UCLNLP/adversarial_qa
Dataset Card for adversarialQA Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial… See the full description on the dataset page: https://huggingface.co/datasets/UCLNLP/adversarial_qa.
| Author / organization | UCLNLP |
|---|---|
| License (as declared) | cc-by-sa-4.0 |
| Downloads | 9.2K (as reported by Hugging Face) |
| Likes | 44 |
| Created | 2 March 2022 |
| Last updated at source | 21 December 2023 |
Recent changes
- 16 Oct 2026 Description: Dataset Card for adversarialQA Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2 → Dataset Card for adversarialQA Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2
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