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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-classification |
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- tensorflow |
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- bert |
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library_name: tensorflow |
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--- |
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# BERT Sentiment Classifier |
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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. |
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## Requirements |
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To use this model, you need the following packages: |
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- TensorFlow 2.x |
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- ktrain |
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## Installation |
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First, ensure you have Python 3.6 or newer installed. Then, install the required packages using pip: |
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```bash |
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pip install tensorflow ktrain |
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``` |
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## Loading the Predictor |
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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. |
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```python |
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import ktrain |
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predictor = ktrain.load_predictor('./model') |
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``` |
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## Making Predictions |
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You can make predictions with the model as follows: |
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```python |
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text = "I absolutely loved this movie! The acting was great and the story was compelling." |
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prediction = predictor.predict(text) |
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print("Sentiment:", "Positive" if prediction[0] == 1 else "Negative") |
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``` |
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## Model Files |
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This model repository includes the following files: |
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- `tf_model.h5`: The model weights. |
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- `tf_model.preproc`: The preprocessing data for the model inputs, ensuring input data is in the correct format for prediction. |
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## Additional Notes |
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This model is intended for educational and research purposes. It may require further tuning for optimal performance on specific tasks. |
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For any questions or issues, please open an issue in the repository or contact the model maintainers. |
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