Getting Started with Google BERT/

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Sentiment Analysis

Sentiment Analysis

Learn how to fine-tune the pre-trained BERT model for sentiment analysis tasks.

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

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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.

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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 ...