| --- |
| title: Sentiment classifier |
| emoji: 🎭 |
| colorFrom: blue |
| colorTo: red |
| sdk: streamlit |
| sdk_version: 1.25.0 |
| pinned: false |
| app_file: sentiment_analysis.py |
| --- |
| |
| # bert-sentiment-analysis |
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| Prototype that classifies text into positive or negative sentiments using a fine tuned bert model |
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| ## Installation of dependencies |
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| `pip install -r requirements.txt` |
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| ## Usage |
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| 1. Download the [trained model](https://huggingface.co/rootstrap-org/bert-sentiment-classifier/blob/main/sentiments_bert_model.h5) and move it to the *models* directory |
| 2. Create a `.env` file and set a MODEL_NAME property with the name of the trained model file and optionally a MODEL_REPOSITORY_NAME property with the name of the huggingface repository of the model. |
| 3. Use the tool: |
| * To use it as a **streamlit web app** run: |
| |
| `streamlit run sentiment_analysis.py` |
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| It will open a web app on `http://localhost:8501` |
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| * To use it from **command line** run |
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| `python sentiment_classificator.py <TEXT_TO_CLASSIFY>` |
|
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| ## Training |
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| 1. Download the [all_sentiment_dataset.csv](https://drive.google.com/file/d/175Ccd3B6kLWMBvr1WAUzQJT4TwgzXF6N/view?usp=sharing) |
| 2. Execute the *classify_sentiment_with_bert* notebook which is in the *notebooks* directory |
| 3. The model should be saved under *models* directory as **sentiments_bert_model.h5** |
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|
