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| Main Author: | |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2302.04391 |
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| _version_ | 1866917960204943360 |
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| author | Guo, Tong |
| author_facet | Guo, Tong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_04391 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Re-Label Method For Data-Centric Machine Learning Guo, Tong Machine Learning Computation and Language 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. |
| title | The Re-Label Method For Data-Centric Machine Learning |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2302.04391 |