spellmtei

SpellMtei — M1 (ByT5)

The best corrector from SpellMtei, a spelling corrector for Manipuri (Meetei) in the Meetei Mayek script. google/byt5-small fine-tuned as a denoiser on synthetic supervision: clean Meetei Mayek text is corrupted by an error model grounded in the script's structure and in Meetei phonology, and the model learns to reconstruct it.

Tokenizer-free (byte-level), so no vocabulary work for the script.

Anonymised for peer review. De-anonymised on acceptance.

Results — frozen synthetic test set (1,012 sentences, input CER 6.26%)

Removes 54% of the character error and lifts chrF by 6.4 points. Like the other SpellMtei models it over-corrects already-correct input (it rewrites 60% of clean sentences) — raising the clean-sentence rate in training is the clearest next step.

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tok = AutoTokenizer.from_pretrained("nyobemedoc/spellmtei")
model = AutoModelForSeq2SeqLM.from_pretrained("nyobemedoc/spellmtei")

text = "ꯑꯩꯈꯣꯏꯒꯤ ꯅꯥꯛꯇꯥ ꯍꯧꯖꯤꯛ ꯂꯔꯦ"          # dropped vowel sign
ids = tok(text, return_tensors="pt").input_ids
out = model.generate(ids, num_beams=4, max_length=1024)
print(tok.decode(out[0], skip_special_tokens=True))   # ... ꯍꯧꯖꯤꯛ ꯂꯩꯔꯦ

Feed one sentence at a time; NFC-normalise and collapse whitespace first.

Training

google/byt5-small (~300M params), 6 epochs, one RTX 6000 Ada, bf16, Adafactor, cosine schedule, max_len 1024, beam 4 at inference. Noise is resampled every epoch; 20% of training pairs are left uncorrupted.

Related artifacts

Error profile, frozen evaluation sets, pipeline code, and the other two correctors (a from-scratch character edit tagger and a statistical noisy channel): nyobemedoc/spellmtei (dataset).

License

To be finalised with the camera-ready release.