johnlockejrr/opus-arc-targum-vocalization

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
• 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

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:

@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