Hailay/MachineT_TigEng

English–Tigrinya Machine Translation & Tokenizer

📌 Conference

Accepted at the 3rd International Conference on Foundation and Large Language Models (FLLM2025)
📍 25–28 November 2025 | Vienna, Austria

Paper Title: Low-Resource English–Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks

---

📝 Model Summary

This repository provides a custom tokenizer and a fine-tuned MarianMT model for English ↔ Tigrinya machine translation.
It leverages the NLLB dataset for training and OPUS parallel corpora for testing and evaluation, with BLEU used as the primary metric.

• Languages: English (eng), Tigrinya (tig)
• Tokenizer: SentencePiece, customized for Geez-script representation
• Model: MarianMT (multilingual transformer) fine-tuned for English–Tigrinya translation
• License: MIT

---

🔍 Model Details

Tokenizer

• Type: SentencePiece-based subword tokenizer
• Purpose: Handles Geez-script specific tokenization for Tigrinya
• Training Data: NLLB English–Tigrinya subset
• Evaluation Data: OPUS parallel corpus

Translation Model

• Base Model: MarianMT
• Frameworks: Hugging Face Transformers, PyTorch
• Task: Bidirectional English ↔ Tigrinya MT

---

⚙️ Training Details

• Training Dataset: NLLB Parallel Corpus (English ↔ Tigrinya)
• Testing Dataset: OPUS Parallel Corpus
• Epochs: 3
• Batch Size: 8
• Max Sequence Length: 128 tokens
• Learning Rate: 1.44e-07 with decay

Training Loss

• Epoch 1: 0.443
• Epoch 2: 0.4077
• Epoch 3: 0.4379
• Final Loss: 0.4756

Gradient Norms

• Epoch 1: 1.14
• Epoch 2: 1.11
• Epoch 3: 1.06

Performance

• Training Time: ~12 hours (43,376.7s)
• Speed: 96.7 samples/sec | 12.08 steps/sec

---

📊 Evaluation

• Metric: BLEU score
• Evaluation Dataset: OPUS parallel English–Tigrinya

---

🚀 Usage

This model can be directly used for English → Tigrinya and Tigrinya → English translation.

Example (Python)

from transformers import MarianMTModel, MarianTokenizer

# Load the model and tokenizer
model_name = "Hailay/MachineT_TigEng"  
model = MarianMTModel.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)

# Translate English → Tigrinya
english_text = "We must obey the Lord and leave them alone"
inputs = tokenizer(english_text, return_tensors="pt", padding=True, truncation=True)
translated = model.generate(**inputs)
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)

print("Translated text:", translated_text)



##  📌Citation

If you use this model or tokenizer in your work, please cite:

@inproceedings{hailay2025lowres,
  title     = {Low-Resource English–Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks},
  author    = {Hailay Kidu and collaborators},
  booktitle = {Proceedings of the 3rd International Conference on Foundation and Large Language Models (FLLM2025)},
  year      = {2025},
  location  = {Vienna, Austria}
}