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import os |
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from zipfile import ZipFile |
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import datasets |
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_CITATION = """\ |
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@misc{storks2021tiered, |
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title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding}, |
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author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai}, |
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year={2021}, |
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booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}, |
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location={Punta Cana, Dominican Republic}, |
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publisher={Association for Computational Linguistics}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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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. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/sled-umich/TRIP" |
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SERVER_URL = "http://162.212.153.129/" |
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class TRIP(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.1") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"example_id": datasets.Value("string"), |
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"length": datasets.Value("int32"), |
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"label": datasets.Value("int32"), |
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"breakpoint": datasets.Value("int32"), |
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"confl_sents": [datasets.Value("int32")], |
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"confl_pairs": [[datasets.Value("int32")]], |
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"stories":[{ |
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"story_id": datasets.Value("int32"), |
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"worker_id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"idx": datasets.Value("int32"), |
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"aug": datasets.Value("bool"), |
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"actor": datasets.Value("string"), |
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"location": datasets.Value("string"), |
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"objects": datasets.Value("string"), |
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"sentences": datasets.features.Sequence(datasets.Value("string")), |
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"length": datasets.Value("int32"), |
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"example_id": datasets.Value("string"), |
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"plausible": datasets.Value("bool"), |
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"breakpoint": datasets.Value("int32"), |
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"confl_sents": datasets.features.Sequence(datasets.Value("int32")), |
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"confl_pairs": [[datasets.Value("int32")]], |
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"state-h_location": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-conscious": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-wearing": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-h_wet": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-hygiene": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-location": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-exist": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-clean": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-power": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-functional": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-pieces": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-wet": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-open": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-temperature": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-solid": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-contain": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-running": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-moveable": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-mixed": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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"state-edible": [[{"entity": datasets.Value("string"), "num": datasets.Value("int32")}]], |
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}] |
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} |
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), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager): |
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"""Returns SplitGenerators.""" |
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splits = ["ClozeDev", "ClozeTest", "ClozeTrain", "OrderDev", "OrderTest", "OrderTrain"] |
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data_roots = dl_manager.download_and_extract({k: SERVER_URL + f"trip-{k}.jsonl" for k in splits}) |
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return [ |
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datasets.SplitGenerator( |
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name=split, |
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gen_kwargs={ |
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"filepath": data_roots[split], |
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}, |
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) |
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for split in splits |
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] |
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def _generate_examples(self, filepath): |
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import json |
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with open(filepath) as f: |
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data = [json.loads(line) for line in f] |
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for i, example in enumerate(data): |
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yield i, example |
