参考：
adversarialQA
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:adversarial_qa/adversarialQA')
- 说明：
AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (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 human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.
- 许可证：无已知许可证
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
3000 |
'train' |
30000 |
'validation' |
3000 |
- 特征：
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}
dbidaf
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:adversarial_qa/dbidaf')
- 说明：
AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (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 human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.
- 许可证：无已知许可证
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
1000 |
'train' |
10000 |
'validation' |
1000 |
- 特征：
{
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}
}
dbert
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:adversarial_qa/dbert')
- 说明：
AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (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 human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.
- 许可证：无已知许可证
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
1000 |
'train' |
10000 |
'validation' |
1000 |
- 特征：
{
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"_type": "Value"
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"model_in_the_loop": {
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}
}
droberta
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:adversarial_qa/droberta')
- 说明：
AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (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 human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging.
- 许可证：无已知许可证
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
1000 |
'train' |
10000 |
'validation' |
1000 |
- 特征：
{
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},
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}