Akkhara-Mini-Khmer-ONNX (18M)

Akkhara-mini converts Sing Khmer (Khmer typed with Latin letters, as in chat) into Khmer script with a ranked top-5 list, like a typing assistant (IME). It is small and fast, made for phones and keyboards, and runs fully offline in the Akkhara keyboard.

English chat words stay as typed (benchmark, laptop, deadline), with the Khmer loan spelling in the top-5 when one exists (deadline → ដេតឡាញ, internet → អ៊ីនធឺណិត).

Benchmark

Both Akkhara models on the same tests (same beam search, 5 beams): a held-out test split (7,821 sentences), 39 real chat sentences typed and labeled by a native speaker, 86 context tests (the same chat word read from its neighbours, e.g. ng = និង / នឹង / ហ្នឹង) and 7 glued-word tests.

• Real chat and context words: about as good as the big model (85–87% right at #1, 81 of 86 context words), while being 16× smaller.
• Chat abbreviations: tmr → ស្អែក, sry → សុំទោស, gn → រាត្រីសួស្តី, btw → ម្យ៉ាងទៀត, wyd → ធ្វើអីហ្នឹង.
• Formal news-style text: the bigger muyleanging/Akkhara-ByT5-Small-Khmer is a little more accurate.
• CER = share of wrong letters (lower is better). Top-5 = the right sentence is among the 5 suggestions.

Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "muyleanging/Akkhara-Mini-Khmer-ONNX"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

inputs = tokenizer(["nh jg tv sala rean"], return_tensors="pt")
out = model.generate(**inputs, num_beams=5, num_return_sequences=5, max_new_tokens=160)
print(tokenizer.batch_decode(out, skip_special_tokens=True))
# ['ខ្ញុំចង់ទៅសាលារៀន', ...]

# fastest (typing): greedy #1 only
out = model.generate(**inputs, num_beams=1, max_new_tokens=160)

For long text, cut it into ~6-word chunks and join the results.

ONNX (phones, web, other languages)

The onnx/ folder holds an ONNX Runtime export: encoder.onnx + decoder.onnx, or the 8-bit encoder.int8.onnx + decoder.int8.onnx (18.5 MB, used in the keyboard), plus tokenizer.json and abbreviations.tsv (46 abbreviations → full Khmer name: the keyboards show mptc as typed at #1 and ក្រសួងប្រៃសណីយ៍និងទូរគមនាគមន៍ at #2; a plain list, not learned by the model). Run the encoder once per sentence, then the decoder once per Khmer letter (greedy or beam search). On a recent Android phone the int8 model gives the #1 suggestion in about 10 ms.

Model details

• Developed by: Ing Muyleang
• Model type: T5-style encoder–decoder, character tokens (6 encoder + 3 decoder layers, d_model 384)
• Parameters: 17.8M · License: Apache-2.0
• Language: Khmer (km); English chat words in mixed input stay English
• Training data: 398k Sing Khmer → Khmer pairs (incl. 134 Cambodian ministry, university and bank names), collected, generated, cleaned and checked by the author with AI tools (agy, Claude); see muyleanging/Akkhara-Sing-Khmer (access on request).
• Demo: muyleanging/Akkhara-Demo
• Keyboard app (APK): latest release

Limits

• Trained mostly on synthetic pairs; the real-chat test set is small (39 sentences).
• Short abbreviations stay ambiguous (ng, yt, dl, bn); the right answer is often in the top-5.
• It knows rude chat words (they occur in real chat).

Contact

Training details (hardware, time, settings) are not published. Questions: contact the author through the Hugging Face profile or GitHub.

Citation

@misc{ing2026akkharamini,
  author    = {Ing, Muyleang},
  title     = {Akkhara-mini: a small character-level Sing Khmer to Khmer script transliteration model},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/muyleanging/Akkhara-Mini-Khmer-ONNX}
}