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Generative Deep Learning Updated Edition

Chapter 8: Project: Text Generation with Autoregressive Models

8.1 Data Collection and Preprocessing

In this chapter, we will undertake an exciting project to generate text using autoregressive models. This project will provide a hands-on experience with the entire workflow of building, training, and evaluating an autoregressive model for text generation. By the end of this chapter, you will have a comprehensive understanding of how to apply these models to create coherent and contextually relevant text.

Our project will focus on using the GPT-2 model, a popular autoregressive Transformer-based model, to generate text based on a given prompt. We will cover the following topics in this chapter:

  1. Data Collection and Preprocessing
  2. Model Creation
  3. Training the Model
  4. Generating Text
  5. Evaluating the Model

Let's begin with the first step of our project: data collection and preprocessing.

Data collection and preprocessing are critical steps in any machine learning project. Properly prepared data ensures that the model can learn effectively and generalize well to new data. In this section, we will focus on collecting and preprocessing the text data required for training our autoregressive model.

8.1.1 Collecting the Text Data

For our text generation project, we need a substantial amount of text data. There are various sources from which we can collect text data, such as books, articles, and online content. For simplicity, we will use a dataset of publicly available texts.

We will use the Hugging Face Datasets library to download and load the dataset. The Hugging Face Datasets library provides access to a wide range of text datasets that are commonly used for natural language processing tasks.

Example: Loading a Text Dataset

from datasets import load_dataset

# Load the WikiText-2 dataset
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")

# Print the first example from the training set
print(dataset["train"][0])

8.1.2 Preprocessing the Text Data

Preprocessing the text data involves several steps:

  1. Tokenization: Converting the text into a sequence of tokens (words or subwords).
  2. Normalization: Lowercasing, removing punctuation, and handling special characters.
  3. Sequence Creation: Dividing the text into sequences of a fixed length that can be fed into the model.

We will use the GPT-2 tokenizer provided by the Hugging Face Transformers library for tokenization. This tokenizer is designed to work seamlessly with the GPT-2 model and will handle the necessary preprocessing steps.

Example: Preprocessing the Text Data

from transformers import GPT2Tokenizer

# Load the GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Define a function to preprocess the text data
def preprocess_text(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)

# Apply the preprocessing function to the dataset
tokenized_dataset = dataset.map(preprocess_text, batched=True)

# Print the first tokenized example from the training set
print(tokenized_dataset["train"][0])

This code uses the transformers library to load a GPT-2 tokenizer. The tokenizer is used to preprocess text data from a dataset. The preprocessing function truncates or pads the text to a maximum length of 512 tokens. The tokenized dataset is then created by applying the preprocessing function to the original dataset. The last line of the code prints the first tokenized example from the training set.

8.1.3 Creating Training Sequences

After tokenization, we need to create training sequences that can be fed into the model. Each sequence should have a fixed length, and consecutive sequences should overlap to ensure that the model can learn the dependencies between words across sequence boundaries.

We will divide the tokenized text into sequences of a fixed length, with an overlap between consecutive sequences.

Example: Creating Training Sequences

import numpy as np

# Define the sequence length and the overlap size
sequence_length = 128
overlap_size = 64

# Function to create training sequences
def create_sequences(tokenized_text, seq_length, overlap):
    total_length = len(tokenized_text)
    sequences = []
    for i in range(0, total_length - seq_length, seq_length - overlap):
        seq = tokenized_text[i:i + seq_length]
        sequences.append(seq)
    return sequences

# Extract the tokenized text from the dataset
tokenized_text = tokenized_dataset["train"]["input_ids"]

# Create training sequences
training_sequences = create_sequences(tokenized_text, sequence_length, overlap_size)

# Print the first training sequence
print(training_sequences[0])

This code imports the numpy library, defines the sequence length and overlap size, and then defines a function to create sequences. It applies this function to a tokenized text from the dataset, creating overlapping sequences of a specified length. The first training sequence is then printed out.

8.1 Data Collection and Preprocessing

In this chapter, we will undertake an exciting project to generate text using autoregressive models. This project will provide a hands-on experience with the entire workflow of building, training, and evaluating an autoregressive model for text generation. By the end of this chapter, you will have a comprehensive understanding of how to apply these models to create coherent and contextually relevant text.

Our project will focus on using the GPT-2 model, a popular autoregressive Transformer-based model, to generate text based on a given prompt. We will cover the following topics in this chapter:

  1. Data Collection and Preprocessing
  2. Model Creation
  3. Training the Model
  4. Generating Text
  5. Evaluating the Model

Let's begin with the first step of our project: data collection and preprocessing.

Data collection and preprocessing are critical steps in any machine learning project. Properly prepared data ensures that the model can learn effectively and generalize well to new data. In this section, we will focus on collecting and preprocessing the text data required for training our autoregressive model.

8.1.1 Collecting the Text Data

For our text generation project, we need a substantial amount of text data. There are various sources from which we can collect text data, such as books, articles, and online content. For simplicity, we will use a dataset of publicly available texts.

We will use the Hugging Face Datasets library to download and load the dataset. The Hugging Face Datasets library provides access to a wide range of text datasets that are commonly used for natural language processing tasks.

Example: Loading a Text Dataset

from datasets import load_dataset

# Load the WikiText-2 dataset
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")

# Print the first example from the training set
print(dataset["train"][0])

8.1.2 Preprocessing the Text Data

Preprocessing the text data involves several steps:

  1. Tokenization: Converting the text into a sequence of tokens (words or subwords).
  2. Normalization: Lowercasing, removing punctuation, and handling special characters.
  3. Sequence Creation: Dividing the text into sequences of a fixed length that can be fed into the model.

We will use the GPT-2 tokenizer provided by the Hugging Face Transformers library for tokenization. This tokenizer is designed to work seamlessly with the GPT-2 model and will handle the necessary preprocessing steps.

Example: Preprocessing the Text Data

from transformers import GPT2Tokenizer

# Load the GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Define a function to preprocess the text data
def preprocess_text(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)

# Apply the preprocessing function to the dataset
tokenized_dataset = dataset.map(preprocess_text, batched=True)

# Print the first tokenized example from the training set
print(tokenized_dataset["train"][0])

This code uses the transformers library to load a GPT-2 tokenizer. The tokenizer is used to preprocess text data from a dataset. The preprocessing function truncates or pads the text to a maximum length of 512 tokens. The tokenized dataset is then created by applying the preprocessing function to the original dataset. The last line of the code prints the first tokenized example from the training set.

8.1.3 Creating Training Sequences

After tokenization, we need to create training sequences that can be fed into the model. Each sequence should have a fixed length, and consecutive sequences should overlap to ensure that the model can learn the dependencies between words across sequence boundaries.

We will divide the tokenized text into sequences of a fixed length, with an overlap between consecutive sequences.

Example: Creating Training Sequences

import numpy as np

# Define the sequence length and the overlap size
sequence_length = 128
overlap_size = 64

# Function to create training sequences
def create_sequences(tokenized_text, seq_length, overlap):
    total_length = len(tokenized_text)
    sequences = []
    for i in range(0, total_length - seq_length, seq_length - overlap):
        seq = tokenized_text[i:i + seq_length]
        sequences.append(seq)
    return sequences

# Extract the tokenized text from the dataset
tokenized_text = tokenized_dataset["train"]["input_ids"]

# Create training sequences
training_sequences = create_sequences(tokenized_text, sequence_length, overlap_size)

# Print the first training sequence
print(training_sequences[0])

This code imports the numpy library, defines the sequence length and overlap size, and then defines a function to create sequences. It applies this function to a tokenized text from the dataset, creating overlapping sequences of a specified length. The first training sequence is then printed out.

8.1 Data Collection and Preprocessing

In this chapter, we will undertake an exciting project to generate text using autoregressive models. This project will provide a hands-on experience with the entire workflow of building, training, and evaluating an autoregressive model for text generation. By the end of this chapter, you will have a comprehensive understanding of how to apply these models to create coherent and contextually relevant text.

Our project will focus on using the GPT-2 model, a popular autoregressive Transformer-based model, to generate text based on a given prompt. We will cover the following topics in this chapter:

  1. Data Collection and Preprocessing
  2. Model Creation
  3. Training the Model
  4. Generating Text
  5. Evaluating the Model

Let's begin with the first step of our project: data collection and preprocessing.

Data collection and preprocessing are critical steps in any machine learning project. Properly prepared data ensures that the model can learn effectively and generalize well to new data. In this section, we will focus on collecting and preprocessing the text data required for training our autoregressive model.

8.1.1 Collecting the Text Data

For our text generation project, we need a substantial amount of text data. There are various sources from which we can collect text data, such as books, articles, and online content. For simplicity, we will use a dataset of publicly available texts.

We will use the Hugging Face Datasets library to download and load the dataset. The Hugging Face Datasets library provides access to a wide range of text datasets that are commonly used for natural language processing tasks.

Example: Loading a Text Dataset

from datasets import load_dataset

# Load the WikiText-2 dataset
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")

# Print the first example from the training set
print(dataset["train"][0])

8.1.2 Preprocessing the Text Data

Preprocessing the text data involves several steps:

  1. Tokenization: Converting the text into a sequence of tokens (words or subwords).
  2. Normalization: Lowercasing, removing punctuation, and handling special characters.
  3. Sequence Creation: Dividing the text into sequences of a fixed length that can be fed into the model.

We will use the GPT-2 tokenizer provided by the Hugging Face Transformers library for tokenization. This tokenizer is designed to work seamlessly with the GPT-2 model and will handle the necessary preprocessing steps.

Example: Preprocessing the Text Data

from transformers import GPT2Tokenizer

# Load the GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Define a function to preprocess the text data
def preprocess_text(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)

# Apply the preprocessing function to the dataset
tokenized_dataset = dataset.map(preprocess_text, batched=True)

# Print the first tokenized example from the training set
print(tokenized_dataset["train"][0])

This code uses the transformers library to load a GPT-2 tokenizer. The tokenizer is used to preprocess text data from a dataset. The preprocessing function truncates or pads the text to a maximum length of 512 tokens. The tokenized dataset is then created by applying the preprocessing function to the original dataset. The last line of the code prints the first tokenized example from the training set.

8.1.3 Creating Training Sequences

After tokenization, we need to create training sequences that can be fed into the model. Each sequence should have a fixed length, and consecutive sequences should overlap to ensure that the model can learn the dependencies between words across sequence boundaries.

We will divide the tokenized text into sequences of a fixed length, with an overlap between consecutive sequences.

Example: Creating Training Sequences

import numpy as np

# Define the sequence length and the overlap size
sequence_length = 128
overlap_size = 64

# Function to create training sequences
def create_sequences(tokenized_text, seq_length, overlap):
    total_length = len(tokenized_text)
    sequences = []
    for i in range(0, total_length - seq_length, seq_length - overlap):
        seq = tokenized_text[i:i + seq_length]
        sequences.append(seq)
    return sequences

# Extract the tokenized text from the dataset
tokenized_text = tokenized_dataset["train"]["input_ids"]

# Create training sequences
training_sequences = create_sequences(tokenized_text, sequence_length, overlap_size)

# Print the first training sequence
print(training_sequences[0])

This code imports the numpy library, defines the sequence length and overlap size, and then defines a function to create sequences. It applies this function to a tokenized text from the dataset, creating overlapping sequences of a specified length. The first training sequence is then printed out.

8.1 Data Collection and Preprocessing

In this chapter, we will undertake an exciting project to generate text using autoregressive models. This project will provide a hands-on experience with the entire workflow of building, training, and evaluating an autoregressive model for text generation. By the end of this chapter, you will have a comprehensive understanding of how to apply these models to create coherent and contextually relevant text.

Our project will focus on using the GPT-2 model, a popular autoregressive Transformer-based model, to generate text based on a given prompt. We will cover the following topics in this chapter:

  1. Data Collection and Preprocessing
  2. Model Creation
  3. Training the Model
  4. Generating Text
  5. Evaluating the Model

Let's begin with the first step of our project: data collection and preprocessing.

Data collection and preprocessing are critical steps in any machine learning project. Properly prepared data ensures that the model can learn effectively and generalize well to new data. In this section, we will focus on collecting and preprocessing the text data required for training our autoregressive model.

8.1.1 Collecting the Text Data

For our text generation project, we need a substantial amount of text data. There are various sources from which we can collect text data, such as books, articles, and online content. For simplicity, we will use a dataset of publicly available texts.

We will use the Hugging Face Datasets library to download and load the dataset. The Hugging Face Datasets library provides access to a wide range of text datasets that are commonly used for natural language processing tasks.

Example: Loading a Text Dataset

from datasets import load_dataset

# Load the WikiText-2 dataset
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")

# Print the first example from the training set
print(dataset["train"][0])

8.1.2 Preprocessing the Text Data

Preprocessing the text data involves several steps:

  1. Tokenization: Converting the text into a sequence of tokens (words or subwords).
  2. Normalization: Lowercasing, removing punctuation, and handling special characters.
  3. Sequence Creation: Dividing the text into sequences of a fixed length that can be fed into the model.

We will use the GPT-2 tokenizer provided by the Hugging Face Transformers library for tokenization. This tokenizer is designed to work seamlessly with the GPT-2 model and will handle the necessary preprocessing steps.

Example: Preprocessing the Text Data

from transformers import GPT2Tokenizer

# Load the GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Define a function to preprocess the text data
def preprocess_text(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)

# Apply the preprocessing function to the dataset
tokenized_dataset = dataset.map(preprocess_text, batched=True)

# Print the first tokenized example from the training set
print(tokenized_dataset["train"][0])

This code uses the transformers library to load a GPT-2 tokenizer. The tokenizer is used to preprocess text data from a dataset. The preprocessing function truncates or pads the text to a maximum length of 512 tokens. The tokenized dataset is then created by applying the preprocessing function to the original dataset. The last line of the code prints the first tokenized example from the training set.

8.1.3 Creating Training Sequences

After tokenization, we need to create training sequences that can be fed into the model. Each sequence should have a fixed length, and consecutive sequences should overlap to ensure that the model can learn the dependencies between words across sequence boundaries.

We will divide the tokenized text into sequences of a fixed length, with an overlap between consecutive sequences.

Example: Creating Training Sequences

import numpy as np

# Define the sequence length and the overlap size
sequence_length = 128
overlap_size = 64

# Function to create training sequences
def create_sequences(tokenized_text, seq_length, overlap):
    total_length = len(tokenized_text)
    sequences = []
    for i in range(0, total_length - seq_length, seq_length - overlap):
        seq = tokenized_text[i:i + seq_length]
        sequences.append(seq)
    return sequences

# Extract the tokenized text from the dataset
tokenized_text = tokenized_dataset["train"]["input_ids"]

# Create training sequences
training_sequences = create_sequences(tokenized_text, sequence_length, overlap_size)

# Print the first training sequence
print(training_sequences[0])

This code imports the numpy library, defines the sequence length and overlap size, and then defines a function to create sequences. It applies this function to a tokenized text from the dataset, creating overlapping sequences of a specified length. The first training sequence is then printed out.