timeseries_transformer_classification

Introduction

The repository provides a model for time-series classification using a Transformer model, as featured on the Hugging Face Hub. The model utilizes the attention mechanism for effective classification of time-series data. The dataset used is FordA, sourced from the UCR archive, which involves a balanced binary classification task to detect engine issues from motor sensor measurements.

Architecture

The model is built using TensorFlow Keras, leveraging the Transformer architecture. Transformers are known for their ability to capture long-range dependencies in data using self-attention mechanisms, making them suitable for time-series tasks where temporal relationships are crucial.

Training

The FordA dataset comprises 3601 training instances and 1320 testing instances. Each instance represents engine noise data captured by motor sensors. The task is to classify these instances into two categories, indicating the presence or absence of specific engine issues. The model training process involves optimizing the classification accuracy on this binary task.

Guide: Running Locally

1. Clone the Repository: Start by cloning the repository from Hugging Face.
2. Set Up Environment: Ensure you have TensorFlow Keras installed. You may use a virtual environment for better dependency management.
3. Download Dataset: Obtain the FordA dataset from the UCR archive or use the dataset link provided in the repository.
4. Run the Model: Execute the training script provided in the repository. This will require setting the library_name to tf-keras.
5. Evaluate: After training, test the model using the provided test data.

For more efficient training, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure, especially if dealing with large datasets or models.

License

The model and associated materials are licensed under CC0-1.0, which allows for use without restrictions.