Welcome to the Sequence Data Project! In this project, we will explore the fascinating world of sequence data, focusing on text sentences as our primary example. Language is inherently composed of sequences, with characters forming words and words forming sentences. This project will introduce you to the concept of recurrent neural networks (RNNs) and their application in text prediction.
- Prepare sequence data for training a recurrent neural network (RNN).
- Build and train an RNN model for word prediction.
In this project, we will create a simple text predictor, akin to autocomplete or suggestion features commonly found in applications like search bars or text editors. While sophisticated text prediction models are often trained on extensive datasets and require substantial computational resources, this project will provide you with exposure to language processing, sequence data, and the fundamental architecture of recurrent neural networks (RNNs).
We'll start by reading and cleaning a dataset of headlines from the New York Times newspaper. These headlines will serve as our training data. We will process the text data, tokenize it, and create sequences of tokens that our model can learn from.
Tokenization involves converting words into numerical representations. We'll use the Keras Tokenizer class to accomplish this. Additionally, we'll remove unwanted data such as headlines labeled as "Unknown" and perform text cleaning, making all text lowercase and removing punctuation.
Our model will consist of an embedding layer, followed by an LSTM (Long Short-Term Memory) layer. The embedding layer learns a vector representation for each word, while the LSTM layer captures the sequential dependencies in the data. The output layer will predict the next word in the sequence.
We'll compile and train our model using categorical cross-entropy loss, suitable for predicting a single word from a vocabulary. The Adam optimizer will be used for efficient training.
With a trained model, we'll implement functions to generate text predictions. Starting with a seed text, the model will predict the next words, allowing us to generate new headlines or complete sentences.
Through this project, you'll gain practical experience in handling sequence data and building a basic text prediction model using recurrent neural networks. While this project provides a foundational understanding, keep in mind that modern natural language processing often leverages more advanced techniques like transformer-based models for improved performance.
Feel free to explore, experiment, and generate your own headlines using the trained model. Have fun, and enjoy your journey into the world of sequence data and text prediction!