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--- |
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license: apache-2.0 |
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language: |
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- tgj |
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- en |
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datasets: |
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- repleeka/Tagin-KIFS-Corpus-v1.0 |
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base_model: |
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- facebook/mbart-large-50-many-to-many-mmt |
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pipeline_tag: translation |
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tags: |
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- mbart |
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- nmt |
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- lowres |
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- tagin |
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--- |
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# mBART-tgj-final |
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`mBART-tgj-final` is a fine-tuned sequence-to-sequence model based on `mBART-50-M2M` for `Tagin → English` Neural Machine Translation. |
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The model is trained using a Knowledge-Integrated Filtering System (KIFS) applied to a large mixed corpus of synthetic and manually curated sentence pairs, enabling significant improvements in translation quality for an extremely low-resource language. |
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## Developer |
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- Name: Tungon Dugi |
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- Institution: National Institute of Technology Arunachal Pradesh |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast |
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model = MBartForConditionalGeneration.from_pretrained("repleeka/mBART-tgj-final") |
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tokenizer = MBart50TokenizerFast.from_pretrained("repleeka/mBART-tgj-final") |
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tokenizer.src_lang = "en_XX" |
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text = "How are you?" |
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inputs = tokenizer(text, return_tensors="pt") |
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generated_tokens = model.generate( |
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**inputs, |
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forced_bos_token_id=tokenizer.convert_tokens_to_ids("<tgj_IN>"), |
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num_beams=5, |
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max_length=128, |
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) |
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print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)) |
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``` |
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## Intended Use |
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- Research in low-resource and endangered language technology |
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- Tagin language documentation and preservation |
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- Transfer learning experiments with Tani-language family |
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- Domain adaptation and linguistic resource development |
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## Not intended for |
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- High-stakes applications (medical, legal, safety-critical) |
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- Fully automated decision systems without human validation |
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## Model Architecture |
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| Component | Value | |
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| :--- | :--- | |
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| **Base Model** | mBART-50 Large | |
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| **Layers** | 12-layer encoder & decoder | |
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| **Hidden size** | 1024 | |
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| **FFN dimension** | 4096 | |
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| **Attention heads** | 16 | |
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| **Vocabulary size** | 250,055 | |
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| **Tokenizer** | MBart50Tokenizer | |
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## Evaluation Results |
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| Metric | Score | |
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| :--- | :--- | |
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| **BLEU** | 40.27 | |
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| **ChrF** | 59.38 | |
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| **METEOR** | 46.29 | |
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| **TER** | 44.42 | |
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| **Loss** | 0.0503 | |
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### Statistical Significance Test Results |
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`Evaluation based on 890-sentence held-out test set with Approximate Randomization significance testing (p < 0.01), showing large performance gains over baseline mBART-tgj-base.` |
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| Parameter | Detail | |
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| :--- | :--- | |
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| **Evaluation Set** | 890-sentence held-out test set | |
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| **Test Method** | Approximate Randomization | |
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| **Significance Level** | p < 0.01 | |
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| **Comparison** | Large performance gains over baseline `mBART-tgj-base` | |
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## Limitations & Ethical Considerations |
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| Category | Details | |
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| :--- | :--- | |
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| **Linguistic Limitations** | Model may struggle with rare morphology, honorific forms, and culturally grounded expressions | |
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| **Cultural Sensitivity** | Sensitive cultural or mythological meanings may require expert review | |
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| **Data Bias** | Bias inherited from synthetic data cannot be fully eliminated | |
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| **Deployment Scope** | Not suitable for automated translation of critical documents | |
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## Citation |
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Dugi, T., & Sambyo, K. (2025). |
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*A Knowledge-Integrated System for Quality-Driven Filtering of Low-Resource Tagin-English Bitexts.* |
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National Institute of Technology Arunachal Pradesh. |
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## License |
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MIT / Apache-2.0 recommended for research openness and compatibility. |
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(Data licensing depends on source text availability.) |
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## Contact |
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For collaboration, feedback, or issues: |
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*tungondugi@gmail.com* |
