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Getting Started with Google BERT

Sentiment Analysis

Explore how to fine-tune a pre-trained BERT model for sentiment analysis tasks. Understand the preprocessing steps such as tokenization, adding special tokens, attention masks, and segment IDs. Train and evaluate the model using the IMDB movie review dataset to classify sentiment effectively.

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The following figure shows how we fine-tune the pre-trained BERT model for a sentiment analysis task:

Fine-tuning the pre-trained BERT model for text classification
Fine-tuning the pre-trained BERT model for text classification

As we can observe from the preceding figure, we feed the tokens to the pre-trained BERT model and get the embeddings of all the tokens. We take the embedding of the [CLS] token and feed it to a feedforward network with a softmax function and perform classification.

Let's get a better understanding of how fine-tuning works by getting hands-on with fine-tuning the pre-trained BERT model for a sentiment analysis task.

Fine-tuning BERT for sentiment analysis

Let's explore how to fine-tune the pre-trained BERT model for a sentiment analysis task with the IMDBInternet Movie Database dataset. The IMDB dataset consists of movie reviews along with the respective sentiment of the review.

Importing the dependencies

Let's install the necessary libraries:

!pip install nlp==0.4.0
!pip install transformers==4.30.0

Import the necessary modules:

from transformers import BertForSequenceClassification, BertTokenizerFast,Trainer, TrainingArguments
from nlp import load_dataset
import torch
import numpy as np

Loading the dataset

Download and load the dataset using the nlp library:

!gdown https://drive.google.com/uc?id=11_M4ootuT7I1G0RlihcC0cA3Elqotlc-
dataset = load_dataset('csv', data_files='./imdbs.csv', split='train')

Let's check the datatype:

type(dataset)

Here is ...