The Aina project has embarked on an ambitious journey to create a high-quality machine translation model that translates from Catalan to Spanish. Through extensive training using the Fairseq toolkit, this model has been tested against various public datasets and has shown promising results. In this article, we’ll guide you on how to effectively use this model, discuss its limitations, and provide some troubleshooting tips.

Model Description

The Aina machine translation model was trained from scratch on a staggering 92 million sentences, making it a powerful tool for seamless communication between Catalan and Spanish speakers. It evaluates its performance across five different domains: general, administrative, technology, biomedical, and news.

Intended Uses and Limitations

This model is designed specifically for translating sentences from Catalan to Spanish. However, like every AI model, it has its limitations. Currently, the model has not undergone comprehensive bias and toxicity assessments, but awareness of potential biases is present. Future updates aim to address these concerns.

How to Use the Aina Translator

Required Libraries

Before using the model, make sure you have the following libraries installed:

• ctranslate2
• pyonmttok

Installation

To install the required libraries, run the command:

pip install ctranslate2 pyonmttok

Translation Example

Here’s a step-by-step breakdown of how to translate a sentence using Python:

import ctranslate2
import pyonmttok
from huggingface_hub import snapshot_download

model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-ca-es", revision="main")
tokenizer = pyonmttok.Tokenizer(mode="none", sp_model_path=model_dir + "/spm.model")
tokenized = tokenizer.tokenize("Benvingut al projecte Aina!")
translator = ctranslate2.Translator(model_dir)
translated = translator.translate_batch([tokenized[0]])
print(tokenizer.detokenize(translated[0][0]["tokens"]))

In this code, think of the translation process as a well-orchestrated team performance:

• Tokenization (Getting into Formation): Just as a team member prepares for their role, the text is divided into manageable pieces (tokens).
• Translation (Executing the Play): The translator acts as the team captain, expertly converting the formations (tokens) into the target language.
• Detokenization (Putting It All Together): Finally, just as a team celebrates a victory, the individual translations are combined back into a coherent sentence.

Model Limitations and Bias

It’s important to acknowledge that models can reflect biases present in their training data. Although no bias assessments have been implemented yet, the team is committed to addressing these issues in the future.

Training Overview

The Aina model underwent rigorous training using diverse datasets totaling around 92 million bilingual sentences. Various cleaning and filtering techniques, including the mBERT Gencata parallel filter, were employed to ensure the quality of the training data.

Performance Evaluation

The model’s effectiveness is evaluated using the BLEU score on a series of test datasets. Here’s a comparison of its performance against existing benchmarks:

Test Set	SoftCatalà	Google Translate	Aina Translator
Spanish Constitution	70.7	77.1	83.3
United Nations	78.1	84.3	87.3
Average	53.4	53.2	55.1

Troubleshooting Tips

If you encounter issues while using the Aina translator, here are some troubleshooting ideas:

• Library Installation Problems: Ensure you have a compatible version of Python and have installed the required libraries correctly.
• Model Download Issues: Confirm that the model directory is downloaded successfully from the Hugging Face repository.
• Translation Errors: Check the format of the input text to ensure it follows the expected configuration for tokenization.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.