---
language:
- arc
tags:
- diacritization
- aramaic
- vocalization
- targum
- semitic-languages
- sequence-to-sequence
license: mit
base_model: Helsinki-NLP/opus-mt-afa-afa
library_name: transformers
---

# Aramaic Targum Diacritization (Vocalization) MarianMT Model

This model fine-tunes the Helsinki-NLP/opus-mt-afa-afa MarianMT model for the task of **Aramaic diacritization**: adding nikkud (vowel points) to consonantal Aramaic Targum text. The model is trained on a parallel corpus of consonantal and fully vocalized Aramaic, both in Hebrew script.

## Model Details

- **Model Name**: `johnlockejrr/opus-arc-targum-vocalization`
- **Base Model**: [Helsinki-NLP/opus-mt-afa-afa](https://huggingface.co/Helsinki-NLP/opus-mt-afa-afa)
- **Task**: Aramaic diacritization (consonantal → vocalized)
- **Script**: Hebrew script (consonantal and vocalized)
- **Domain**: Targumic/Biblical Aramaic
- **License**: MIT

## Dataset

- **Source**: Consonantal Aramaic Targum text (no nikkud)
- **Target**: Fully vocalized Aramaic Targum text (with nikkud)
- **Format**: CSV with columns `consonantal` (input) and `vocalized` (target)
- **Alignment**: Verse-aligned or phrase-aligned

## Training Configuration

- **Base Model**: Helsinki-NLP/opus-mt-afa-afa
- **Batch Size**: 8 (per device, gradient accumulation as needed)
- **Learning Rate**: 1e-5
- **Epochs**: 100 (typical)
- **FP16**: Enabled
- **No language prefix** (single language, Aramaic)
- **Tokenizer**: MarianMT default
- **Max Input/Target Length**: 512

## Usage

### Inference Example

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "johnlockejrr/opus-arc-targum-vocalization"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Diacritize consonantal Aramaic
consonantal = "בקדמין ברא יי ית שמיא וית ארעא"
inputs = tokenizer(consonantal, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(**inputs, max_length=512, num_beams=4)
vocalized = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Vocalized: {vocalized}")
```

## Intended Use

- **Primary**: Automatic diacritization (vocalization) of Aramaic Targum text
- **Research**: Useful for digital humanities, Semitic linguistics, and textual studies
- **Education**: Can assist in language learning and textual analysis

## Limitations

- **Context**: The model is trained at the phrase/verse level and does not have document-level context
- **Domain**: Optimized for Targumic/Biblical Aramaic; may not generalize to other dialects
- **Orthography**: Input must be consonantal Aramaic in Hebrew script
- **Ambiguity**: Some words may have multiple valid vocalizations; the model predicts the most likely

## Citation

If you use this model, please cite:

```bibtex
@misc{opus-arc-targum-vocalization,
  author = {John Locke Jr.},
  title = {Aramaic Targum Diacritization (Vocalization) MarianMT Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face model repository},
  howpublished = {\url{https://huggingface.co/johnlockejrr/opus-arc-targum-vocalization}},
}
```

## Acknowledgements

- **Targumic Aramaic sources**: Public domain or open-access editions
- **Helsinki-NLP**: For the base MarianMT model

## License

MIT