Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Size:
1K - 10K
Tags:
import os | |
from zipfile import ZipFile | |
import datasets | |
_CITATION = """\ | |
@misc{storks2021tiered, | |
title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding}, | |
author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai}, | |
year={2021}, | |
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}, | |
location={Punta Cana, Dominican Republic}, | |
publisher={Association for Computational Linguistics}, | |
} | |
""" | |
_DESCRIPTION = """\ | |
We introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. | |
""" | |
_HOMEPAGE = "https://huggingface.co/datasets/sled-umich/TRIP" | |
class TRIP(datasets.GeneratorBasedBuilder): | |
VERSION = datasets.Version("1.0.1") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"example_id": datasets.Value("string"), | |
"length": datasets.Value("int32"), | |
"label": datasets.Value("int32"), | |
"breakpoint": datasets.Value("int32"), | |
"confl_sents": [datasets.Value("int32")], | |
"confl_pairs": [[datasets.Value("int32")]], | |
"stories":[{ | |
"story_id": datasets.Value("int32"), | |
"worker_id": datasets.Value("string"), | |
"type": datasets.Value("string"), | |
"idx": datasets.Value("int32"), | |
"aug": datasets.Value("bool"), | |
"actor": datasets.Value("string"), | |
"location": datasets.Value("string"), | |
"objects": datasets.Value("string"), | |
"sentences": datasets.features.Sequence(datasets.Value("string")), | |
"length": datasets.Value("int32"), | |
"example_id": datasets.Value("string"), | |
"plausible": datasets.Value("bool"), | |
"breakpoint": datasets.Value("int32"), | |
"confl_sents": datasets.features.Sequence(datasets.Value("int32")), | |
"confl_pairs": [[datasets.Value("int32")]], | |
"state-h_location": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-conscious": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-wearing": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-h_wet": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-hygiene": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-location": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-exist": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-clean": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-power": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-functional": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-pieces": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-wet": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-open": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-temperature": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-solid": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-contain": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-running": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-moveable": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-mixed": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
"state-edible": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], | |
}] | |
} | |
), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager: datasets.DownloadManager): | |
"""Returns SplitGenerators.""" | |
splits = ["ClozeDev", "ClozeTest", "ClozeTrain", "OrderDev", "OrderTest", "OrderTrain"] | |
data_roots = dl_manager.download_and_extract({k: f"trip-{k}.jsonl" for k in splits}) | |
return [ | |
datasets.SplitGenerator( | |
name=split, | |
gen_kwargs={ | |
"filepath": data_roots[split], | |
}, | |
) | |
for split in splits | |
] | |
def _generate_examples(self, filepath): | |
# load jsonl file | |
import json | |
with open(filepath) as f: | |
data = [json.loads(line) for line in f] | |
for i, example in enumerate(data): | |
yield i, example |