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:
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.
import ktrain
predictor = ktrain.load_predictor('./model')
Making Predictions
You can make predictions with the model as follows:
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.