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Generative Deep Learning Updated Edition

Chapter 8: Project: Text Generation with Autoregressive Models

8.2 Model Creation

In this section, we will focus on creating the autoregressive model for our text generation project. We will use the GPT-2 model, a well-known Transformer-based model, which has proven to be highly effective for text generation tasks. We will leverage the Hugging Face Transformers library to load and configure the GPT-2 model for our specific needs.

8.2.1 Loading the Pre-trained GPT-2 Model

The first step in model creation is to load a pre-trained GPT-2 model. Using a pre-trained model allows us to benefit from the vast amounts of data the model has already been trained on, making it easier to fine-tune it for our specific task.

Example: Loading the Pre-trained GPT-2 Model

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Print the model architecture
model.summary()

8.2.2 Configuring the Model for Fine-Tuning

To adapt the GPT-2 model for our text generation task, we need to configure it for fine-tuning. This involves setting up the training parameters and ensuring that the model's architecture aligns with our data.

Key Configurations:

  • Learning Rate: Determines how quickly the model adjusts its weights during training.
  • Batch Size: Number of training samples used in one iteration.
  • Number of Epochs: Number of times the model will go through the entire training dataset.

Example: Configuring the Model for Fine-Tuning

from transformers import Trainer, TrainingArguments

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=2,
    save_steps=10_000,
    save_total_limit=2,
    logging_dir='./logs',
)

# Print training arguments to verify configuration
print(training_args)

The code uses the TrainingArguments class from the 'transformers' library to define several important training parameters:

  • output_dir specifies the directory where the training outputs (like the trained model) will be stored.
  • overwrite_output_dir is a boolean parameter which, when set to 'True', allows the script to overwrite existing files in the output directory.
  • num_train_epochs determines the number of passes (epochs) over the entire training data.
  • per_device_train_batch_size defines the number of examples per batch of data for training. This can impact both the speed of training and the quality of the model.
  • save_steps determines after how many steps the model checkpoint would be saved.
  • save_total_limit limits the total amount of checkpoints that can be kept on the disk.
  • logging_dir is the directory for storing logs generated during training.

After defining these arguments, the script prints them out to verify their values before proceeding with the training. This helps ensure that the parameters are set as intended, and can be particularly useful when troubleshooting or optimizing the training process.

8.2.3 Creating a Custom Dataset for Fine-Tuning

To fine-tune the model, we need to create a custom dataset that can be fed into the Trainer API provided by the Hugging Face Transformers library. This dataset will use the preprocessed text data we prepared earlier.

Example: Creating a Custom Dataset

import torch
from torch.utils.data import Dataset

class TextDataset(Dataset):
    def __init__(self, sequences):
        self.sequences = sequences

    def __len__(self):
        return len(self.sequences)

    def __getitem__(self, idx):
        item = torch.tensor(self.sequences[idx])
        return {"input_ids": item, "labels": item}

# Create an instance of the custom dataset
train_dataset = TextDataset(training_sequences)

# Print the first example from the dataset
print(train_dataset[0])

This example imports the necessary libraries and defines a custom text dataset class using PyTorch. The class, TextDataset, takes in a list of sequences as input. It has three main methods: __init____len__, and __getitem__.

__init__ initializes the class with the sequences input. __len__ returns the total number of sequences in the dataset. __getitem__ allows the class to be indexed, returning a dictionary with 'input_ids' and 'labels' keys, both having the same sequence tensor as their value.

After the class definition, an instance of the dataset is created using 'training_sequences' and the first item in the dataset is printed.

8.2.4 Initializing the Trainer

The Trainer API simplifies the training process by handling many of the details involved in training and evaluating the model. We will initialize the Trainer with our model, training arguments, and custom dataset.

Example: Initializing the Trainer

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Print the Trainer configuration to verify initialization
print(trainer)

8.2.5 Fine-Tuning the Model

With the Trainer initialized, we can now fine-tune the GPT-2 model on our custom dataset. Fine-tuning involves training the model on the new data while leveraging the pre-trained weights to improve performance on the specific task.

Example: Fine-Tuning the Model

# Fine-tune the GPT-2 model
trainer.train()

8.2 Model Creation

In this section, we will focus on creating the autoregressive model for our text generation project. We will use the GPT-2 model, a well-known Transformer-based model, which has proven to be highly effective for text generation tasks. We will leverage the Hugging Face Transformers library to load and configure the GPT-2 model for our specific needs.

8.2.1 Loading the Pre-trained GPT-2 Model

The first step in model creation is to load a pre-trained GPT-2 model. Using a pre-trained model allows us to benefit from the vast amounts of data the model has already been trained on, making it easier to fine-tune it for our specific task.

Example: Loading the Pre-trained GPT-2 Model

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Print the model architecture
model.summary()

8.2.2 Configuring the Model for Fine-Tuning

To adapt the GPT-2 model for our text generation task, we need to configure it for fine-tuning. This involves setting up the training parameters and ensuring that the model's architecture aligns with our data.

Key Configurations:

  • Learning Rate: Determines how quickly the model adjusts its weights during training.
  • Batch Size: Number of training samples used in one iteration.
  • Number of Epochs: Number of times the model will go through the entire training dataset.

Example: Configuring the Model for Fine-Tuning

from transformers import Trainer, TrainingArguments

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=2,
    save_steps=10_000,
    save_total_limit=2,
    logging_dir='./logs',
)

# Print training arguments to verify configuration
print(training_args)

The code uses the TrainingArguments class from the 'transformers' library to define several important training parameters:

  • output_dir specifies the directory where the training outputs (like the trained model) will be stored.
  • overwrite_output_dir is a boolean parameter which, when set to 'True', allows the script to overwrite existing files in the output directory.
  • num_train_epochs determines the number of passes (epochs) over the entire training data.
  • per_device_train_batch_size defines the number of examples per batch of data for training. This can impact both the speed of training and the quality of the model.
  • save_steps determines after how many steps the model checkpoint would be saved.
  • save_total_limit limits the total amount of checkpoints that can be kept on the disk.
  • logging_dir is the directory for storing logs generated during training.

After defining these arguments, the script prints them out to verify their values before proceeding with the training. This helps ensure that the parameters are set as intended, and can be particularly useful when troubleshooting or optimizing the training process.

8.2.3 Creating a Custom Dataset for Fine-Tuning

To fine-tune the model, we need to create a custom dataset that can be fed into the Trainer API provided by the Hugging Face Transformers library. This dataset will use the preprocessed text data we prepared earlier.

Example: Creating a Custom Dataset

import torch
from torch.utils.data import Dataset

class TextDataset(Dataset):
    def __init__(self, sequences):
        self.sequences = sequences

    def __len__(self):
        return len(self.sequences)

    def __getitem__(self, idx):
        item = torch.tensor(self.sequences[idx])
        return {"input_ids": item, "labels": item}

# Create an instance of the custom dataset
train_dataset = TextDataset(training_sequences)

# Print the first example from the dataset
print(train_dataset[0])

This example imports the necessary libraries and defines a custom text dataset class using PyTorch. The class, TextDataset, takes in a list of sequences as input. It has three main methods: __init____len__, and __getitem__.

__init__ initializes the class with the sequences input. __len__ returns the total number of sequences in the dataset. __getitem__ allows the class to be indexed, returning a dictionary with 'input_ids' and 'labels' keys, both having the same sequence tensor as their value.

After the class definition, an instance of the dataset is created using 'training_sequences' and the first item in the dataset is printed.

8.2.4 Initializing the Trainer

The Trainer API simplifies the training process by handling many of the details involved in training and evaluating the model. We will initialize the Trainer with our model, training arguments, and custom dataset.

Example: Initializing the Trainer

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Print the Trainer configuration to verify initialization
print(trainer)

8.2.5 Fine-Tuning the Model

With the Trainer initialized, we can now fine-tune the GPT-2 model on our custom dataset. Fine-tuning involves training the model on the new data while leveraging the pre-trained weights to improve performance on the specific task.

Example: Fine-Tuning the Model

# Fine-tune the GPT-2 model
trainer.train()

8.2 Model Creation

In this section, we will focus on creating the autoregressive model for our text generation project. We will use the GPT-2 model, a well-known Transformer-based model, which has proven to be highly effective for text generation tasks. We will leverage the Hugging Face Transformers library to load and configure the GPT-2 model for our specific needs.

8.2.1 Loading the Pre-trained GPT-2 Model

The first step in model creation is to load a pre-trained GPT-2 model. Using a pre-trained model allows us to benefit from the vast amounts of data the model has already been trained on, making it easier to fine-tune it for our specific task.

Example: Loading the Pre-trained GPT-2 Model

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Print the model architecture
model.summary()

8.2.2 Configuring the Model for Fine-Tuning

To adapt the GPT-2 model for our text generation task, we need to configure it for fine-tuning. This involves setting up the training parameters and ensuring that the model's architecture aligns with our data.

Key Configurations:

  • Learning Rate: Determines how quickly the model adjusts its weights during training.
  • Batch Size: Number of training samples used in one iteration.
  • Number of Epochs: Number of times the model will go through the entire training dataset.

Example: Configuring the Model for Fine-Tuning

from transformers import Trainer, TrainingArguments

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=2,
    save_steps=10_000,
    save_total_limit=2,
    logging_dir='./logs',
)

# Print training arguments to verify configuration
print(training_args)

The code uses the TrainingArguments class from the 'transformers' library to define several important training parameters:

  • output_dir specifies the directory where the training outputs (like the trained model) will be stored.
  • overwrite_output_dir is a boolean parameter which, when set to 'True', allows the script to overwrite existing files in the output directory.
  • num_train_epochs determines the number of passes (epochs) over the entire training data.
  • per_device_train_batch_size defines the number of examples per batch of data for training. This can impact both the speed of training and the quality of the model.
  • save_steps determines after how many steps the model checkpoint would be saved.
  • save_total_limit limits the total amount of checkpoints that can be kept on the disk.
  • logging_dir is the directory for storing logs generated during training.

After defining these arguments, the script prints them out to verify their values before proceeding with the training. This helps ensure that the parameters are set as intended, and can be particularly useful when troubleshooting or optimizing the training process.

8.2.3 Creating a Custom Dataset for Fine-Tuning

To fine-tune the model, we need to create a custom dataset that can be fed into the Trainer API provided by the Hugging Face Transformers library. This dataset will use the preprocessed text data we prepared earlier.

Example: Creating a Custom Dataset

import torch
from torch.utils.data import Dataset

class TextDataset(Dataset):
    def __init__(self, sequences):
        self.sequences = sequences

    def __len__(self):
        return len(self.sequences)

    def __getitem__(self, idx):
        item = torch.tensor(self.sequences[idx])
        return {"input_ids": item, "labels": item}

# Create an instance of the custom dataset
train_dataset = TextDataset(training_sequences)

# Print the first example from the dataset
print(train_dataset[0])

This example imports the necessary libraries and defines a custom text dataset class using PyTorch. The class, TextDataset, takes in a list of sequences as input. It has three main methods: __init____len__, and __getitem__.

__init__ initializes the class with the sequences input. __len__ returns the total number of sequences in the dataset. __getitem__ allows the class to be indexed, returning a dictionary with 'input_ids' and 'labels' keys, both having the same sequence tensor as their value.

After the class definition, an instance of the dataset is created using 'training_sequences' and the first item in the dataset is printed.

8.2.4 Initializing the Trainer

The Trainer API simplifies the training process by handling many of the details involved in training and evaluating the model. We will initialize the Trainer with our model, training arguments, and custom dataset.

Example: Initializing the Trainer

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Print the Trainer configuration to verify initialization
print(trainer)

8.2.5 Fine-Tuning the Model

With the Trainer initialized, we can now fine-tune the GPT-2 model on our custom dataset. Fine-tuning involves training the model on the new data while leveraging the pre-trained weights to improve performance on the specific task.

Example: Fine-Tuning the Model

# Fine-tune the GPT-2 model
trainer.train()

8.2 Model Creation

In this section, we will focus on creating the autoregressive model for our text generation project. We will use the GPT-2 model, a well-known Transformer-based model, which has proven to be highly effective for text generation tasks. We will leverage the Hugging Face Transformers library to load and configure the GPT-2 model for our specific needs.

8.2.1 Loading the Pre-trained GPT-2 Model

The first step in model creation is to load a pre-trained GPT-2 model. Using a pre-trained model allows us to benefit from the vast amounts of data the model has already been trained on, making it easier to fine-tune it for our specific task.

Example: Loading the Pre-trained GPT-2 Model

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the GPT-2 tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Print the model architecture
model.summary()

8.2.2 Configuring the Model for Fine-Tuning

To adapt the GPT-2 model for our text generation task, we need to configure it for fine-tuning. This involves setting up the training parameters and ensuring that the model's architecture aligns with our data.

Key Configurations:

  • Learning Rate: Determines how quickly the model adjusts its weights during training.
  • Batch Size: Number of training samples used in one iteration.
  • Number of Epochs: Number of times the model will go through the entire training dataset.

Example: Configuring the Model for Fine-Tuning

from transformers import Trainer, TrainingArguments

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=2,
    save_steps=10_000,
    save_total_limit=2,
    logging_dir='./logs',
)

# Print training arguments to verify configuration
print(training_args)

The code uses the TrainingArguments class from the 'transformers' library to define several important training parameters:

  • output_dir specifies the directory where the training outputs (like the trained model) will be stored.
  • overwrite_output_dir is a boolean parameter which, when set to 'True', allows the script to overwrite existing files in the output directory.
  • num_train_epochs determines the number of passes (epochs) over the entire training data.
  • per_device_train_batch_size defines the number of examples per batch of data for training. This can impact both the speed of training and the quality of the model.
  • save_steps determines after how many steps the model checkpoint would be saved.
  • save_total_limit limits the total amount of checkpoints that can be kept on the disk.
  • logging_dir is the directory for storing logs generated during training.

After defining these arguments, the script prints them out to verify their values before proceeding with the training. This helps ensure that the parameters are set as intended, and can be particularly useful when troubleshooting or optimizing the training process.

8.2.3 Creating a Custom Dataset for Fine-Tuning

To fine-tune the model, we need to create a custom dataset that can be fed into the Trainer API provided by the Hugging Face Transformers library. This dataset will use the preprocessed text data we prepared earlier.

Example: Creating a Custom Dataset

import torch
from torch.utils.data import Dataset

class TextDataset(Dataset):
    def __init__(self, sequences):
        self.sequences = sequences

    def __len__(self):
        return len(self.sequences)

    def __getitem__(self, idx):
        item = torch.tensor(self.sequences[idx])
        return {"input_ids": item, "labels": item}

# Create an instance of the custom dataset
train_dataset = TextDataset(training_sequences)

# Print the first example from the dataset
print(train_dataset[0])

This example imports the necessary libraries and defines a custom text dataset class using PyTorch. The class, TextDataset, takes in a list of sequences as input. It has three main methods: __init____len__, and __getitem__.

__init__ initializes the class with the sequences input. __len__ returns the total number of sequences in the dataset. __getitem__ allows the class to be indexed, returning a dictionary with 'input_ids' and 'labels' keys, both having the same sequence tensor as their value.

After the class definition, an instance of the dataset is created using 'training_sequences' and the first item in the dataset is printed.

8.2.4 Initializing the Trainer

The Trainer API simplifies the training process by handling many of the details involved in training and evaluating the model. We will initialize the Trainer with our model, training arguments, and custom dataset.

Example: Initializing the Trainer

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Print the Trainer configuration to verify initialization
print(trainer)

8.2.5 Fine-Tuning the Model

With the Trainer initialized, we can now fine-tune the GPT-2 model on our custom dataset. Fine-tuning involves training the model on the new data while leveraging the pre-trained weights to improve performance on the specific task.

Example: Fine-Tuning the Model

# Fine-tune the GPT-2 model
trainer.train()