Category: Using Hugging Face

Recently, transformer-based models have been gaining attention in the field of Natural Language Processing (NLP) due to their outstanding performance. Among them, BigBird, developed by Google, is an innovative architecture designed for large-scale document understanding and processing long sequences. In this course, we will learn how to set up the BigBird model using Hugging Face’s transformers library and how to load a pre-trained model.

1. What is BigBird?

BigBird is a model designed as an extension of the Transformer model, particularly created to efficiently handle long sequence data. Traditional Transformer models have limitations on the length of the input sequence, usually processing only up to about 512 tokens of text. In contrast, BigBird overcomes this limitation using a sparse attention mechanism. This is useful for various NLP tasks such as document summarization, question answering, and text classification.

1.1 Key Features of BigBird

• Ability to process long input sequences
• Reduces memory consumption and improves processing speed
• Easy to apply to various NLP tasks by utilizing pre-trained models

2. Setting Up the Environment

To use the BigBird model, you need to set up your Python environment. Follow the steps below to proceed with the installation.

2.1 Installing Python and pip

You need Python version 3.6 or higher. You can install Python and pip with the following commands:

sudo apt update
sudo apt install python3 python3-pip

2.2 Installing Hugging Face Transformers Library

Use the command below to install Hugging Face’s transformers library:

pip install transformers

2.3 Installing Additional Libraries

Additional libraries also need to be installed to use the BigBird model:

pip install torch

3. Loading the Pre-Trained Model

Now that all the settings are complete, we are ready to load and use the BigBird model. We will use Hugging Face’s transformers library for this.

3.1 Text Summarization

Let’s take a look at an example of text summarization using the BigBird model. Refer to the code below:

from transformers import BigBirdTokenizer, BigBirdForSequenceClassification

# Load the tokenizer and model
tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
model = BigBirdForSequenceClassification.from_pretrained('google/bigbird-roberta-base')

# Input text
text = "Deep learning is a branch of machine learning that utilizes artificial neural networks. It is used to learn patterns from data and make predictions and decisions based on this."

# Tokenize the text and convert it to tensor
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Model prediction
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()

print(f"Predicted class: {predicted_class}")

Code Explanation

In the code above, we use the BigBirdTokenizer and BigBirdForSequenceClassification classes to load the pre-trained BigBird model and tokenizer.

• We load Google’s pre-trained BigBird model using the from_pretrained method.
• To tokenize the input text, we use tokenizer to convert the text into a tensor.
• To check the model’s prediction results, we perform an argmax operation on the output logits to predict the class.

3.2 Training the Model

Now, let’s look at how to further train the pre-trained model on a specific dataset. Below is a code showing a simple training routine:

from transformers import Trainer, TrainingArguments
from datasets import load_dataset

# Load the dataset (e.g., IMDB sentiment analysis dataset)
dataset = load_dataset('imdb')

# Set training arguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=1,
    per_device_train_batch_size=8,
    save_steps=10_000,
    save_total_limit=2,
)

# Create a Trainer object
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset['train'],
    eval_dataset=dataset['test'],
)

# Train the model
trainer.train()

Code Explanation

In the code above, we load the IMDB sentiment analysis dataset using the datasets library. We perform training on the BigBird model based on this dataset:

• We specify various training settings (epochs, batch size, etc.) using TrainingArguments.
• The Trainer class allows us to perform training and evaluation.

4. Summary

In this course, we learned how to set up the BigBird model using the Hugging Face transformers library and how to load a pre-trained model. BigBird is a powerful tool that can efficiently process long input sequences. By applying it to various NLP tasks, we can significantly enhance performance, and we can optimize the model through fine-tuning for specific tasks.

We hope you continue exploring how to utilize models like BigBird in various deep learning projects. If you need additional materials or have questions, please leave a comment! Thank you.

Recently, natural language processing (NLP) has become a major challenge in the field of artificial intelligence, with models like BERT (Bidirectional Encoder Representations from Transformers) leading innovations in this area. The BERT model provides the ability to understand the context of words in both directions, enabling more sophisticated approaches to solving natural language problems. In this course, we will explore how to set up the BERT model using Hugging Face’s Transformers library and implement ensemble learning.

1. What is BERT Ensemble Learning?

Ensemble learning is a methodology that combines the predictions of multiple models to create a final prediction. This can be done by averaging the predictions of several models or by using majority voting, which helps reduce the bias of a single model and improves generalization performance. Leveraging multiple powerful language models such as BERT in an ensemble can maximize the learning and prediction performance of the models.

2. Environment Setup

To use Hugging Face’s Transformers library, you first need to install the necessary packages. You can install them using the following command.

pip install transformers torch

Additionally, we will use pandas for data processing and scikit-learn for model performance evaluation.

pip install pandas scikit-learn

3. Data Preparation

In this course, we will use a movie review sentiment analysis dataset. This dataset contains reviews and sentiment labels, distinguishing between positive and negative reviews. The dataset can be loaded using pandas.

import pandas as pd

# Load dataset
data = pd.read_csv('movie_reviews.csv')
print(data.head())

4. BERT Model Setup

We will set up the BERT model using Hugging Face’s Transformers library. To use BERT, we first need to load the model and set up the tokenizer to process the input data.

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Tokenizing input data
def tokenize_data(sentences):
    return tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Tokenizing the first sentence as an example
tokens = tokenize_data(data['review'].tolist())
print(tokens)  # Check tokenized data

5. Data Preprocessing

Data preprocessing is necessary for model training. Each review will be tokenized and converted into a format that the model can recognize. Additionally, a batch size will be set to improve training speed on the GPU.

from torch.utils.data import DataLoader, TensorDataset

# Setting input data and labels
inputs = tokens['input_ids']
attn_masks = tokens['attention_mask']
labels = torch.tensor(data['label'].tolist())

# Creating tensor dataset
dataset = TensorDataset(inputs, attn_masks, labels)

# Setting data loader
batch_size = 16
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

6. Model Training

To train the BERT model, we need to set up the optimizer and loss function. Here, we will use the AdamW optimizer and CrossEntropyLoss as the loss function for model training.

from transformers import AdamW
from torch import nn

# Setting optimizer
optimizer = AdamW(model.parameters(), lr=5e-5)

# Setting loss function
loss_fn = nn.CrossEntropyLoss()

# Function for training the model
def train_model(dataloader, model, optimizer, loss_fn, epochs=3):
    model.train()
    for epoch in range(epochs):
        total_loss = 0
        for batch in dataloader:
            input_ids, attention_masks, labels = batch
            
            # Sending data to model
            input_ids = input_ids.to('cuda')
            attention_masks = attention_masks.to('cuda')
            labels = labels.to('cuda')
            
            # Initializing gradients
            optimizer.zero_grad()
            
            # Model prediction
            outputs = model(input_ids, token_type_ids=None, attention_mask=attention_masks)
            loss = loss_fn(outputs.logits, labels)
            
            # Calculating loss and backpropagation
            total_loss += loss.item()
            loss.backward()
            optimizer.step()
        print(f'Epoch: {epoch+1}, Loss: {total_loss/len(dataloader)}')

# Training the model
train_model(dataloader, model.to('cuda'), optimizer, loss_fn)

7. Ensemble Model Setup

Having set up the basic BERT model, we will now ensemble multiple BERT models to enhance performance. Here, we will train two BERT models and average their predictions for the final prediction.

def create_ensemble_model(num_models=2):
    models = []
    for _ in range(num_models):
        model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2).to('cuda')
        models.append(model)
    return models

# Creating ensemble model
ensemble_models = create_ensemble_model()

8. Ensemble Training and Prediction

We will train the ensemble models, perform predictions on the test data, and then average the results to create the final prediction.

def train_ensemble(models, dataloader, optimizer, loss_fn, epochs=3):
    for model in models:
        train_model(dataloader, model, optimizer, loss_fn, epochs)

def ensemble_predict(models, input_ids, attention_masks):
    preds = []
    for model in models:
        model.eval()
        with torch.no_grad():
            outputs = model(input_ids, attention_mask=attention_masks)
            preds.append(outputs.logits)
    return sum(preds) / len(preds)

# Training ensemble model
train_ensemble(ensemble_models, dataloader, optimizer, loss_fn)

# Predicting the first sentence as an example
inputs = tokenize_data([data['review'].iloc[0]])
average_logits = ensemble_predict(ensemble_models, inputs['input_ids'].to('cuda'), inputs['attention_mask'].to('cuda'))
predictions = torch.argmax(average_logits, dim=1)
print(f'Predicted label: {predictions}')  # Check prediction result

9. Model Performance Evaluation

Finally, we will evaluate the model’s performance on the test dataset. We will measure accuracy, precision, recall, etc., to review performance.

from sklearn.metrics import accuracy_score, classification_report

# Load test data
test_data = pd.read_csv('movie_reviews_test.csv')
test_tokens = tokenize_data(test_data['review'].tolist())
test_inputs = test_tokens['input_ids'].to('cuda')
test_masks = test_tokens['attention_mask'].to('cuda')

# Ensemble prediction
test_logits = ensemble_predict(ensemble_models, test_inputs, test_masks)
test_predictions = torch.argmax(test_logits, axis=1)

# Output accuracy and evaluation metrics
accuracy = accuracy_score(test_data['label'].tolist(), test_predictions.cpu())
report = classification_report(test_data['label'].tolist(), test_predictions.cpu())

print(f'Accuracy: {accuracy}\n')
print(report)

Conclusion

In this course, we explored how to use the BERT model to solve natural language processing problems, as well as how to enhance performance by ensembling multiple models. With Hugging Face’s Transformers library, applying the BERT model is straightforward, and through custom ensemble modeling, we can expect even stronger performance. I hope to continue utilizing such technologies in various natural language processing problems in the future.

Introduction

Deep learning has brought about innovations in the field of Natural Language Processing (NLP) in recent years. In particular, the BERT (Bidirectional Encoder Representations from Transformers) model demonstrates powerful performance in understanding context and has achieved state-of-the-art results across various NLP tasks. This article will detail how to implement ensemble learning of the BERT model using Hugging Face’s Transformers library and define a custom dataset.

1. Introduction to Hugging Face Transformers

Hugging Face creates various advanced libraries to make NLP models easily accessible. In particular, the Transformers library simplifies the use of several state-of-the-art models, such as BERT, GPT-2, and T5. Using this library allows for the simplification of complex neural network architectures.

1.1 What is BERT?

BERT is a bidirectional transformer encoder that can effectively grasp the relationships between words in a sentence. BERT is trained in two main steps: Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). Thanks to this training methodology, BERT understands context and performs exceptionally well in various NLP tasks.

2. The Concept of Ensemble Learning

Ensemble learning is a technique that combines multiple models to achieve better predictive performance. It reduces the bias of individual models and enhances performance through model diversity. Common ensemble methods include Bagging and Boosting. We will explore combining the strengths of different models through ensemble learning of the BERT model.

3. Environment Setup

In this course, we will use Python and the Hugging Face Transformers library. To install the necessary packages, enter the following command in the terminal.

pip install transformers datasets torch

4. Defining a Custom Dataset

To train an NLP model, a properly formatted dataset is required. This section will explain how to define a custom dataset.

4.1 Dataset Format

A dataset generally consists of text and corresponding labels. The dataset we will use will be prepared in CSV format. For example, it should follow the format below.

    text,label
    "This movie was really interesting.",1
    "It was not great.",0
    

4.2 Loading Data

Now, let’s write code to load the custom dataset. We can easily load it using Hugging Face’s datasets library.

import pandas as pd
from datasets import Dataset

# Load data from CSV file
data = pd.read_csv('custom_dataset.csv')
dataset = Dataset.from_pandas(data)
    

5. Configuring and Training the BERT Model

Now that the dataset is prepared, let’s move on to configuring and training the BERT model. The Hugging Face Transformers library makes it easy to use the BERT model.

5.1 Loading the BERT Model

The following code demonstrates how to load the BERT model and tokenizer.

from transformers import BertTokenizer, BertForSequenceClassification

# Load the model and tokenizer
model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
    

5.2 Data Preprocessing

Before inputting data into the BERT model, data preprocessing must be performed. We typically use the following code to tokenize input text and pad and truncate it to an appropriate format.

def preprocess_function(examples):
    return tokenizer(examples['text'], padding='max_length', truncation=True)

# Perform data preprocessing
tokenized_dataset = dataset.map(preprocess_function, batched=True)
    

5.3 Training the Model

With data preprocessing complete, we are ready to train the model. We will use the trainer API to perform training and evaluation.

from transformers import Trainer, TrainingArguments

# Set training arguments
training_args = TrainingArguments(
    output_dir='./results',
    evaluation_strategy='epoch',
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    num_train_epochs=3,
)

# Create Trainer object
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
)

# Train the model
trainer.train()
    

6. Implementing Ensemble Models

This process involves enhancing performance by combining several BERT models. We will combine the predictions of each model to derive the final prediction. Let’s train two or more models and combine their results.

6.1 Training Multiple Models

# Train two BERT models
model1 = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
model2 = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Perform training on each
trainer1 = Trainer(
    model=model1,
    args=training_args,
    train_dataset=tokenized_dataset,
)

trainer2 = Trainer(
    model=model2,
    args=training_args,
    train_dataset=tokenized_dataset,
)

trainer1.train()
trainer2.train()
    

6.2 Performing Ensemble Predictions

The ensemble prediction results are derived by averaging the predictions of the two models.

import numpy as np

# Perform predictions
preds1 = trainer1.predict(tokenized_dataset)['logits']
preds2 = trainer2.predict(tokenized_dataset)['logits']

# Perform ensemble prediction
final_preds = (preds1 + preds2) / 2
final_predictions = np.argmax(final_preds, axis=1)
    

7. Evaluating Results

Evaluating the performance of the model is important, and we can assess it using accuracy and F1 scores.

from sklearn.metrics import accuracy_score, f1_score

# Evaluate performance by comparing labels and predictions
true_labels = tokenized_dataset['label']
accuracy = accuracy_score(true_labels, final_predictions)
f1 = f1_score(true_labels, final_predictions)

print(f'Accuracy: {accuracy}')
print(f'F1 Score: {f1}')
    

Conclusion

In this course, we explored the process of performing ensemble learning of the BERT model using Hugging Face’s Transformers library. We learned about defining a custom dataset, configuring and training models, and ensemble techniques, gaining insights into how to improve the performance of deep learning models. Through this process, I hope readers have gained a deeper understanding of how to use the BERT model and the concept of ensemble learning.

With the advancement of deep learning, innovative models have also emerged in the field of Natural Language Processing (NLP). One of them is BERT (Bidirectional Encoder Representations from Transformers). BERT understands bidirectional context and demonstrates outstanding performance in NLP tasks. In this article, we will take a closer look at how to perform ensemble learning using BERT with Hugging Face’s Transformers library. In particular, we will focus on the data loading part and explain how to quickly handle various datasets.

1. What is BERT?

BERT is a model announced by Google, providing pretrained context-based embeddings and showing excellent performance in many NLP tasks. BERT operates relying on two main technologies:

• Bidirectionality: It captures richer meanings by considering context from both left and right sides simultaneously.
• Masked Language Model (Masked LM): It randomly masks words in the input data and trains the model to predict those masked words.

Through this, BERT demonstrates better performance than traditional models in various NLP tasks, such as sentence classification, sentiment analysis, named entity recognition, etc.

2. The Necessity of Ensemble Learning

Ensemble learning is a technique that combines the predictions of several models to improve performance. It has superior generalization capabilities compared to single models and helps reduce overfitting. Even when using complex models like BERT, improvements in performance can be expected through ensemble learning.

3. Introduction to Hugging Face Transformers Library

Hugging Face’s Transformers library provides various pretrained NLP models and is a powerful tool that helps users easily load and train these models. This library allows for straightforward use of several transformer models, including BERT.

4. Overview of DataLoader

Efficiently loading datasets is crucial for training deep learning models. DataLoader loads data in batches and maximizes training speed. In Hugging Face’s Transformers library, the Dataset and DataLoader classes help perform this process easily.

4.1 Dataset Class

The Dataset class from Hugging Face defines a standard structure for datasets. This allows for easy data preprocessing and batch generation. By inheriting from the Dataset class, users can implement it in a way that suits their datasets.

4.2 DataLoader Class

The DataLoader is a utility that generates batches and samples from the given dataset. It helps efficiently load data through parameters such as shuffle and batch_size.

5. Practice: Implementing DataLoader for BERT Ensemble Learning

Now, let’s practice using DataLoader to perform ensemble learning with the BERT model. Here is the overall flow:

1. Install and import necessary libraries
2. Prepare the dataset
3. Define the Dataset class
4. Load data using DataLoader
5. Train BERT model and implement ensemble learning

5.1 Installing and Importing Necessary Libraries

First, we will install and import the necessary libraries. Here is how to proceed:

!pip install transformers datasets torch

import torch
from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizer, BertForSequenceClassification, AdamW
from datasets import load_dataset

5.2 Preparing the Dataset

In this example, we will use the datasets library to obtain a movie review dataset. This dataset consists of positive and negative reviews:

dataset = load_dataset("imdb")
train_texts = dataset['train']['text']
train_labels = dataset['train']['label']

5.3 Defining the Dataset Class

We will define a Dataset class that performs preprocessing of the data to input to the BERT model:

class IMDBDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_length):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_length = max_length

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

    def __getitem__(self, index):
        text = self.texts[index]
        label = self.labels[index]
        encoding = self.tokenizer.encode_plus(
            text,
            truncation=True,
            max_length=self.max_length,
            padding='max_length',
            return_tensors='pt',
        )
        return {
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }

5.4 Loading Data Using DataLoader

Now we will create a data loader using the previously defined IMDBDataset class:

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
max_length = 256
train_dataset = IMDBDataset(train_texts, train_labels, tokenizer, max_length)

train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

5.5 Training BERT Model and Implementing Ensemble Learning

Now we will look at how to train the BERT model and implement ensembling. First, we load the BERT model and set up the optimizer:

model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
optimizer = AdamW(model.parameters(), lr=2e-5)

During the training process, we learn from multiple batches over several epochs:

model.train()
for epoch in range(3):  # Training over several epochs
    for batch in train_loader:
        input_ids = batch['input_ids']
        attention_mask = batch['attention_mask']
        labels = batch['labels']

        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()

        print(f"Epoch: {epoch}, Loss: {loss.item()}")

To implement ensemble learning, we can train several BERT models and average their predictions. This can reinforce performance:

# Training multiple models
num_models = 5
models = [BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) for _ in range(num_models)]
# Train each model (repeat training process above)
# Ensemble predictions
predictions = []

for model in models:
    model.eval()
    for batch in train_loader:
        input_ids = batch['input_ids']
        attention_mask = batch['attention_mask']

        with torch.no_grad():
            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits
            predictions.append(logits.argmax(dim=1).cpu().numpy())

# Calculating average predictions
ensemble_prediction = np.mean(predictions, axis=0)

6. Conclusion

In this tutorial, we explored how to implement a DataLoader for ensemble learning with the BERT model using Hugging Face’s Transformers library. Understanding how to improve data loading efficiency and train various models to maximize performance is important. Experience how effective ensemble techniques utilizing powerful models like BERT can be in NLP tasks.

Through this tutorial, we hope you have gained not only the foundational knowledge needed to utilize BERT in the field of natural language processing but also practical example code. Continue to study and experiment with deep learning to develop models that deliver top performance!

1. Introduction

In modern natural language processing (NLP), BERT has established itself as an innovative model. BERT stands for Bidirectional Encoder Representations from Transformers and possesses a powerful ability to understand bidirectional context. Utilizing BERT is essential in developing deep learning-based NLP models, particularly enhancing model performance through ensemble learning and data augmentation techniques. This course will cover how to maximize the performance of the BERT model using Hugging Face’s Transformers library through ensemble learning methods and data augmentation techniques.

2. BERT: Overview

BERT uses an architecture called Transformer to understand the context of words. The most significant feature of BERT is its bidirectionality in understanding the relationships between tokens. While traditional RNN-based models process words sequentially, BERT can consider the context of all words in a sentence simultaneously.

3. Introduction to Hugging Face Transformers Library

The Hugging Face Transformers library is a Python library designed to make various Transformer models easily accessible. It supports not only BERT but also other state-of-the-art models like GPT and T5. Through this library, we can easily load pre-trained models and fine-tune them to fit our data.

4. Importance of Data Augmentation

Data augmentation is a critical technique for enhancing machine learning performance. Especially in NLP, when data is scarce, generating new data or transforming existing data can enhance the model’s generalization performance. Various techniques exist for data augmentation, and this course will focus specifically on methods for augmenting text data.

5. BERT Ensemble Learning

Ensemble learning is a technique that improves performance by combining multiple models. Generally, the final result is derived by combining the predictions of several models. In BERT ensemble learning, we can improve performance by combining the outputs of multiple BERT models trained with different hyperparameters.

6. Environment Setup

!pip install transformers torch

The above command installs the Hugging Face Transformers library and PyTorch. These libraries help load the BERT model and assist in data preprocessing.

7. Data Preparation and Preprocessing

In this course, we will deal with a simple text classification problem. We will assume that the data is as follows.

data = {
    'text': ['This movie is really good.', 'It was the worst movie.', 'It is a really interesting movie.', 'This movie is boring.'],
    'label': [1, 0, 1, 0]  # 1: Positive, 0: Negative
}
    

8. Data Augmentation Techniques

Among various data augmentation techniques, we will use the following methods:

• Key Word Replacement: Replaces specific words with synonyms to generate new sentences.
• Random Insertion: Inserts a randomly selected word into the existing sentence to create a new sentence.
• Random Deletion: Randomly removes specific words to modify the sentence.

8.1 Key Word Replacement Example

import random
from nltk.corpus import wordnet

def synonym_replacement(text):
    words = text.split()
    new_words = words.copy()
    random_word_idx = random.randint(0, len(words)-1)
    word = words[random_word_idx]
    
    synonyms = wordnet.synsets(word)
    if synonyms:
        synonym = synonyms[0].lemmas()[0].name()
        new_words[random_word_idx] = synonym.replace('_', ' ')
        
    return ' '.join(new_words)
    

8.2 Random Insertion Example

def random_insertion(text):
    words = text.split()
    new_words = words.copy()
    random_word = random.choice(words)
    new_words.insert(random.randint(0, len(new_words)-1), random_word)
    return ' '.join(new_words)
    

8.3 Random Deletion Example

def random_deletion(text, p=0.5):
    words = text.split()
    if len(words) == 1:  # only one word, it's better not to drop it
        return text
    
    remaining = list(filter(lambda x: random.random() > p, words))
    return ' '.join(remaining) if len(remaining) > 0 else ' '.join(random.sample(words, 1))
    

9. Applying Data Augmentation

Now let’s apply data augmentation to the collected data.

augmented_texts = []
augmented_labels = []

for index, row in enumerate(data['text']):
    augmented_texts.append(row)  # Add original data
    augmented_labels.append(data['label'][index])  # Add corresponding label
    
    # Data augmentation
    augmented_texts.append(synonym_replacement(row))
    augmented_labels.append(data['label'][index])
    
    augmented_texts.append(random_insertion(row))
    augmented_labels.append(data['label'][index])
    
    augmented_texts.append(random_deletion(row))
    augmented_labels.append(data['label'][index])

print("Number of augmented data:", len(augmented_texts))
    

10. Training the BERT Model

Once data augmentation is complete, we need to train the BERT model. The following code demonstrates how to load the BERT model and begin training.

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import Trainer, TrainingArguments
import torch

# Load tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)

# Tokenize data
train_encodings = tokenizer(augmented_texts, truncation=True, padding=True)
train_labels = augmented_labels

# Define dataset
class AugmentedDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
        
    def __getitem__(self, idx):
        item = {k: torch.tensor(v[idx]) for k, v in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item
    
    def __len__(self):
        return len(self.labels)

train_dataset = AugmentedDataset(train_encodings, train_labels)

# Set training parameters
training_args = TrainingArguments(
    per_device_train_batch_size=4,
    num_train_epochs=3,
    logging_dir='./logs',
    logging_steps=10,
)

# Initialize Trainer and start training
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

trainer.train()
    

11. Ensemble Learning

Now we will train the BERT model with various hyperparameters and apply ensemble learning.

def create_and_train_model(learning_rate, epochs):
    model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
    training_args = TrainingArguments(
        per_device_train_batch_size=4,
        num_train_epochs=epochs,
        learning_rate=learning_rate,
        logging_dir='./logs',
        logging_steps=10,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
    )
    
    trainer.train()
    return model

models = []
for lr in [5e-5, 2e-5]:
    for epoch in [3, 4]:
        models.append(create_and_train_model(lr, epoch))
    

12. Ensemble Prediction

The final predictions of the ensemble model are typically generated by averaging the predictions of several models. The following code can be used to perform ensemble predictions.

def ensemble_predict(models, texts):
    predictions = []
    
    for model in models:
        model_predictions = trainer.predict(texts)
        predictions.append(model_predictions.predictions)
    
    predictions = sum(predictions) / len(predictions)
    return predictions

ensemble_results = ensemble_predict(models, test_data)  # test_data is a separate test dataset
    

13. Conclusion

In this course, we explored how to apply ensemble learning to the BERT model using the Hugging Face Transformers library and how to implement data augmentation techniques. BERT provides powerful performance; however, its performance may degrade when data is insufficient or biased. Data augmentation and ensemble techniques are useful methods to address these issues.

14. References

• Hugging Face Transformers Documentation: https://transformers.huggingface.co/
• Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Natural Language Processing with Transformers (Book)