## Timeseries classification with a Transformer model on the 🤗Hub! 
Full credits go to [Theodoros Ntakouris](https://github.com/ntakouris).

This repository contains the model from [this notebook on time-series classification using the attention mechanism](https://keras.io/examples/timeseries/timeseries_classification_transformer/). 

The dataset we are using here is called [FordA](http://www.j-wichard.de/publications/FordPaper.pdf). The data comes from the UCR archive. The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. For this task, the goal is to automatically detect the presence of a specific issue with the engine. The problem is a balanced binary classification task.
{"license": "cc0-1.0", "library_name": "keras", "tags": ["time-series"], "dataset": ["FordA"]}
keras time-series license:cc0-1.0 has_space region:us
TAGS
#keras #time-series #license-cc0-1.0 #has_space #region-us 

## Timeseries classification with a Transformer model on the Hub! 
Full credits go to Theodoros Ntakouris.

This repository contains the model from this notebook on time-series classification using the attention mechanism. 

The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. For this task, the goal is to automatically detect the presence of a specific issue with the engine. The problem is a balanced binary classification task.
## Timeseries classification with a Transformer model on the Hub! 
Full credits go to Theodoros Ntakouris.

This repository contains the model from this notebook on time-series classification using the attention mechanism. 

The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. For this task, the goal is to automatically detect the presence of a specific issue with the engine. The problem is a balanced binary classification task.
TAGS
#keras #time-series #license-cc0-1.0 #has_space #region-us 
 ## Timeseries classification with a Transformer model on the Hub! 
Full credits go to Theodoros Ntakouris.

This repository contains the model from this notebook on time-series classification using the attention mechanism. 

The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. For this task, the goal is to automatically detect the presence of a specific issue with the engine. The problem is a balanced binary classification task.
passage: TAGS
#keras #time-series #license-cc0-1.0 #has_space #region-us 
## Timeseries classification with a Transformer model on the Hub! 
Full credits go to Theodoros Ntakouris.

This repository contains the model from this notebook on time-series classification using the attention mechanism. 

The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset contains 3601 training instances and another 1320 testing instances. Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. For this task, the goal is to automatically detect the presence of a specific issue with the engine. The problem is a balanced binary classification task.