Hugging Face transformer models

Everything we have learned in this chapter can be condensed into a ready-to-use Hugging Face transformer model.

With Hugging Face, we can implement machine translation in three lines of code!

Open Multi_Head_Attention_Sub_Layer.ipynb in Google Colaboratory. Save the notebook in your Google Drive (make sure you have a Gmail account). Then, go to the two last cells.

We first ensure that Hugging Face transformers are installed:

!pip -q install transformers

The first cell imports the Hugging Face pipeline that contains several transformer usages:

#@title Retrieve pipeline of modules and choose English to French translation
from transformers import pipeline

We then implement the Hugging Face pipeline, which contains ready-to-use functions. In our case, to illustrate the Transformer model of this chapter, we activate the translator model and enter a sentence to translate from English to French:

translator = pipeline("translation_en_to_fr")
#One line of code!
print(translator("It is easy to translate languages with transformers", max_length=40))

And voilà! The translation is displayed:

[{'translation_text': 'Il est facile de traduire des langues à l'aide de transformateurs.'}]

Hugging Face shows how transformer architectures can be used in ready-to-use models.