Chapter 8: Project: Text Generation with Autoregressive Models
8.1 Data Collection and Preprocessing
In this chapter, we will apply what we have learned about autoregressive models in a practical, hands-on project. The goal of this project is to generate human-like text using an autoregressive model. Our model will learn to predict the next word in a sequence based on the previous words, and in this way, it will be able to generate entirely new sequences of text that mimic the style and structure of the training data.
The first step in any machine learning project is data collection and preprocessing. For our text generation project, we will need a large corpus of text data to train our model. The choice of dataset can have a significant impact on the model's performance and the type of text it generates.
8.1.1 Dataset Selection
There are many publicly available text datasets that we can use for this project. For example, Project Gutenberg (https://www.gutenberg.org) offers over 60,000 free eBooks, the Brown Corpus contains 500 samples of English-language text, and the Wikipedia dataset includes all of Wikipedia's articles. Depending on the style of text you want your model to generate, you might choose a dataset of novels, news articles, scientific papers, or even social media posts.
For the purposes of this project, let's say we're using the text of every Shakespeare play. This dataset is a popular choice for text generation projects because it's relatively small, freely available, and results in a model that generates text in a distinctive style.
import requests
# Download the complete works of Shakespeare
response = requests.get('https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt')
shakespeare_text = response.text8.1.2 Text Preprocessing
Once we have our dataset, the next step is to preprocess the text to make it suitable for training a model. This typically involves:
- Lowercasing: Convert all the text to lowercase so that our model does not treat words like "The" and "the" as different words.
shakespeare_text = shakespeare_text.lower()- Tokenization: Split the text into individual words (or "tokens").
from nltk.tokenize import word_tokenize
tokens = word_tokenize(shakespeare_text)- Build vocabulary: Create a list of all unique words (or "vocabulary") in the text. This will allow us to convert words to numerical values, which our model can work with.
vocab = sorted(set(tokens))- Vectorization: Convert each word in the text to its corresponding numerical value.
word_to_index = {word: index for index, word in enumerate(vocab)}
index_to_word = {index: word for index, word in enumerate(vocab)}- Create sequences: Finally, we need to create sequences of words to use as training data. Each sequence will be a fixed length (e.g., 10 words) and the model's task will be to predict the next word in the sequence.
import numpy as np
sequence_length = 10
# Create training sequences
sequences = []
for i in range(sequence_length, len(tokens)):
sequences.append(tokens[i-sequence_length:i])
# Vectorize the sequences
sequences = [[word_to_index[word] for word in sequence] for sequence in sequences]
sequences = np.array(sequences)
# Split into input and output
X = sequences[:, :-1]
y = sequences[:, -1]That's it for the data collection and preprocessing! We have now completed the training data preparation and it is ready to be fed into our model for training. The X variable contains sequences of words and the y variable contains the corresponding next words for each sequence. They are both in a numerical format which our model can work with.
Here's the completed part of the code:
# Split into input and output
X = sequences[:,:-1]
y = sequences[:,-1]
print('Total Sequences:', len(X))The output of the script would provide the total number of sequences we've prepared for training.
Keep in mind that these are just the first steps to starting our project. Data collection and preprocessing are essential tasks because they directly influence the performance of the model. A well-structured and clean dataset will always be a boon for the model's performance.
In the next section, we will delve into the construction of the autoregressive model for our project.
8.1 Data Collection and Preprocessing
In this chapter, we will apply what we have learned about autoregressive models in a practical, hands-on project. The goal of this project is to generate human-like text using an autoregressive model. Our model will learn to predict the next word in a sequence based on the previous words, and in this way, it will be able to generate entirely new sequences of text that mimic the style and structure of the training data.
The first step in any machine learning project is data collection and preprocessing. For our text generation project, we will need a large corpus of text data to train our model. The choice of dataset can have a significant impact on the model's performance and the type of text it generates.
8.1.1 Dataset Selection
There are many publicly available text datasets that we can use for this project. For example, Project Gutenberg (https://www.gutenberg.org) offers over 60,000 free eBooks, the Brown Corpus contains 500 samples of English-language text, and the Wikipedia dataset includes all of Wikipedia's articles. Depending on the style of text you want your model to generate, you might choose a dataset of novels, news articles, scientific papers, or even social media posts.
For the purposes of this project, let's say we're using the text of every Shakespeare play. This dataset is a popular choice for text generation projects because it's relatively small, freely available, and results in a model that generates text in a distinctive style.
import requests
# Download the complete works of Shakespeare
response = requests.get('https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt')
shakespeare_text = response.text8.1.2 Text Preprocessing
Once we have our dataset, the next step is to preprocess the text to make it suitable for training a model. This typically involves:
- Lowercasing: Convert all the text to lowercase so that our model does not treat words like "The" and "the" as different words.
shakespeare_text = shakespeare_text.lower()- Tokenization: Split the text into individual words (or "tokens").
from nltk.tokenize import word_tokenize
tokens = word_tokenize(shakespeare_text)- Build vocabulary: Create a list of all unique words (or "vocabulary") in the text. This will allow us to convert words to numerical values, which our model can work with.
vocab = sorted(set(tokens))- Vectorization: Convert each word in the text to its corresponding numerical value.
word_to_index = {word: index for index, word in enumerate(vocab)}
index_to_word = {index: word for index, word in enumerate(vocab)}- Create sequences: Finally, we need to create sequences of words to use as training data. Each sequence will be a fixed length (e.g., 10 words) and the model's task will be to predict the next word in the sequence.
import numpy as np
sequence_length = 10
# Create training sequences
sequences = []
for i in range(sequence_length, len(tokens)):
sequences.append(tokens[i-sequence_length:i])
# Vectorize the sequences
sequences = [[word_to_index[word] for word in sequence] for sequence in sequences]
sequences = np.array(sequences)
# Split into input and output
X = sequences[:, :-1]
y = sequences[:, -1]That's it for the data collection and preprocessing! We have now completed the training data preparation and it is ready to be fed into our model for training. The X variable contains sequences of words and the y variable contains the corresponding next words for each sequence. They are both in a numerical format which our model can work with.
Here's the completed part of the code:
# Split into input and output
X = sequences[:,:-1]
y = sequences[:,-1]
print('Total Sequences:', len(X))The output of the script would provide the total number of sequences we've prepared for training.
Keep in mind that these are just the first steps to starting our project. Data collection and preprocessing are essential tasks because they directly influence the performance of the model. A well-structured and clean dataset will always be a boon for the model's performance.
In the next section, we will delve into the construction of the autoregressive model for our project.
8.1 Data Collection and Preprocessing
In this chapter, we will apply what we have learned about autoregressive models in a practical, hands-on project. The goal of this project is to generate human-like text using an autoregressive model. Our model will learn to predict the next word in a sequence based on the previous words, and in this way, it will be able to generate entirely new sequences of text that mimic the style and structure of the training data.
The first step in any machine learning project is data collection and preprocessing. For our text generation project, we will need a large corpus of text data to train our model. The choice of dataset can have a significant impact on the model's performance and the type of text it generates.
8.1.1 Dataset Selection
There are many publicly available text datasets that we can use for this project. For example, Project Gutenberg (https://www.gutenberg.org) offers over 60,000 free eBooks, the Brown Corpus contains 500 samples of English-language text, and the Wikipedia dataset includes all of Wikipedia's articles. Depending on the style of text you want your model to generate, you might choose a dataset of novels, news articles, scientific papers, or even social media posts.
For the purposes of this project, let's say we're using the text of every Shakespeare play. This dataset is a popular choice for text generation projects because it's relatively small, freely available, and results in a model that generates text in a distinctive style.
import requests
# Download the complete works of Shakespeare
response = requests.get('https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt')
shakespeare_text = response.text8.1.2 Text Preprocessing
Once we have our dataset, the next step is to preprocess the text to make it suitable for training a model. This typically involves:
- Lowercasing: Convert all the text to lowercase so that our model does not treat words like "The" and "the" as different words.
shakespeare_text = shakespeare_text.lower()- Tokenization: Split the text into individual words (or "tokens").
from nltk.tokenize import word_tokenize
tokens = word_tokenize(shakespeare_text)- Build vocabulary: Create a list of all unique words (or "vocabulary") in the text. This will allow us to convert words to numerical values, which our model can work with.
vocab = sorted(set(tokens))- Vectorization: Convert each word in the text to its corresponding numerical value.
word_to_index = {word: index for index, word in enumerate(vocab)}
index_to_word = {index: word for index, word in enumerate(vocab)}- Create sequences: Finally, we need to create sequences of words to use as training data. Each sequence will be a fixed length (e.g., 10 words) and the model's task will be to predict the next word in the sequence.
import numpy as np
sequence_length = 10
# Create training sequences
sequences = []
for i in range(sequence_length, len(tokens)):
sequences.append(tokens[i-sequence_length:i])
# Vectorize the sequences
sequences = [[word_to_index[word] for word in sequence] for sequence in sequences]
sequences = np.array(sequences)
# Split into input and output
X = sequences[:, :-1]
y = sequences[:, -1]That's it for the data collection and preprocessing! We have now completed the training data preparation and it is ready to be fed into our model for training. The X variable contains sequences of words and the y variable contains the corresponding next words for each sequence. They are both in a numerical format which our model can work with.
Here's the completed part of the code:
# Split into input and output
X = sequences[:,:-1]
y = sequences[:,-1]
print('Total Sequences:', len(X))The output of the script would provide the total number of sequences we've prepared for training.
Keep in mind that these are just the first steps to starting our project. Data collection and preprocessing are essential tasks because they directly influence the performance of the model. A well-structured and clean dataset will always be a boon for the model's performance.
In the next section, we will delve into the construction of the autoregressive model for our project.
8.1 Data Collection and Preprocessing
In this chapter, we will apply what we have learned about autoregressive models in a practical, hands-on project. The goal of this project is to generate human-like text using an autoregressive model. Our model will learn to predict the next word in a sequence based on the previous words, and in this way, it will be able to generate entirely new sequences of text that mimic the style and structure of the training data.
The first step in any machine learning project is data collection and preprocessing. For our text generation project, we will need a large corpus of text data to train our model. The choice of dataset can have a significant impact on the model's performance and the type of text it generates.
8.1.1 Dataset Selection
There are many publicly available text datasets that we can use for this project. For example, Project Gutenberg (https://www.gutenberg.org) offers over 60,000 free eBooks, the Brown Corpus contains 500 samples of English-language text, and the Wikipedia dataset includes all of Wikipedia's articles. Depending on the style of text you want your model to generate, you might choose a dataset of novels, news articles, scientific papers, or even social media posts.
For the purposes of this project, let's say we're using the text of every Shakespeare play. This dataset is a popular choice for text generation projects because it's relatively small, freely available, and results in a model that generates text in a distinctive style.
import requests
# Download the complete works of Shakespeare
response = requests.get('https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt')
shakespeare_text = response.text8.1.2 Text Preprocessing
Once we have our dataset, the next step is to preprocess the text to make it suitable for training a model. This typically involves:
- Lowercasing: Convert all the text to lowercase so that our model does not treat words like "The" and "the" as different words.
shakespeare_text = shakespeare_text.lower()- Tokenization: Split the text into individual words (or "tokens").
from nltk.tokenize import word_tokenize
tokens = word_tokenize(shakespeare_text)- Build vocabulary: Create a list of all unique words (or "vocabulary") in the text. This will allow us to convert words to numerical values, which our model can work with.
vocab = sorted(set(tokens))- Vectorization: Convert each word in the text to its corresponding numerical value.
word_to_index = {word: index for index, word in enumerate(vocab)}
index_to_word = {index: word for index, word in enumerate(vocab)}- Create sequences: Finally, we need to create sequences of words to use as training data. Each sequence will be a fixed length (e.g., 10 words) and the model's task will be to predict the next word in the sequence.
import numpy as np
sequence_length = 10
# Create training sequences
sequences = []
for i in range(sequence_length, len(tokens)):
sequences.append(tokens[i-sequence_length:i])
# Vectorize the sequences
sequences = [[word_to_index[word] for word in sequence] for sequence in sequences]
sequences = np.array(sequences)
# Split into input and output
X = sequences[:, :-1]
y = sequences[:, -1]That's it for the data collection and preprocessing! We have now completed the training data preparation and it is ready to be fed into our model for training. The X variable contains sequences of words and the y variable contains the corresponding next words for each sequence. They are both in a numerical format which our model can work with.
Here's the completed part of the code:
# Split into input and output
X = sequences[:,:-1]
y = sequences[:,-1]
print('Total Sequences:', len(X))The output of the script would provide the total number of sequences we've prepared for training.
Keep in mind that these are just the first steps to starting our project. Data collection and preprocessing are essential tasks because they directly influence the performance of the model. A well-structured and clean dataset will always be a boon for the model's performance.
In the next section, we will delve into the construction of the autoregressive model for our project.