This project demonstrates text classification using TensorFlow. The model is trained to classify sentiment in IMDB movie reviews using a pre-trained embedding layer and a simple neural network.
LICENSE: Project license.README.md: Project documentation.Text_Classification_with_Tensorflow.ipynb: Jupyter Notebook containing the full implementation of the text classification model.
- Pre-trained Embeddings: Leverages a pre-trained text embedding from TensorFlow Hub for efficient representation of textual data.
- Binary Sentiment Classification: Classifies IMDB movie reviews as positive or negative.
- Transfer Learning: Fine-tunes a pre-trained model for text embeddings.
- Metrics Visualization: Displays training accuracy and loss for analysis.
- High Accuracy: Achieves robust accuracy on the IMDB dataset.
The IMDB movie review dataset is loaded using tensorflow_datasets. It includes:
- Training Set: 25,000 examples (split into 15,000 training and 10,000 validation samples).
- Test Set: 25,000 examples.
- TensorFlow
- TensorFlow Hub
- TensorFlow Datasets
Install the required packages:
pip install tensorflow tensorflow-hub tensorflow-datasets