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5 months ago

The Re-Label Method For Data-Centric Machine Learning

Tong Guo

The Re-Label Method For Data-Centric Machine Learning

Abstract

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The dev dataset evaluation results and human evaluation results verify our idea.

Code Repositories

guotong1988/Automatic-Label-Error-Correction
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
label-error-detection-on-trec-6github.com/guotong1988/Automatic-Label-Error-Correction
Accuracy: 99.0
text-classification-on-trec-6Automatic Label Error Correction
Error: 0.40

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