mctaco
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MC-TACO is a dataset of 13k question-answer pairs that require temporal
commonsense comprehension. The dataset contains five temporal properties:
- duration (how long an event takes)
- temporal ordering (typical order of events)
- typical time (when an event occurs)
- frequency (how often an event occurs)
- stationarity (whether a state is maintained for a very long time or
indefinitely)
We hope that this dataset can promote the future exploration of this particular
class of reasoning problems.
Split |
Examples |
'test' |
9,442 |
'validation' |
3,783 |
FeaturesDict({
'answer': Text(shape=(), dtype=string),
'category': ClassLabel(shape=(), dtype=int64, num_classes=5),
'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
'question': Text(shape=(), dtype=string),
'sentence': Text(shape=(), dtype=string),
})
Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
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|
answer |
Text |
|
string |
|
category |
ClassLabel |
|
int64 |
|
label |
ClassLabel |
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int64 |
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question |
Text |
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string |
|
sentence |
Text |
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string |
|
@inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
}
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Last updated 2022-12-14 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-12-14 UTC."],[],[],null,["# mctaco\n\n\u003cbr /\u003e\n\n- **Description**:\n\nMC-TACO is a dataset of 13k question-answer pairs that require temporal\ncommonsense comprehension. The dataset contains five temporal properties:\n\n1. duration (how long an event takes)\n2. temporal ordering (typical order of events)\n3. typical time (when an event occurs)\n4. frequency (how often an event occurs)\n5. stationarity (whether a state is maintained for a very long time or indefinitely)\n\nWe hope that this dataset can promote the future exploration of this particular\nclass of reasoning problems.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/mc-taco)\n\n- **Homepage** :\n \u003chttps://github.com/CogComp/MCTACO\u003e\n\n- **Source code** :\n [`tfds.datasets.mctaco.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/mctaco/mctaco_dataset_builder.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): No release notes.\n- **Download size** : `2.27 MiB`\n\n- **Dataset size** : `3.18 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| `'test'` | 9,442 |\n| `'validation'` | 3,783 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'answer': Text(shape=(), dtype=string),\n 'category': ClassLabel(shape=(), dtype=int64, num_classes=5),\n 'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n 'question': Text(shape=(), dtype=string),\n 'sentence': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|----------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| answer | Text | | string | |\n| category | ClassLabel | | int64 | |\n| label | ClassLabel | | int64 | |\n| question | Text | | string | |\n| sentence | Text | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\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{ZKNR19,\n author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},\n title = {\"Going on a vacation\" takes longer than \"Going for a walk\": A Study of Temporal Commonsense Understanding },\n booktitle = {EMNLP},\n year = {2019},\n }"]]