Deep Learning for Natural Language Processing: Solving KorNLI with KoBERT (Multi-Class Classification)

1. Introduction

Natural language processing is a technology that enables computers to understand and process human language, utilized in various fields such as machine translation, sentiment analysis, and question-answering systems. Particularly, recent advancements in deep learning technology have led to significant innovations in the field of natural language processing. In this article, we aim to explore the multi-class classification problem using the KoBERT model with the KorNLI dataset in depth.

2. Natural Language Processing and Deep Learning

The reasons deep learning technology is used for natural language processing are as follows. First, deep learning models can learn based on large amounts of data, making them suitable for learning complex patterns in language. Second, neural network architectures have the capability to integrate and process various types of data (text, images, etc.). Finally, recently, Transformer-based models have shown outstanding performance in the field of natural language processing.

3. Introduction to KoBERT

KoBERT is a BERT (Bidirectional Encoder Representations from Transformers) model specialized for the Korean language, pre-trained on Korean datasets. This model demonstrates high performance in Korean natural language processing tasks and can easily be applied to various sub-tasks (sentiment analysis, named entity recognition, etc.). The main features of KoBERT are as follows:

• Based on a bidirectional Transformer architecture for effective context understanding
• Uses a tokenizer optimized for the characteristics of the Korean language
• Excellent transfer learning performance for various natural language processing tasks

4. KorNLI Dataset

The KorNLI (Korean Natural Language Inference) dataset is a Korean natural language inference (NLI) dataset that performs the task of classifying relationships between sentence pairs into multiple classes (Entailment, Neutral, Contradiction). This dataset is suitable for evaluating the reasoning capabilities of natural language processing models. The characteristics of the KorNLI dataset are as follows:

• Consists of a total of 50,000 sentence pairs
• Includes a variety of topics, covering general natural language inference problems
• Labels are composed of Entailment, Neutral, and Contradiction

5. Building a KorNLI Model Using KoBERT

5.1. Library Installation

We will install the necessary libraries to build the model. Primarily, we will use PyTorch and Hugging Face’s Transformers library.

!pip install torch torchvision transformers

5.2. Data Preprocessing

This is the process of loading the KorNLI dataset and preprocessing it into the appropriate format. We will use sentence pairs as input and assign the corresponding labels as output.

import pandas as pd

# Load the dataset
data = pd.read_csv('kornli_dataset.csv')

# Check the data
print(data.head())
            

5.3. Defining the KoBERT Model

We will load the KoBERT model and add layers for the classification task. Below is the basic code for model definition.

from transformers import BertTokenizer, BertForSequenceClassification

# Initialize tokenizer and model
tokenizer = BertTokenizer.from_pretrained('monologg/kobert')
model = BertForSequenceClassification.from_pretrained('monologg/kobert', num_labels=3)
            

5.4. Model Training

We will use PyTorch’s DataLoader to load the data in batches for model training. The model will be trained for a number of epochs.

from torch.utils.data import DataLoader, Dataset

class KorNliDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_len):
        self.labels = labels
        self.texts = texts
        self.tokenizer = tokenizer
        self.max_len = max_len

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

    def __getitem__(self, idx):
        text = self.texts[idx]
        label = self.labels[idx]

        encoding = self.tokenizer.encode_plus(
            text,
            add_special_tokens=True,
            max_length=self.max_len,
            return_token_type_ids=False,
            padding='max_length',
            return_attention_mask=True,
            return_tensors='pt',
            truncation=True
        )

        return {
            'input_ids': encoding['input_ids'].flatten(),
            'attention_mask': encoding['attention_mask'].flatten(),
            'labels': torch.tensor(label, dtype=torch.long)
        }

# Define DataLoader
dataset = KorNliDataset(data['text'].values, data['label'].values, tokenizer, max_len=128)
dataloader = DataLoader(dataset, batch_size=16, shuffle=True)

# Define optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)

# Model training
model.train()
for epoch in range(epochs):
    for batch in dataloader:
        optimizer.zero_grad()
        outputs = model(
            input_ids=batch['input_ids'],
            attention_mask=batch['attention_mask'],
            labels=batch['labels']
        )
        loss = outputs.loss
        loss.backward()
        optimizer.step()
            

5.5. Model Evaluation

We will use validation data to evaluate the performance of the trained model. Performance metrics such as accuracy can be used.

model.eval()
predictions = []
true_labels = []

with torch.no_grad():
    for batch in validation_dataloader:
        outputs = model(
            input_ids=batch['input_ids'],
            attention_mask=batch['attention_mask']
        )

        preds = torch.argmax(outputs.logits, dim=1)
        predictions.extend(preds.numpy())
        true_labels.extend(batch['labels'].numpy())

# Calculate accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(true_labels, predictions)
print(f'Accuracy: {accuracy}')
            

6. Conclusion

By utilizing KoBERT to solve the multi-class classification problem with the KorNLI dataset, we explored the potential for advancements in Korean natural language processing and the usefulness of deep learning technology. Furthermore, the development of deep learning-based natural language processing technologies is expected to accelerate, with applications in various fields.