Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
natural-language-inference
Languages:
English
Size:
10M<n<100M
Tags:
License:
| import datasets | |
| import json | |
| import os | |
| citation=''' | |
| @inproceedings{rudinger-etal-2020-thinking, | |
| title = "Thinking Like a Skeptic: Defeasible Inference in Natural Language", | |
| author = "Rudinger, Rachel and | |
| Shwartz, Vered and | |
| Hwang, Jena D. and | |
| Bhagavatula, Chandra and | |
| Forbes, Maxwell and | |
| Le Bras, Ronan and | |
| Smith, Noah A. and | |
| Choi, Yejin", | |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", | |
| month = nov, | |
| year = "2020", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/2020.findings-emnlp.418", | |
| doi = "10.18653/v1/2020.findings-emnlp.418", | |
| pages = "4661--4675" | |
| } | |
| ''' | |
| class DefeasibleNLIConfig(datasets.BuilderConfig): | |
| citation=citation | |
| configs = ['atomic','snli','social'] | |
| splits=['train', 'test', 'dev'] | |
| _URLs = {(f,s):f"https://huggingface.co/datasets/metaeval/defeasible-nli/resolve/main/{f}_{s}.jsonl" for f in configs for s in splits} | |
| class DefeasibleNLI(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| DefeasibleNLIConfig( | |
| name=n, | |
| data_dir=n | |
| ) for n in configs | |
| ] | |
| def _split_generators(self, dl_manager: datasets.DownloadManager): | |
| path = lambda split: dl_manager.download(_URLs[self.config.name,split]) | |
| return [ datasets.SplitGenerator(name=name, gen_kwargs={'path':path(split),'split':split}) | |
| for name,split in zip([datasets.Split.TRAIN,datasets.Split.VALIDATION,datasets.Split.TEST], | |
| ['train','dev','test'])] | |
| def _info(self): | |
| return datasets.DatasetInfo() | |
| def _generate_examples(self,path,split): | |
| """Yields examples.""" | |
| with open(path, "r", encoding="utf-8") as f: | |
| for id_, line in enumerate(f): | |
| line_dict = json.loads(line) | |
| if not line_dict['UpdateTypeImpossible']: | |
| fields = ["Premise","Hypothesis","Update","UpdateType"]#,"UpdateTypeImpossible","UpdateTypeImpossibleReason"] | |
| line_dict = {k:v for k,v in line_dict.items() if k in fields} | |
| yield id_, line_dict | |