Papers › The Re-Label Method For Data-Centric Machine Learning
The Re-Label Method For Data-Centric Machine Learning
Tong Guo
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
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Label Error Detection | TREC-6 | github.com/guotong1988/Automatic-Label-Error-Correction | Accuracy | 99.0 | #1 of 1 | Archive leaderboard | report |
| Text Classification | TREC-6 | Automatic Label Error Correction | Error | 0.40 | #1 of 19 | Archive leaderboard | report |
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