Mt5 Base Eng Yor Mt

🚀 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

⚠️ 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.