Text_Classification_With_Tenserflow

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.

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File Structure

• LICENSE: Project license.
• README.md: Project documentation.
• Text_Classification_with_Tensorflow.ipynb: Jupyter Notebook containing the full implementation of the text classification model.

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Features

• 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.

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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.

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Installation

Dependencies

• TensorFlow
• TensorFlow Hub
• TensorFlow Datasets

Install the required packages:

pip install tensorflow tensorflow-hub tensorflow-datasets