Fastai is a powerful Python library that provides a high-level API for training and deploying deep learning models. One of the key functionalities of Fastai is language modeling, which allows us to build and train models that can generate text or perform various natural language processing tasks. In this blog post, we will explore how to perform language modeling using Fastai in Python.

Setting up the environment

Before we dive into language modeling, we need to set up our Python environment. Fastai can be installed via pip by running the following command:

pip install fastai

We will also need the pandas library for data manipulation and the matplotlib library for visualizations. Install them using the following commands:

pip install pandas
pip install matplotlib

Preparing the data

To train a language model, we need a large amount of text data. The Fastai library provides a convenient way to download pre-trained language model datasets. We can use the URLs class to fetch the dataset. For example, to download the English Wikipedia dataset, we can use the following code:

from fastai.text import *
path = untar_data(URLs.WIKITEXT_TINY)

Text preprocessing

After obtaining the data, we need to preprocess it before training our language model. The Fastai library provides built-in functions for text preprocessing. We can use the TextList class to load and preprocess the text data in our dataset. Here is an example code to process the text:

data_lm = TextList.from_folder(path)\
    .filter_by_folder(include=['train', 'valid'])\
    .split_by_rand_pct(0.1, seed=42)\
    .label_for_lm()\
    .databunch(bs=32)

Training the language model

Once we have preprocessed the data, we can proceed to train our language model. The Fastai library provides a language_model_learner function for this purpose. We can specify the pre-trained architecture, the data, and the learning rate. Here is an example code to train the language model:

learn = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.3)
learn.fit_one_cycle(1, 1e-2)

Generating text

After training the language model, we can use it to generate text based on a given input. The learn.predict function in Fastai allows us to generate text by providing a starting prompt. Here is an example code to generate text with our trained model:

prompt = "Once upon a time"
text = learn.predict(prompt, n_words=30, temperature=0.75)
print(text)

Conclusion

Language modeling is a powerful technique in natural language processing, and Fastai makes it easy to build and train language models in Python. In this blog post, we learned how to set up the environment, preprocess the text data, train a language model, and generate text using Fastai. With Fastai’s high-level API, it becomes extremely convenient to perform language modeling tasks and explore the world of natural language processing.