Loading the IMDB Dataset and Pre-Trained BERT Model

This document outlines the steps to download, load, and prepare the IMDB dataset, along with loading a pre-trained BERT model and its corresponding tokenizer, for subsequent fine-tuning.

1. Download and Load the IMDB Dataset

Begin by downloading the IMDB dataset and then loading it using the Hugging Face nlp library.

First, download the dataset file:

!gdown https://drive.google.com/uc?id=11_M4ootuT7I1G0RlihcC0cA3Elqotlc-

Next, load the dataset from the downloaded CSV file:

from datasets import load_dataset

dataset = load_dataset('csv', data_files='./imdbs.csv', split='train')

You can verify the type of the loaded dataset:

type(dataset)

Expected Output:

nlp.arrow_dataset.Dataset

2. Split the Dataset into Training and Testing Sets

To prepare the dataset for model training and evaluation, split it into training and testing sets. A common practice is to use a 70-30 ratio for training and testing, respectively.

dataset = dataset.train_test_split(test_size=0.3)
print(dataset)

Output Overview:

The train_test_split method returns a dictionary containing the split datasets. The output will look similar to this, indicating the number of rows in each split:

{
  'test': Dataset(features={'text': Value(dtype='string'), 'label': Value(dtype='int64')}, num_rows=30),
  'train': Dataset(features={'text': Value(dtype='string'), 'label': Value(dtype='int64')}, num_rows=70)
}

Create individual variables for the training and testing datasets for easier access:

train_set = dataset['train']
test_set = dataset['test']

3. Load the Pre-Trained BERT Model and Tokenizer

For sequence classification tasks like sentiment analysis, the BertForSequenceClassification model is suitable. We will load the pre-trained bert-base-uncased model.

from transformers import BertForSequenceClassification

model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

It's recommended to use BertTokenizerFast for its improved performance and additional features compared to the standard BertTokenizer. Load the tokenizer corresponding to the chosen BERT model:

from transformers import BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')

This completes the initial setup, preparing the IMDB dataset and a pre-trained BERT model for the subsequent steps of data preprocessing and model fine-tuning.

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Interview Questions

• How do you load a custom CSV dataset for NLP tasks using Hugging Face?
• What is the advantage of using BertTokenizerFast over the standard tokenizer?
• Why is bert-base-uncased commonly used for text classification?
• How do you split a dataset into training and testing using the Hugging Face Datasets library?
• What is the structure of a Hugging Face Dataset object?
• How do you define a classification head in BERT?
• What are the key steps before fine-tuning a BERT model?
• Why is a 70-30 split commonly used in machine learning tasks?
• How does the train_test_split() function in Hugging Face work?
• What are the roles of text and label columns in sentiment classification tasks?