twi-en-nllb-v2 — Twi → English Translation
A fine-tuned version of facebook/nllb-200-distilled-600M for translating Twi (Akan, twi_Latn) into English (eng_Latn). This model improves substantially over the v1 MarianMT baseline and is designed for low-resource Ghanaian language translation.
Model Details
| |
|---|
| Base model | facebook/nllb-200-distilled-600M |
| Task | Sequence-to-sequence translation |
| Direction | twi_Latn → eng_Latn |
| Parameters | 615.1M |
| License | CC BY-NC 4.0 |
Training Details
| Hyperparameter | Value |
|---|
| Epochs | 10 |
| Batch size | 4 (effective: 32 with gradient accumulation) |
| Gradient accumulation steps | 8 |
| Learning rate | 3e-5 |
| LR scheduler | Cosine |
| Warmup ratio | 0.06 |
| Weight decay | 0.01 |
| Label smoothing | 0.1 |
| Max sequence length | 128 |
| Precision | fp16 |
| Best model metric | chrF |
Dataset
Training data sourced from the GhanaNLP TWI-ENGLISH Parallel Text dataset.
| Split | Pairs |
|---|
| Train | 7,732 |
| Validation | 967 |
| Test | 967 |
| Total | 9,666 |
Results
Evaluated on 967 held-out test pairs:
| Metric | v2 (this model) | v1 MarianMT baseline | Δ |
|---|
| BLEU | 27.30 | 10.50 | +16.80 |
| chrF | 51.03 | 32.30 | +18.73 |
| TER ↓ | 61.48 | 87.64 | −26.16 |
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_id = "ninte/twi-en-nllb-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
model.eval()
def translate(text, src_lang="twi_Latn", tgt_lang="eng_Latn"):
tokenizer.src_lang = src_lang
tgt_token_id = tokenizer.convert_tokens_to_ids(tgt_lang)
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
output = model.generate(
**inputs,
forced_bos_token_id=tgt_token_id,
num_beams=4,
max_length=128,
no_repeat_ngram_size=3,
length_penalty=1.0,
early_stopping=True,
)
return tokenizer.decode(output[0], skip_special_tokens=True)
print(translate("Ɛsɛ sɛ mmofra di aduan pa na ama wɔanyin yiye."))
# → "Children need to eat healthy foods to grow well."
Translation Examples
| Twi | Prediction | Reference |
|---|
| Ɛsɛ sɛ mmofra di aduan pa na ama wɔanyin yiye. | Children need to eat healthy foods to grow well. | Children need to feed well in order to grow well. |
| Mpanimfoɔ a wɔda mansini no ano... | The elders at the border of the district have teamed up with the loggers... | The district officials connive with loggers to make money out of trees. |
Limitations
- Trained on ~9,600 sentence pairs; performance on out-of-domain text may vary.
- Max sequence length of 128 tokens — longer inputs are truncated.
- Best suited for standard/formal Twi; dialectal variation is not explicitly handled.
Citation
If you use this model, please cite the base NLLB model and the training dataset:
@article{nllb2022,
title={No Language Left Behind: Scaling Human-Centered Machine Translation},
author={NLLB Team et al.},
journal={arXiv preprint arXiv:2207.04672},
year={2022}
}
@dataset{ghananlp_twi_english_2023,
author = {GhanaNLP},
title = {GhanaNLP TWI-ENGLISH Parallel Text},
year = {2023},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/Ghana-NLP/TWI_ENGLISH_PARALLEL_TEXT}
}
Limitations
- Trained on ~9,600 sentence pairs; performance on out-of-domain text may vary.
- Max sequence length of 128 tokens — longer inputs are truncated.
- Best suited for standard/formal Twi; dialectal variation is not explicitly handled.