---
library_name: ctranslate2
pipeline_tag: translation
license: apache-2.0
base_model: protonx-models/distilled-protonx-legal-tc
language:
  - vi
tags:
  - ctranslate2
  - ct2
  - text2text-generation
  - vietnamese
  - ocr-correction
  - post-ocr
---

# distilled-protonx-legal-tc — CTranslate2 build (Vietnamese OCR text correction)

CTranslate2-converted version of [`protonx-models/distilled-protonx-legal-tc`](https://huggingface.co/protonx-models/distilled-protonx-legal-tc), optimised for fast CPU OCR text correction on Vietnamese administrative documents.

The upstream `distilled-protonx-legal-tc` is a smaller student distilled from `protonx-legal-tc`. This repo only does the CT2 conversion + INT8 quantization — no further training. Used by the [ScanIndex](https://github.com/welcomyou/scanindex) pipeline as the correction stage between OCR and PDF/DOCX export.

## Performance (ScanIndex internal benchmark, 13-page Vietnamese admin doc)

| Variant | Time | Accuracy |
|---|---|---|
| `protonx-legal-tc` — CT2 OPTIMIZE, beam=1 | 14.5s | 99.561% |
| **This repo** (`distilled-protonx-legal-tc` CT2 INT8, beam=1) | **8.3s** | **99.550%** |

42% faster, 0.011 pp accuracy drop — recommended trade-off for CPU.

## Files

- `distilled_ct2/model.bin` — CTranslate2 model
- `distilled_ct2/tokenizer.json`, `tokenizer_config.json`, `special_tokens_map.json`, `shared_vocabulary.json`, `config.json`

## Loading

```python
import ctranslate2
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download

local = snapshot_download("welcomyou/distilled-protonx-vn-correction-ct2", local_dir="models")
sub = f"{local}/distilled_ct2"
translator = ctranslate2.Translator(sub, device="cpu")
tok = AutoTokenizer.from_pretrained(sub)
```

## License

Apache-2.0, inheriting from the protonx-legal-tc base.