---
license: apache-2.0
language:
- en
- ig
tags:
- translation
- nllb
- igbo
- english
- low-resource
base_model: facebook/nllb-200-distilled-600M
---

# AIN — English ↔ Igbo Translation Model

This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M),
adapted for English -- Igbo translation as part of the **aAIN** project — a speech-to-speech
translation system for English and Nigerian languages (Yoruba, Hausa, Igbo).

## Model description

The base NLLB-200-distilled-600M model was fine-tuned on **27,097 Igbo-English sentence pairs**
extracted and cleaned from a bilingual dictionary dataset. Fine-tuning was aimed at improving
translation quality specifically for Igbo, a low-resource language with limited high-quality
parallel data available for training.

## Training results

| Metric | Before fine-tuning | After fine-tuning |
|---|---|---|
| BLEU score | 14.06 | 26.86 |

BLEU improved by +12.8 points on held-out test data — nearly double the baseline score.

## Intended use

This model is intended for English ↔ Igbo text translation, as part of a larger speech-to-speech
pipeline (ASR → translation → TTS). It can be used standalone for text translation via the
`transformers` library.

## Limitations

- **BLEU 26.86 indicates decent but imperfect translation quality.** Expect generally coherent
  output, but not publication-grade accuracy on every sentence.
- Some common conversational phrases (e.g. certain greetings) may occasionally be returned
  untranslated or copied verbatim from the source. This is a known limitation observed during
  testing and an area for improvement in future versions.
- The training data was sourced from a dictionary dataset, which may bias the model toward more
  formal or literal phrasing rather than casual/conversational Igbo.
- This model does not include Igbo text-to-speech; a separate TTS component is planned for a
  future release.

## How to use

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "Ntui-Chinua/aAIN"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

tokenizer.src_lang = "eng_Latn"
inputs = tokenizer("I am going to the market.", return_tensors="pt")
forced_bos_token_id = tokenizer.convert_tokens_to_ids("ibo_Latn")

generated_tokens = model.generate(**inputs, forced_bos_token_id=forced_bos_token_id, max_length=128)
print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0])
```

## Training data

27,097 English-Igbo sentence pairs extracted from a bilingual dictionary dataset.

## License

Apache 2.0