| task_categories: | |
| - text-classification | |
| # AutoTrain Dataset for project: tax_issues | |
| ## Dataset Description | |
| This dataset has been automatically processed by AutoTrain for project tax_issues. | |
| ### Languages | |
| The BCP-47 code for the dataset's language is unk. | |
| ## Dataset Structure | |
| ### Data Instances | |
| A sample from this dataset looks as follows: | |
| ```json | |
| [ | |
| { | |
| "text": "How is Inheritance Tax calculated?", | |
| "target": 10, | |
| "feat_Unnamed: 2": null, | |
| "feat_Unnamed: 3": null, | |
| "feat_Unnamed: 4": null | |
| }, | |
| { | |
| "text": "What happens if I work part-time or have multiple jobs as an international student in the UK?", | |
| "target": 13, | |
| "feat_Unnamed: 2": null, | |
| "feat_Unnamed: 3": null, | |
| "feat_Unnamed: 4": null | |
| } | |
| ] | |
| ``` | |
| ### Dataset Fields | |
| The dataset has the following fields (also called "features"): | |
| ```json | |
| { | |
| "text": "Value(dtype='string', id=None)", | |
| "target": "ClassLabel(names=['Question1', 'Question10', 'Question11', 'Question12', 'Question13', 'Question14', 'Question15', 'Question16', 'Question17', 'Question18', 'Question19', 'Question2', 'Question20', 'Question21', 'Question22', 'Question23', 'Question24', 'Question25', 'Question26', 'Question27', 'Question28', 'Question29', 'Question3', 'Question30', 'Question31', 'Question32', 'Question33', 'Question34', 'Question35', 'Question36', 'Question37', 'Question38', 'Question39', 'Question4', 'Question40', 'Question41', 'Question42', 'Question43', 'Question44', 'Question45', 'Question46', 'Question47', 'Question49', 'Question5', 'Question50', 'Question6', 'Question7', 'Question8', 'Question9', 'question48'], id=None)", | |
| "feat_Unnamed: 2": "Value(dtype='float64', id=None)", | |
| "feat_Unnamed: 3": "Value(dtype='float64', id=None)", | |
| "feat_Unnamed: 4": "Value(dtype='float64', id=None)" | |
| } | |
| ``` | |
| ### Dataset Splits | |
| This dataset is split into a train and validation split. The split sizes are as follow: | |
| | Split name | Num samples | | |
| | ------------ | ------------------- | | |
| | train | 2377 | | |
| | valid | 622 | | |