mt5_ossetian_translator

A fine-tuned version of google/mt5-small for Ossetian language translation. This model is designed to assist with translating text to/from Ossetian (Ирон ӕвзаг), a Northeastern Iranian language spoken primarily in North and South Ossetia.

⚠️ Experimental Model: This is an early-stage model with limited performance (BLEU: 0.0809). Results should be validated before use in production. Contributions and feedback are welcome!

🌐 Language Support

Direction	Source → Target	Notes
Primary	English → Ossetian	Trained on available parallel data
Secondary	Ossetian → English	May require task prefix

Note: mT5 supports 101 languages. This fine-tune focuses on Ossetian pairs present in the training data.

🚀 Quick Start

Installation

pip install transformers sentencepiece torch

Inference Example

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "ajsbsd/mt5_ossetian_translator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Translate English → Ossetian
input_text = "translate English to Ossetian: Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt", padding=True)

outputs = model.generate(**inputs, max_length=128)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)

Using the pipeline API

from transformers import pipeline

translator = pipeline("translation", model="ajsbsd/mt5_ossetian_translator")
result = translator("translate English to Ossetian: Welcome to Ossetia.")
print(result[0]['translation_text'])

📊 Model Performance

Metric	Value	Notes
BLEU	0.0809	Evaluated on held-out test set
Validation Loss	2.5350	Final epoch
Training Steps	1,130	Over 10 epochs

Training History

Epoch	Train Loss	Val Loss	BLEU
1	2.4300	2.6400	0.0903
5	1.6915	2.5980	0.0760
10	1.6618	2.5350	0.0809

Full training logs available in the model card metadata.

📚 Training Details

Hyperparameters

base_model: google/mt5-small
learning_rate: 5e-4
batch_size: 8 (train/eval)
optimizer: AdamW (fused, betas=(0.9, 0.999))
lr_scheduler: linear
epochs: 10
seed: 42
max_length: 128

Dataset

• Source: Pontoon-Translations (community-contributed parallel corpus)
• Preprocessing: Tokenized with mT5 SentencePiece tokenizer (128k vocab)
• Train/Val/Test Split: Determined by dataset provider

🔍 Dataset transparency: If you are the dataset maintainer, consider adding documentation about source, licensing, and language coverage to improve reproducibility.

⚠️ Limitations & Biases

1. Low BLEU score (0.08) indicates limited fluency/accuracy—suitable for research, prototyping, or low-stakes applications only.
2. Data scarcity: Ossetian is a low-resource language; training data volume and quality directly impact performance.
3. Domain bias: Model reflects topics/domains present in Pontoon-Translations (likely software/UI strings).
4. No human evaluation: Metrics are automated; real-world quality may vary.
5. Prefix sensitivity: mT5 requires task prefixes (e.g., "translate English to Ossetian: ") for optimal results.

💡 Intended Use Cases

✅ Appropriate:

• Research on low-resource language translation
• Prototyping Ossetian-language NLP tools
• Educational projects and linguistic exploration
• Community-driven language preservation efforts

❌ Not Recommended:

• Production translation services without human review
• Legal, medical, or high-stakes content translation
• Applications requiring high fluency or cultural nuance

🤝 Contributing & Improving This Model

This model is a starting point. Ways to help improve it:

• 📥 Add data: Contribute high-quality Ossetian parallel sentences to the dataset
• 🔁 Retrain: Fine-tune with more epochs, larger batch size, or curriculum learning
• 🎯 Task adaptation: Add prefixes for specific domains (e.g., "translate technical: ...")
• 📝 Evaluate: Share human evaluation results or error analysis
• 🔄 Back-translation: Augment training data using synthetic Ossetian text

Open an issue or PR on the model repo to collaborate.

📄 Citation

If you use this model, please cite:

@article{xue2020mt5,
  title={mT5: A massively multilingual pre-trained text-to-text transformer},
  author={Xue, Linting and Constant, Noah and Roberts, Adam and Kale, Mihir and Al-Rfou, Rami and Siddhant, Aditya and Barua, Aditya and Raffel, Colin},
  journal={arXiv preprint arXiv:2010.11934},
  year={2020}
}

And reference this model card:

@misc{ajsbsd_mt5_ossetian,
  title={mt5\_ossetian\_translator},
  author={Aaron},
  year={2026},
  howpublished={\url{https://huggingface.co/ajsbsd/mt5_ossetian_translator}},
  note={Hugging Face Model Hub}
}

🙏 Acknowledgements

• Google Research for the mT5 base model
• Community contributors to the Pontoon-Translations dataset
• Hugging Face transformers and datasets libraries

---

Model maintained by @ajsbsd. Last updated: June 2026.
License: Apache 2.0 — see LICENSE for details. ```