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Datasets:
sled-umich
/
TRIP

Tasks:
Text Classification
Modalities:
Tabular
Text
Sub-tasks:
natural-language-inference
Languages:
English
Size:
1K - 10K
Libraries:
Datasets
Dataset card Viewer Files Community
TRIP
File size: 5,600 Bytes 
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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