
This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt).
See the paper: https://arxiv.org/abs/2306.15895 for details.

- `label.txt`: the label name for each class
- `train.jsonl`: The original training set.
- `valid.jsonl`: The original validation set.
- `test.jsonl`: The original test set.
- `simprompt.jsonl`: The training data generated by the simple prompt.
- `attrprompt.jsonl`: The training data generated by the attributed prompt.

**Note**: Different than the other datasets, the `labels` for training/validation/test data are all a *list* instead of an integer as it is a multi-label classification dataset.   
task_categories:text-classification size_categories:10K<n<100K language:en license:apache-2.0 multilabel_classification arxiv scientific_papers arxiv:2306.15895 region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["multilabel_classification", "arxiv", "scientific_papers"], "version": ["V1"]}
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #multilabel_classification #arxiv #scientific_papers #arxiv-2306.15895 #region-us 


This is the data used in the paper Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias.
See the paper: URL for details.

- 'URL': the label name for each class
- 'URL': The original training set.
- 'URL': The original validation set.
- 'URL': The original test set.
- 'URL': The training data generated by the simple prompt.
- 'URL': The training data generated by the attributed prompt.

Note: Different than the other datasets, the 'labels' for training/validation/test data are all a *list* instead of an integer as it is a multi-label classification dataset.   
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #multilabel_classification #arxiv #scientific_papers #arxiv-2306.15895 #region-us 
