Datasets:

tau
/

scrolls

@@ -81,8 +81,7 @@ Given a non-disclosure agreement (the premise), the task is to predict whether a
 The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.
 The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.
-### Data Fields
 All the datasets in the benchmark are in the same input-output format
@@ -91,7 +90,7 @@ All the datasets in the benchmark are in the same input-output format
 - `id`: a `string` feature. Unique per input.
 - `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).
-### Citation
 If you use the SCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://scrolls-tau.s3.us-east-2.amazonaws.com/scrolls_datasets.bib)]
 ```
 @misc{shaham2022scrolls,

 The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google.
 The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples.
+## Data Fields
 All the datasets in the benchmark are in the same input-output format
 - `id`: a `string` feature. Unique per input.
 - `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target).
+## Citation
 If you use the SCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://scrolls-tau.s3.us-east-2.amazonaws.com/scrolls_datasets.bib)]
 ```
 @misc{shaham2022scrolls,