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README.md

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+# 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.

tf_model.h5

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