print(translation) Here, we use the translation pipeline with the Helsinki-NLP/opus-mt-fr-en model. The French input text is translated into English, showcasing the model's ability to understand and translate between languages. Text Translation French to English Language
Conclusion
The Hugging Face library offers powerful tools for a variety of NLP tasks. By using simple pipelines, we can perform sentiment analysis, zero-shot classification, text generation, summarization, and translation with just a few lines of code. This notebook serves as an excellent starting point for exploring the capabilities of Hugging Face models in NLP projects.
Feel free to experiment with different models and tasks to see the full potential of Hugging Face in action!
This brings us to the end of this article. I hope you have understood everything clearly. Make sure you practice as much as possible.
If you wish to check out more resources related to Data Science, Machine Learning, and Deep Learning, you can refer to my GitHub account.
