| # BERT Sentiment Classifier | |
| This model is a fine-tuned version of BERT (Bidirectional Encoder Representations from Transformers) designed to classify text sentiment into positive or negative. It's trained on a large corpus of movie reviews and can be adapted for similar natural language processing tasks. | |
| ## Requirements | |
| To use this model, you need the following packages: | |
| - TensorFlow 2.x | |
| - ktrain | |
| ## Installation | |
| First, ensure you have Python 3.6 or newer installed. Then, install the required packages using pip: | |
| ```bash | |
| pip install tensorflow ktrain | |
| ``` | |
| ## Loading the Predictor | |
| To load the predictor, use the following code snippet. Ensure the model directory ('./model') is correctly specified to the location where you've downloaded the model files. | |
| ```python | |
| import ktrain | |
| predictor = ktrain.load_predictor('./model') | |
| ``` | |
| ## Making Predictions | |
| You can make predictions with the model as follows: | |
| ```python | |
| text = "I absolutely loved this movie! The acting was great and the story was compelling." | |
| prediction = predictor.predict(text) | |
| print("Sentiment:", "Positive" if prediction[0] == 1 else "Negative") | |
| ``` | |
| ## Model Files | |
| This model repository includes the following files: | |
| - `tf_model.h5`: The model weights. | |
| - `tf_model.preproc`: The preprocessing data for the model inputs, ensuring input data is in the correct format for prediction. | |
| ## Additional Notes | |
| This model is intended for educational and research purposes. It may require further tuning for optimal performance on specific tasks. | |
| For any questions or issues, please open an issue in the repository or contact the model maintainers. | |