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stevillis/bertimbau-finetuned-glassdoor-reviews

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App README

Bertimbau Finetuned Glassdoor Reviews

This project provides a Streamlit web application for classifying Glassdoor reviews into sentiment categories using a fine-tuned BERT model. The model is based on the pre-trained BERT model from neuralmind/bert-base-portuguese-cased and fine-tuned on Glassdoor review data.

Model

The model architecture and training process can be found at glassdoor-reviews-analysis-nlp.

Installation

To run this project locally, follow these steps:

  1. 1.Download the pytorch_model.bin from stevillis/bertimbau-finetuned-glassdoor-reviews.
  1. 1.Clone the repository:
sh
    git clone https://github.com/your-username/bertimbau-finetuned-glassdoor-reviews.git
    cd bertimbau-finetuned-glassdoor-reviews
  1. 1.Create a virtual environment and activate it:
sh
    python -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
  1. 1.Install the required dependencies:
sh
    pip install -r requirements.txt
  1. 1.Move the pytorch_model.bin to bertimbau-finetuned-glassdoor-reviews directory.
  1. 1.Run the Streamlit application:
sh
    streamlit run app.py

Usage

  1. 1.Open your web browser and go to http://localhost:8501.
  2. 2.Enter a Glassdoor review text in the input box.
  3. 3.The application will display the predicted sentiment and its corresponding score.