---
license: apache-2.0
datasets:
- TigreGotico/arabic_diacritized_text
language:
- ar
tags:
- arabic
- nlp
- diacritization
- tashkeel
- onnx
new_version: TigreGotico/rawi-ensemble
---

# Rawi V3 — Arabic Diacritizer (two-head gated)

A lightweight Arabic diacritizer (tashkeel) that **internalizes the gate** into a
single network. Successor to [`TigreGotico/rawi-v2`](https://huggingface.co/TigreGotico/rawi-v2).

## Credits

- **TigreGotico** — author: the corpus, the model, and the training notebook.
- **Mike Hansen** — ran the training on his GPUs.

## Architecture

A shared **bidirectional LSTM** encoder feeds two heads:

- **presence head** (`Linear(512 → 1)`, sigmoid) — decides **where** a mark goes
  (the gate);
- **value head** (`Linear(512 → 75)`, argmax) — decides **which** mark, given one.

`Embedding(236, 128)` → 2-layer `BiLSTM(256)` → {presence, value}. At inference a
mark is applied to a letter only where `sigmoid(presence) > 0.5`, then the value
head's class is attached. This is the single-network form of the two-model
"gate + value" ensemble (rawi-v2 gating rawi-v1) — one pass instead of two.

Normalization is NFD with symbols (`So`) dropped; the value head's 75 NFD-based
classes restore hamzas and the superscript alef. Vocabulary is identical to V2.

## Files

| file | what |
|---|---|
| `rawi_v3.onnx` | fp32 ONNX — two outputs `presence` (B,T) and `value` (B,T,75) |
| `rawi_v3.int8.onnx` | INT8 dynamic quantization (~2.5 MB) |
| `rawi_v3.vocab.json` | `char_to_idx` / `diac_to_idx` (shared with V2) |
| `diacritization_model_lstm_3.pth` | original PyTorch checkpoint |
| `rawi_v3_train.ipynb` | training notebook (two-stage model) |

## Usage

```python
from text2tashkeel import Diacritizer        # pip install text2tashkeel
Diacritizer("rawi-v3").diacritize("بسم الله الرحمن الرحيم")
# بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ
```

`text2tashkeel` bundles this model and runs it in pure onnxruntime (no PyTorch at
inference); full benchmarks and the where/which gating analysis are in that repo.