeka-tr-spellfix-pairs", revision="v1").

Licensing

Common Voice source sentences are CC0-1.0 (Common Voice licensing); the everyday sentences were written for this project and are also released under CC0-1.0, as are all derived pairs. No Common Voice program code (MPL-2.0) is included here. The generation code is MIT, in the GitHub repository.