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Ntui-Chinua/
aAIN

Name: aAIN
Availability: InStock

facebook/nllb-200-distilled-600M

finetune

apache-2.0

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AIN — English ↔ Igbo Translation Model

This model is a fine-tuned version of 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