Davlan / mt5_base_yor_eng_mt

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language: - yo - en datasets: - JW300 + Menyo-20k

mT5_base_yor_eng_mt

Model description

mT5_base_yor_eng_mt is a machine translation model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a strong baseline for automatically translating texts from Yorùbá to English.

Specifically, this model is a mT5_base model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for MT.

from transformers import MT5ForConditionalGeneration, T5Tokenizer

model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_yor_eng_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Akọni ajìjàgbara obìnrin tó sun àtìmalé torí owó orí"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)

Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.

Training data

This model was fine-tuned on on JW300 Yorùbá corpus and Menyo-20k dataset

Training procedure

This model was trained on a single NVIDIA V100 GPU

Eval results on Test set (BLEU score)

15.57 BLEU on Menyo-20k test set

BibTeX entry and citation info

By David Adelani