wiki_table_questions
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The dataset contains pairs table-question, and the respective answer. The
questions require multi-step reasoning and various data operations such as
comparison, aggregation, and arithmetic computation. The tables were randomly
selected among Wikipedia tables with at least 8 rows and 5 columns.
(As per the documentation usage notes)
Split |
Examples |
'split-1-dev' |
2,810 |
'split-1-train' |
11,321 |
'split-2-dev' |
2,838 |
'split-2-train' |
11,312 |
'split-3-dev' |
2,838 |
'split-3-train' |
11,311 |
'test' |
4,344 |
'train' |
14,149 |
FeaturesDict({
'input_text': FeaturesDict({
'context': string,
'table': Sequence({
'column_header': string,
'content': string,
'row_number': int16,
}),
}),
'target_text': string,
})
Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
input_text |
FeaturesDict |
|
|
|
input_text/context |
Tensor |
|
string |
|
input_text/table |
Sequence |
|
|
|
input_text/table/column_header |
Tensor |
|
string |
|
input_text/table/content |
Tensor |
|
string |
|
input_text/table/row_number |
Tensor |
|
int16 |
|
target_text |
Tensor |
|
string |
|
@inproceedings{pasupat-liang-2015-compositional,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and
Liang, Percy",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = jul,
year = "2015",
address = "Beijing, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P15-1142",
doi = "10.3115/v1/P15-1142",
pages = "1470--1480",
}
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Last updated 2022-12-06 UTC.
[null,null,["Last updated 2022-12-06 UTC."],[],[],null,["# wiki_table_questions\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThe dataset contains pairs table-question, and the respective answer. The\nquestions require multi-step reasoning and various data operations such as\ncomparison, aggregation, and arithmetic computation. The tables were randomly\nselected among Wikipedia tables with at least 8 rows and 5 columns.\n\n(As per the documentation usage notes)\n\n- Dev: Mean accuracy over three (not five) splits of the training data. In\n other words, train on 'split-{1,2,3}-train' and test on 'split-{1,2,3}-dev',\n respectively, then average the accuracy.\n\n- Test: Train on 'train' and test on 'test'.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/wikitablequestions)\n\n- **Homepage** :\n \u003chttps://ppasupat.github.io/WikiTableQuestions/#usage-notes\u003e\n\n- **Source code** :\n [`tfds.structured.wiki_table_questions.WikiTableQuestions`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/structured/wiki_table_questions/wiki_table_questions.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): Initial release.\n- **Download size** : `65.36 MiB`\n\n- **Dataset size** : `237.24 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|-------------------|----------|\n| `'split-1-dev'` | 2,810 |\n| `'split-1-train'` | 11,321 |\n| `'split-2-dev'` | 2,838 |\n| `'split-2-train'` | 11,312 |\n| `'split-3-dev'` | 2,838 |\n| `'split-3-train'` | 11,311 |\n| `'test'` | 4,344 |\n| `'train'` | 14,149 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'input_text': FeaturesDict({\n 'context': string,\n 'table': Sequence({\n 'column_header': string,\n 'content': string,\n 'row_number': int16,\n }),\n }),\n 'target_text': string,\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|--------------------------------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| input_text | FeaturesDict | | | |\n| input_text/context | Tensor | | string | |\n| input_text/table | Sequence | | | |\n| input_text/table/column_header | Tensor | | string | |\n| input_text/table/content | Tensor | | string | |\n| input_text/table/row_number | Tensor | | int16 | |\n| target_text | Tensor | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('input_text', 'target_text')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @inproceedings{pasupat-liang-2015-compositional,\n title = \"Compositional Semantic Parsing on Semi-Structured Tables\",\n author = \"Pasupat, Panupong and\n Liang, Percy\",\n booktitle = \"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\",\n month = jul,\n year = \"2015\",\n address = \"Beijing, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/P15-1142\",\n doi = \"10.3115/v1/P15-1142\",\n pages = \"1470--1480\",\n }"]]