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metadata
license: bsd-3-clause
language: ar
inference: false
tags:
  - secryst
  - transliteration
  - diacritization
  - onnx
  - byt5

ara-diac-2.0

Part of the Secryst phonological-layer family — a dedicated model for diacritization (AR), distributed as a Secryst IMF artifact and loadable through any Secryst crystal:

from secryst import Model
Model.load("ara-diac-2.0")
  • Artifact: ara-diac-2.0-fp32.zip (2.59 GiB)
  • Precision variants: ara-diac-2.0-int8

Measured quality

Full evaluation sets, zero skipped examples, the benchmark's own evaluator. Provenance for every number is the results record of the training project.

metric value provenance
der_total_greedy 2.2864 rababa/docs/RESULTS.md#r7-verdict-table-sadeeddiac-25-2026-08-28
der_morph_greedy 1.3343 rababa/docs/RESULTS.md#r7-verdict-table-sadeeddiac-25-2026-08-28
wer_wikinews_multiref 17.3794 rababa/docs/RESULTS.md#r7-ood-verdict-table-wikinews-2024-multiref-2026-08-28
der_wikinews_multiref 11.8273 rababa/docs/RESULTS.md#r7-ood-verdict-table-wikinews-2024-multiref-2026-08-28

Benchmark

SadeedDiac-25 — full 1,200-paragraph set, Misraj's evaluator, zero skipped examples, greedy decode (protocol-matched published numbers only):

system Total DER kind
Claude-3.7-Sonnet 1.39% frontier LLM
ara-diac-2.0 (this model) 2.29% dedicated, 580M
GLM-5.2 2.69% frontier LLM
Gemini-Flash-2.0 3.19% frontier LLM
GPT-4 3.86% frontier LLM
Sadeed-1.5B 7.29% leaderboard baseline

Word-final iʿrāb endings (the hardest component): 1.33% DER. Out-of-domain generalization (WikiNews-2024 multi-reference): 17.38 WER / 11.83 DER.

Architecture

ByT5-large-class seq2seq transformer (580M parameters), byte-level input/output (no tokenizer — id = byte + 3). Trained on classical and modern Arabic running text with a news-domain adaptation stage over a morphology-auxiliary curriculum. The canonical distribution is the Secryst IMF artifact (sha256-verified, parity-gated against the PyTorch reference); this repository mirrors those exact bytes.

License

BSD-3-CLAUSE

Provenance

Trained in the Secryst training monorepo and published through the interscript-ml distribution contract (models.yaml index, sha256-verified release assets, parity-gated artifacts). The canonical download channel is GitHub Releases; this repository is the Hugging Face mirror of the same bytes, pinned to the index revision.