Using Hugging Face Transformers Course, BERT Ensemble Learning and Prediction Beyond Learning Datasets

In this article, we will discuss how to perform ensemble learning using the BERT model provided by the Hugging Face Transformers library, and how this can improve prediction performance. Ensemble learning is a technique that aims to achieve better performance by combining the prediction results of several models. This tutorial will detail the process of implementing an ensemble by combining various BERT models.

1. Basics of Ensemble Learning

Ensemble learning is a method that combines multiple models to create the final prediction result. This approach leverages the strengths of each model to enhance the overall model performance. Common ensemble methods include the following techniques:

  • Bagging: Independently trains multiple models and improves performance by averaging the final prediction results.
  • Boosting: Increases the weights of the data that previous models mispredicted to train the next model.
  • Stacking: Uses the predictions of various models as new features to train a meta model for the final prediction.

2. Introduction to BERT Model

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model based on Transformers that demonstrates excellent performance across various natural language processing (NLP) tasks. Two features of BERT are:

  • Bidirectionality: BERT learns context from both directions to understand word meanings more accurately.
  • Pre-training: It is pre-trained on vast amounts of data, making it capable of handling various tasks with just fine-tuning.

3. Preparing Data

The dataset prepared for ensemble learning should ideally address a simple natural language processing problem. For example, we will classify sentiment (positive/negative) using movie review data.

First, install the Hugging Face library and necessary packages:

!pip install transformers datasets torch scikit-learn

Loading the Dataset

Next, we will load the dataset using Hugging Face’s datasets library:

from datasets import load_dataset

dataset = load_dataset('imdb')
train_data = dataset['train']
test_data = dataset['test']

4. Model Setup

In this example, we will create two variant models based on the BERT model. This will allow us to achieve an ensemble effect. First, let’s write a function to load the BERT model:

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

# Load BERT model and tokenizer
def load_model_and_tokenizer(model_name='bert-base-uncased'):
    tokenizer = BertTokenizer.from_pretrained(model_name)
    model = BertForSequenceClassification.from_pretrained(model_name)
    return tokenizer, model

5. Data Preprocessing

Let’s explain the process of preprocessing the text data for use as model input:

def preprocess_data(dataset, tokenizer, max_len=128):
    inputs = tokenizer(dataset['text'], padding=True, truncation=True, max_length=max_len, return_tensors='pt')
    inputs['labels'] = torch.tensor(dataset['label'])
    return inputs

train_inputs = preprocess_data(train_data, tokenizer)
test_inputs = preprocess_data(test_data, tokenizer)

6. Model Training

It is now time to train the model. We will use the previously loaded model and preprocessed data for training:

def train_model(model, train_inputs):
    training_args = TrainingArguments(
        output_dir='./results',
        num_train_epochs=3,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=64,
        evaluation_strategy='epoch',
        logging_dir='./logs',
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_inputs,
    )
    trainer.train()

# Train the model
model1 = load_model_and_tokenizer()[1]
train_model(model1, train_inputs)

7. Training the Ensemble Model

Now we add a second model to perform ensemble learning. The BERT architecture remains the same, but we may use different initialization or hyperparameters:

model2 = load_model_and_tokenizer(model_name='bert-large-uncased')[1]
train_model(model2, train_inputs)

8. Ensemble Prediction

We ensemble the model prediction results to generate the final output. We average the predictions from the two models to obtain the final prediction:

import numpy as np

def ensemble_predict(models, inputs):
    preds = []
    for model in models:
        model.eval()
        with torch.no_grad():
            outputs = model(**inputs)
            preds.append(outputs.logits)
    
    ensemble_preds = np.mean(preds, axis=0)
    return ensemble_preds

models = [model1, model2]
predictions = ensemble_predict(models, test_inputs)

9. Performance Evaluation

Now we evaluate the performance of the ensemble model. Metrics such as accuracy or F1 score can be used:

from sklearn.metrics import accuracy_score, f1_score

# Retrieve the ground truth labels
labels = test_data['label']

# Calculate metrics based on ensemble predictions and labels
predicted_labels = np.argmax(predictions, axis=1)

accuracy = accuracy_score(labels, predicted_labels)
f1 = f1_score(labels, predicted_labels)

print(f'Accuracy: {accuracy:.4f}, F1 Score: {f1:.4f}')

10. Conclusion and Future Work

Through this tutorial, we learned about ensemble learning methods using the BERT model. We explored how combining the predictions of multiple models can improve performance. Future work may include:

  • Ensemble using more models
  • Improving preprocessing and data augmentation
  • Optimizing performance through hyperparameter tuning

Ensemble learning continues to be a promising method in the field of deep learning, achieving higher accuracy by mixing various models. As mentioned earlier, various experiments can be conducted to enhance performance using multiple BERT models.

References