| title: Sentiment classificator | |
| emoji: 🎭 | |
| colorFrom: blue | |
| colorTo: red | |
| sdk: streamlit | |
| sdk_version: 1.25.0 | |
| pinned: false | |
| app_file: sentiment_analysis.py | |
| # bert-sentiment-analysis | |
| Prototype that classifies text into positive or negative sentiments using a fine tuned bert model | |
| ## Installation of dependencies | |
| `pip install -r requirements.txt` | |
| ## Usage | |
| 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. Use the tool: | |
| * To use it as a **streamlit web app** run: | |
| `streamlit run sentiment_analysis.py` | |
| It will open a web app on `http://localhost:8501` | |
| * To use it from **command line** run | |
| `python sentiment_classificator.py <TEXT_TO_CLASSIFY>` | |
| ## Training | |
| 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** | |