| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - text-classification |
| - tensorflow |
| - bert |
| library_name: tensorflow |
| --- |
| |
|
|
| # 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. |
|
|
