Hugging Face Transformers Tutorial, Preparing Dataset for BigBird Inference

With the advancement of deep learning, noticeable changes are also occurring in the field of Natural Language Processing (NLP). In particular,
Hugging Face‘s Transformer library is one of the key tools that has led this change. In this course, we will take a closer look at how to prepare a dataset that can be inferred using one of the transformer models, BigBird.

1. What is BigBird?

BigBird is a transformer-based model developed by Google that is optimized for processing long texts.
Existing transformer models have limitations in processing long documents due to restrictions on the length of input sequences, but
BigBird was designed to overcome these limitations.
BigBird can process long texts through a more efficient attention mechanism.

1.1. Advantages of BigBird

• Long sequence processing: Effectively handles long documents, overcoming the limitations of existing transformers.
• Efficiency: Reduces computation costs by decreasing the complexity of attention.
• Applicable to various NLP tasks: Can be used in various fields such as text classification, summarization, and translation.

2. Preparing the Dataset

The process of preparing a dataset to use with the BigBird model is relatively simple.
We need to preprocess the given data into the format required by BigBird,
and we will take a look at important considerations in this process.

2.1. Required Data Format

The BigBird model requires text and labels (answers) as input.
Input text must not exceed the model’s maximum length, and
labels should be represented as integers for classification problems and as floats for regression problems.

2.2. Loading the Dataset

Let’s assume that the dataset is provided in CSV file format.
The example code below shows how to load data from a CSV file using pandas.

python
import pandas as pd

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

2.3. Data Preprocessing

This is the process of preprocessing the data to fit the BigBird model.
This process includes text cleaning, tokenization, padding, and more.
Below is an example of the data preprocessing process presented in code.

python
from transformers import BigBirdTokenizer

# Tokenization and padding
tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

max_length = 512  # Set maximum input length

def preprocess_data(text):
    inputs = tokenizer(
        text,
        max_length=max_length,
        padding='max_length',
        truncation=True,
        return_tensors='pt'
    )
    return inputs

# Perform preprocessing on the text column of the dataset
data['inputs'] = data['text'].apply(preprocess_data)
print(data['inputs'].head())

3. Preparing the Model and Performing Inference

Now we are ready to train the BigBird model and perform inference based on the prepared data.
Hugging Face’s transformer library provides an interface that makes loading and inferring models very simple.

3.1. Loading the BigBird Model

We use the transformers library to load the BigBird model.
The example below shows how to load the BigBird model.

python
from transformers import BigBirdForSequenceClassification

# Load model
model = BigBirdForSequenceClassification.from_pretrained('google/bigbird-roberta-base', num_labels=2)

3.2. Performing Inference

We perform inference on the prepared input data using the loaded model.
Below is the code showing how to perform inference with the model and check the results.

python
import torch

# Perform inference
def infer(inputs):
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1)
    return predictions

# Inference on the first input of the dataset
pred = infer(data['inputs'][0])
print(f'Predicted label: {pred.item()}')

4. Conclusion

In this course, we explored the process of preparing a dataset and performing inference using Hugging Face’s BigBird model.
Thanks to BigBird’s excellent performance, we can effectively handle long text data that was difficult for us to process before.
Please modify and utilize the preprocessing and inference code according to your dataset for application in real projects.

4.1. Reference Materials

• Hugging Face Transformers Documentation
• BigBird Paper

4.2. Questions and Feedback

If you have any questions or feedback, please leave a comment.
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