🚀 mT5_base_eng_yor_mt
A machine translation model that effectively translates English texts into Yorùbá, offering a strong baseline for this language pair.
🚀 Quick Start
You can use this model with Transformers pipeline for MT.
from transformers import MT5ForConditionalGeneration, T5Tokenizer
model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Where are you?"
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)
✨ Features
- Machine Translation: Translates English texts to Yorùbá.
- Strong Baseline: Establishes a reliable starting point for English - Yorùbá translation tasks.
📦 Installation
No specific installation steps were provided in the original document.
💻 Usage Examples
Basic Usage
from transformers import MT5ForConditionalGeneration, T5Tokenizer
model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Where are you?"
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)
📚 Documentation
Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine - tuned mT5 - base model. It establishes a strong baseline for automatically translating texts from English to Yorùbá.
Specifically, this model is a mT5_base model that was fine - tuned on JW300 Yorùbá corpus and Menyo-20k
Intended uses & limitations
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 corpus and Menyo-20k dataset
Training procedure
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (BLEU score)
9.82 BLEU on Menyo-20k test set
BibTeX entry and citation info
By David Adelani
Property |
Details |
Supported Languages |
yo, en |
Model Type |
Machine Translation |
Training Data |
JW300 + Menyo-20k |
Training Hardware |
Single NVIDIA V100 GPU |
Evaluation Metric |
BLEU score |
Evaluation Result |
9.82 BLEU on Menyo-20k test set |
⚠️ Important Note
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.