Course on Utilizing Hugging Face Transformers, Mobile BERT vs BERT Tokenizer

Introduction

One of the most notable technologies in the field of deep learning and natural language processing (NLP) in recent years is BERT (Bidirectional Encoder Representations from Transformers). BERT demonstrates exceptional performance in understanding context and is used for various NLP tasks. However, due to its large size and high computational cost, it is difficult to use in mobile environments. To solve these issues, Mobile BERT has emerged. In this course, we will compare the characteristics of BERT and Mobile BERT using Hugging Face’s Transformers library, and we will experiment with the Tokenizer of both models.

1. Introduction to BERT Model

BERT is a language representation model announced by Google in 2018, which learns pre-trained language representations to assist with various NLP tasks. BERT is based on the Transformer’s encoder structure and can understand context in a bidirectional manner. Common NLP tasks include sentiment analysis, question-answering systems, and sentence similarity calculation.

1.1 Features of BERT

  • Bidirectional Attention: Understands context in both directions.
  • Masked Language Modeling: Learns by masking certain words in the input sentence and predicting them.
  • Next Sentence Prediction: Predicts whether two sentences are in a consecutive relationship.

2. Introduction to Mobile BERT Model

Mobile BERT is a lightweight version of BERT designed for efficient use on mobile devices. Mobile BERT greatly reduces the number of parameters compared to BERT while maintaining performance. This allows for smooth execution of natural language processing tasks even on mobile devices.

2.1 Features of Mobile BERT

  • Small Model Size: Mobile BERT is a significantly smaller model compared to BERT.
  • High Processing Speed: Thanks to its lightweight structure, it operates quickly even in mobile environments.
  • Efficient Memory Usage: Optimized to achieve high performance with fewer resources.

3. Introduction to Hugging Face Transformers Library

Hugging Face (Transformers) is a Python library that facilitates easy access to various pre-trained NLP models. This library offers a range of models, including BERT, Mobile BERT, and GPT-2. Additionally, it provides Tokenizers for the models to assist with easy text preprocessing.

3.1 Installation Method

pip install transformers torch

4. Mobile BERT vs BERT Tokenizer Usage Example

Now let’s look into the usage of the Tokenizer for BERT and Mobile BERT. The code below installs the Tokenizer for both models and provides an example of tokenizing an input text.

from transformers import BertTokenizer, MobileBertTokenizer

# Initialize BERT Tokenizer
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Initialize Mobile BERT Tokenizer
mobile_bert_tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased")

# Input text
text = "Deep learning is a very interesting field."

# BERT Tokenization
bert_tokens = bert_tokenizer.tokenize(text)
print("BERT Tokens:", bert_tokens)

# Mobile BERT Tokenization
mobile_bert_tokens = mobile_bert_tokenizer.tokenize(text)
print("Mobile BERT Tokens:", mobile_bert_tokens)

4.1 Code Explanation

In the example above, we initialized two Tokenizers, BertTokenizer and MobileBertTokenizer, provided by the transformers library. We tokenized the input text using the tokenize method and printed the results. You can compare the tokenization results of BERT and Mobile BERT.

5. Comparative Analysis

Using the Tokenizers of BERT and Mobile BERT, we will compare the tokenization results of the two models and analyze the characteristics of each model. The input sentence used is “Deep learning is a very interesting field.”

# BERT Tokenization Results
BERT Tokens: ['deep', '##learning', 'is', 'a', 'very', 'interesting', 'field', '.']

# Mobile BERT Tokenization Results
Mobile BERT Tokens: ['Deep', 'learning', 'is', 'a', 'very', 'interesting', 'field', '.']

5.1 Analysis

The BERT Tokenizer splits a single word into multiple subwords, while the Mobile BERT Tokenizer keeps the input sentence intact without breaking it into smaller word units. This is because Mobile BERT is optimized to function more efficiently in mobile environments.

6. Advanced Applications

Beyond tokenization and model loading, various advanced tasks utilizing BERT and Mobile BERT models can be performed through the Hugging Face library. For example, you can build sentiment analysis models or perform fine-tuning for specific tasks.

6.1 Model Fine-tuning

Model fine-tuning is the process of retraining a pre-trained model on a specific dataset. The code below shows a basic method for fine-tuning the BERT model.

from transformers import BertForSequenceClassification, Trainer, TrainingArguments
import torch
from torch.utils.data import Dataset, DataLoader

# Example dataset class
class CustomDataset(Dataset):
    def __init__(self, texts, labels, tokenizer):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer

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

    def __getitem__(self, idx):
        input_encoding = self.tokenizer(self.texts[idx], truncation=True, padding='max_length', max_length=512, return_tensors='pt')
        item = {key: val[0] for key, val in input_encoding.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

# Initialize model
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)

# Create dataset and DataLoader
train_texts = ["This movie was great.", "This movie was not good."]
train_labels = [1, 0]  # Sentiment labels 1: positive, 0: negative
train_dataset = CustomDataset(train_texts, train_labels, bert_tokenizer)
train_loader = DataLoader(train_dataset, batch_size=2)

# Set TrainingArguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=2,
    logging_dir='./logs',
)

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

# Train the model
trainer.train()

6.2 Code Explanation

The code defines the CustomDataset class to handle input data, loads the BERT model, and then begins training through the Trainer object. This method allows the BERT model to be tailored to specific tasks.

7. Conclusion

In this course, we compared the Tokenizers of BERT and Mobile BERT using Hugging Face’s Transformers library and explored the basic process of model training based on this. While BERT delivers outstanding performance, it demands high-end hardware, whereas Mobile BERT, as a lightweight model, enables natural language processing in mobile environments. We look forward to achieving results in the fields of deep learning and natural language processing through further practice and research.

References

  • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
  • Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). MobileBERT: Transformer-based Model for Resource-bound Devices.