phucdev/noisyner
NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models. It is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique. Some highlights and interesting aspects of the data are: - Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances - Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of gold-standard data can be leveraged - Skewed label distribution (typical for Named En
| Author / organization | phucdev |
|---|---|
| License (as declared) | cc-by-nc-4.0 |
| Downloads | 526 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 5 December 2022 |
| Last updated at source | 5 January 2023 |
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