metadata

language:
  - en
  - ha
tags:
  - translation
  - nllb
  - english
  - hausa
license: mit
datasets:
  - tico19
pipeline_tag: translation
model-index:
  - name: english-hausa-nllb
    results:
      - task:
          type: translation
          name: Translation
        dataset:
          type: tico19
          name: TICO-19
        metrics:
          - type: bleu
            value: 61.48

English-Hausa Neural Machine Translation

This model is a fine-tuned version of facebook/nllb-200-distilled-600M for English to Hausa translation.

Model description

Model Architecture: NLLB-200 (No Language Left Behind)
Base Model: facebook/nllb-200-distilled-600M
Fine-tuning Dataset: TICO-19 English-Hausa parallel corpus
BLEU Score: 61.48
Languages: English (eng_Latn) → Hausa (hau_Latn)

Intended uses & limitations

This model is designed for translating English text to Hausa. It performs best on:

General domain text
Short to medium-length sentences

Training and Evaluation

Training Hyperparameters

The model was trained with the following hyperparameters:

Learning rate: 1e-5
Batch size: 16
Number of epochs: 30
Weight decay: 0.01
Maximum sequence length: 128
Beam size: 10

Training Results

BLEU score: 61.48

How to use

Here's how to use the model with the Transformers library:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load model and tokenizer
model_name = "mide6x/english-hausa-nllb"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Prepare text for translation
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt", padding=True)

# Set source and target languages
tokenizer.src_lang = "eng_Latn"
tokenizer.tgt_lang = "hau_Latn"

# Get the Hausa language token ID correctly
hausa_token_id = tokenizer.convert_tokens_to_ids(tokenizer.tgt_lang)

# Generate translation
outputs = model.generate(
    **inputs,
    forced_bos_token_id=hausa_token_id,
    max_length=128,
    num_beams=10,
    early_stopping=True
)

# Decode the translation
translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(f"English: {text}")
print(f"Hausa: {translation}")

Limitations and Biases

Performance may vary for very long sentences or complex grammatical structures
The model inherits any biases present in the training data

Citation

If you use this model, please cite:

The original NLLB-200 paper
The TICO-19 dataset

Author

GenZ AI