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DQC toolkit

DQC Toolkit is a Python library and framework designed with the goal to facilitate improvement of Machine Learning models by identifying and mitigating label errors in training dataset. Currently, DQC toolkit offers CrossValCurate for curation of text classification datasets (binary / multi-class) using cross validation based label error detection / correction.

Installation

Installation of DQC-toolkit can be done as shown below 

pip install dqc-toolkit

Quick Start

Assuming your text classification data is stored as a pandas dataframe data, with each sample represented by the text column and its corresponding noisy label represented by the label column, here is how you use CrossValCurate - 

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from dqc import CrossValCurate

cvc = CrossValCurate()
data_curated = cvc.fit_transform(data[['text', 'label']])
The result stored in data_curated is a pandas dataframe similar to data with the following columns - 
>>> data_curated.columns
['text', 'label', 'label_correctness_score', 'is_label_correct', 'predicted_label', 'prediction_probability']

  • 'label_correctness_score' represents a normalized score quantifying the correctness of 'label'.
  • 'is_label_correct' is a boolean flag indicating whether the given 'label' is correct (True) or incorrect (False).
  • 'predicted_label' and 'prediction_probability' represent the curation model's prediction and the corresponding probability score.

For more details regarding different hyperparameters available in CrossValCurate, please refer to the API documentation.