alinlab/MASKER

MASKER: Masked Keyword Regularization for Reliable Text Classification (AAAI 2021)

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This tool helps data scientists and machine learning engineers build more reliable text classification models. It takes your labeled text datasets (like movie reviews categorized as positive or negative) and produces a text classification model that is less prone to making decisions based on irrelevant or biased keywords. The goal is a model that accurately classifies text, even when facing new, slightly different data.

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Use this if you need to train text classification models that are robust and make reliable predictions, avoiding biases from specific keywords in your training data.

Not ideal if you are looking for a simple, out-of-the-box text classification solution without needing to dive into advanced model training techniques.

text-classification natural-language-processing bias-reduction model-robustness machine-learning-engineering
No License Stale 6m No Package No Dependents
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Maturity 8 / 25
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Language

Python

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Last pushed

Oct 04, 2023

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