Leçon 7 - Fastai-FR - Le Traitement Automatique du Langage Naturel (NLP)

Introduction

In this tutorial, we will delve into the basics of Natural Language Processing (NLP) using Fastai, focusing on two types of models: a language model and a text classifier. We'll utilize a dataset of movie reviews from IMDB to classify text as positive or negative. This guide will walk you through the process step-by-step, helping you understand the implementation and application of these models.

Step 1: Setting Up Your Environment

To begin, ensure you have the necessary tools and libraries installed.

• Install Fastai and its dependencies. You can do this using pip:

  pip install fastai
  

• Access the Jupyter notebook provided in the GitHub repository: Fastai NLP Notebook

Step 2: Loading the IMDB Dataset

The next step involves loading the IMDB dataset for your NLP tasks.

• Import necessary libraries:

  from fastai.text.all import *
  

• Load the IMDB dataset:

  path = untar_data(URLs.IMDB)
  

• Understand the structure of the dataset, which contains two folders: "train" and "test," each with "pos" and "neg" subfolders.

Step 3: Creating a DataBlock

Now that the dataset is ready, create a DataBlock to prepare your data for modeling.

• Define the DataBlock:

  dblock = DataBlock(
      blocks=(TextBlock.from_folder(path), CategoryBlock),
      get_items=get_texts,
      splitter=GrandparentSplitter(),
      get_y=using_func
  )
  

• Create a DataLoader:

  dls = dblock.dataloaders(path)
  

Step 4: Training a Language Model

Next, we will train a language model that learns the structure of the text.

• Create a language learner:

  learn = language_model_learner(dls)
  

• Train the model using the fit method:

  learn.fit_one_cycle(1, 1e-2)
  

Step 5: Fine-Tuning the Language Model

After training the initial model, you can fine-tune it for better performance.

• Unfreeze the model:

  learn.unfreeze()
  

• Continue training:

  learn.fit_one_cycle(1, 1e-3)
  

Step 6: Creating a Text Classifier

Now, we will build a text classifier based on the language model.

• Create a text classifier learner:

  classifier_learner = text_classifier_learner(dls, drop_mult=0.5)
  

• Train the classifier:

  classifier_learner.fit_one_cycle(1, 1e-2)
  

Step 7: Evaluating the Model

It's essential to evaluate the model's performance to understand its accuracy.

• Use the validate method to check the performance:

  classifier_learner.validate()
  

• Additionally, visualize predictions using:

  classifier_learner.show_results()
  

Conclusion

In this tutorial, you learned how to set up an NLP model using Fastai, from loading the IMDB dataset to training a language model and a text classifier. Key takeaways include understanding data preparation, model training, and evaluation techniques.

For further exploration, consider experimenting with different datasets or tuning hyperparameters to improve model performance. Happy coding!