https://github.com/riishabhraj/sentiment-analysis-using-bert

Sentiment Analysis using BERT
https://github.com/riishabhraj/sentiment-analysis-using-bert

bert-model imdb python pytorch transformers

Last synced: 15 days ago
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Sentiment Analysis using BERT

• Host: GitHub
• URL: https://github.com/riishabhraj/sentiment-analysis-using-bert
• Owner: riishabhraj
• Created: 2024-12-13T10:01:57.000Z (23 days ago)
• Default Branch: main
• Last Pushed: 2024-12-13T10:08:22.000Z (23 days ago)
• Last Synced: 2024-12-21T18:14:51.922Z (15 days ago)
• Topics: bert-model, imdb, python, pytorch, transformers
• Language: Jupyter Notebook
• Homepage:
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Sentiment Analysis with BERT

This repository contains a sentiment analysis application that utilizes 

BERT (Bidirectional Encoder Representations from Transformers) to classify 

movie reviews from the IMDb dataset as positive or negative. 

The project demonstrates how to preprocess text data, train a BERT model, 

evaluate its performance using various metrics.

## Installation

To set up the environment and install the required packages, run the following commands:

``` 

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

pip install transformers datasets scikit-learn

```

## Usage

Clone the repository:

```

git clone https://github.com/riishabhraj/sentiment-analysis-using-BERT.git

cd sentiment-analysis-using-BERT

```

## Run the Jupyter Notebook:

Open the sentiment_analysis_using_BERT.ipynb file in Jupyter Notebook or Google Colab.

## Predict Sentiment:

Use the provided functions to predict sentiment for new text inputs.

JSON Configuration Example

Here is an example of how you can structure your JSON configuration for input data:

``` 

{

  "text": "The movie was absolutely fantastic!",

  "expected_output": {

    "label": "positive",

    "confidence": 0.95

  }

}

```