---
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.