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