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title: Bertimbau Finetuned Glassdoor Reviews | |
emoji: 🏢 | |
colorFrom: gray | |
colorTo: green | |
sdk: streamlit | |
sdk_version: 1.41.1 | |
app_file: app.py | |
pinned: false | |
license: mit | |
short_description: Sentiment Analysis of Glassdoor reviews using BERTimbau | |
# 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](https://huggingface.co/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](https://github.com/stevillis/glassdoor-reviews-analysis-nlp). | |
## Installation | |
To run this project locally, follow these steps: | |
1. Download the [pytorch_model.bin](https://huggingface.co/stevillis/bertimbau-finetuned-glassdoor-reviews/blob/main/pytorch_model.bin) from `stevillis/bertimbau-finetuned-glassdoor-reviews`. | |
2. Clone the repository: | |
```sh | |
git clone https://github.com/your-username/bertimbau-finetuned-glassdoor-reviews.git | |
cd bertimbau-finetuned-glassdoor-reviews | |
``` | |
3. Create a virtual environment and activate it: | |
```sh | |
python -m venv venv | |
source venv/bin/activate # On Windows, use `venv\Scripts\activate` | |
``` | |
4. Install the required dependencies: | |
```sh | |
pip install -r requirements.txt | |
``` | |
5. Move the **pytorch_model.bin** to `bertimbau-finetuned-glassdoor-reviews` directory. | |
6. Run the Streamlit application: | |
```sh | |
streamlit run app.py | |
``` | |
## Usage | |
1. Open your web browser and go to `http://localhost:8501`. | |
2. Enter a Glassdoor review text in the input box. | |
3. The application will display the predicted sentiment and its corresponding score. | |