That’s true. With short sentences and words, there’s not much benefit to using LLM. There are ways to use smaller LMs, etc., but it’s true that you can use Google Translate.
To address the translation issues with short sentences on your website, here are some recommendations that balance ease of implementation with effectiveness:
-
HuggingFace OPUS-MT Model: Utilize the “Helsinki-NLP/opus-mt-mul-en” model, which is designed for multilingual translations. It’s user-friendly with the Transformers library and can handle multiple languages, including French, German, Dutch, and Italian.
-
Facebook’s mBART or M2M Models: Use the EasyNMT package to leverage these models, which are multilingual and support a wide range of languages. They are efficient for short sentences and can be integrated with minimal code.
-
MarianMT Models: Consider these for their speed and efficiency. They are optimized for real-time translations and come in various language pairs, suitable for quick processing on a website.
-
DeepL API: If budget allows, DeepL’s API offers high-quality translations, particularly effective for short texts, though it may have usage limits.
-
Testing and Evaluation: Develop a script to test different models with your specific short phrases. This hands-on approach will help you evaluate which model provides the best accuracy for your needs.
-
Adjustments for Short Texts: Ensure proper tokenization and padding are applied, as some models may require fixed input lengths. This adjustments can improve translation quality for short sentences.
Implementation Steps:
- Start with installing necessary libraries like
transformersoreasynmt. - Use HuggingFace’s pipeline for OPUS-MT or EasyNMT for Facebook’s models.
- Consider setting up a test environment to evaluate each model’s performance.
By exploring these options, you can enhance the accuracy of your translations while maintaining ease of use, especially since you’re a Python beginner.