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import datasets |
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import json |
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import ast |
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import pandas as pd |
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import csv |
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_DESCRIPTION = """\ |
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The dataset is an amendment and re-annotation of LogiQA in 2020, a large-scale logical reasoning reading comprehension dataset adapted from the Chinese Civil Service Examination. We increase the data size, refine the texts with manual translation by professionals, and improve the quality by removing items with distinctive cultural features like Chinese idioms. Furthermore, we conduct a fine-grained annotation on the dataset and turn it into a two-way natural language inference (NLI) task, resulting in 35k premise-hypothesis pairs with gold labels, making it the first large-scale NLI dataset for complex logical reasoning |
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""" |
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english_qa_datasets = ["lsat-ar", "lsat-lr", "lsat-rc", "logiqa-en", "sat-math", "sat-en", "aqua-rat", |
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"sat-en-without-passage", "gaokao-english"] |
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chinese_qa_datasets = ["logiqa-zh", "jec-qa-kd", "jec-qa-ca", "gaokao-chinese", "gaokao-geography", "gaokao-history", |
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"gaokao-biology", "gaokao-chemistry", "gaokao-physics", "gaokao-mathqa"] |
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english_cloze_datasets = ["math"] |
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chinese_cloze_datasets = ["gaokao-mathcloze"] |
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all_lst = english_qa_datasets + chinese_qa_datasets + english_cloze_datasets + chinese_cloze_datasets |
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HEAD= 'https://raw.githubusercontent.com/microsoft/AGIEval/main/data/v1/' |
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_URLS = { |
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e: { |
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"test": HEAD+e+'.jsonl', |
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} for e in all_lst |
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} |
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_URLS['few_shot'] ={'few_shot':'https://raw.githubusercontent.com/microsoft/AGIEval/main/data/few_shot_prompts.csv'} |
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class AgiEval(datasets.GeneratorBasedBuilder): |
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"""TODO: Short description of my dataset.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name= e, |
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version=datasets.Version("2.0.1"), |
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description="", |
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) for e in all_lst |
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] |
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DEFAULT_CONFIG_NAME = "aqua_rat" |
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def _info(self): |
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if self.config.name == "aqua_rat": |
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features = datasets.Features( |
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{ |
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"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.features.Sequence(datasets.Value("string")), |
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"label": datasets.ClassLabel(num_classes=5, names=["A", "B", "C", "D", "E"]), |
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"solution": datasets.Value("string"), |
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} |
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) |
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elif self.config.name in ["sat_en", "sat_math", "sat-en-without-passage"]: |
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features = datasets.Features( |
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{"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.features.Sequence(datasets.Value("string")), |
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"label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]), |
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"solution": datasets.Value("string"), |
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} |
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) |
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elif self.config.name in ['logiqa-en', 'logiqa-zh']: |
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features = datasets.Features( |
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{"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.features.Sequence(datasets.Value("string")), |
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"label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]), |
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"solution": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == 'math': |
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features = datasets.Features( |
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{ |
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"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"solution": datasets.Value("string"), |
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"level": datasets.Value("int32"), |
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"type": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == 'gaokao-mathcloze': |
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features = datasets.Features( |
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{ |
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"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"answer": datasets.Value("string"), |
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"solution": datasets.Value("string"), |
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} |
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) |
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elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']: |
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features = datasets.Features( |
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{ |
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"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.features.Sequence(datasets.Value("string")), |
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"label": datasets.ClassLabel(num_classes=5, names=["A", "B", "C", "D", "E"]), |
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"solution": datasets.Value("string"), |
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} |
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) |
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elif self.config.name in ['gaokao-mathqa', 'gaokao-chinese', 'gaokao-history', 'gaokao-geography', 'gaokao-biology', 'gaokao-chemistry', 'gaokao-english']: |
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features = datasets.Features( |
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{ |
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"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.features.Sequence(datasets.Value("string")), |
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"label": datasets.features.Sequence(datasets.Value("string")), |
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"solution": datasets.Value("string"), |
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} |
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) |
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elif self.config.name in ['gaokao-physics', 'jec-qa-ca', 'jec-qa-kd']: |
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features = datasets.Features( |
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{ |
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"passage": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"options": datasets.features.Sequence(datasets.Value("string")), |
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"label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]), |
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"solution": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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_urls = _URLS[self.config.name] |
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urls = { |
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"test": _urls["test"], |
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"few_shot": _URLS["few_shot"]["few_shot"], |
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} |
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data_dir = dl_manager.download_and_extract(urls) |
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splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"filepath": data_dir["test"], "split": "test"}, |
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), |
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] |
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splits.append(datasets.SplitGenerator( |
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name="few_shot", |
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gen_kwargs={"filepath": data_dir["few_shot"], "split": "few_shot"}, |
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)) |
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return splits |
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def _generate_examples(self, filepath, split): |
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names = {'aqua_rat': 'aqua-rat', 'sat_en': 'sat-en', 'sat_math': 'sat-math', |
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'lsat_ar': 'lsat-ar', 'lsat_lr': 'lsat-lr', 'lsat_rc': 'lsat-rc', |
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'logiqa': 'logiqa-en', 'math_agieval': 'math'} |
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if split == "few_shot": |
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df = pd.read_csv(filepath, keep_default_na=False) |
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samples = df[df.index % 2 == 0].reset_index(drop=True) |
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explanations = df[df.index % 2 != 0].reset_index(drop=True) |
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for key in range(samples.shape[0]): |
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try: |
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data = ast.literal_eval(samples[names[self.config.name]][key]) |
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explanation_row = explanations[names[self.config.name]][key] |
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if self.config.name == "aqua_rat": |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": str(explanation_row), |
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} |
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) |
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elif self.config.name in ["sat_en", "sat_math", "sat-en-without-passage"]: |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": str(explanation_row), |
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} |
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) |
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elif self.config.name in ['logiqa-en', 'logiqa-zh']: |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": str(explanation_row), |
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} |
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) |
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elif self.config.name == 'math': |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"answer": data["answer"], |
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"solution": str(explanation_row), |
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"level": data["level"], |
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"type": data["type"], |
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} |
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) |
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elif self.config.name == 'gaokao-mathcloze': |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"answer": data["answer"], |
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"solution": str(explanation_row), |
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} |
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) |
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elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']: |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["passquestionage"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": str(explanation_row), |
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} |
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) |
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elif self.config.name in ['gaokao-mathqa', 'gaokao-chinese', 'gaokao-history', 'gaokao-geography', 'gaokao-biology', 'gaokao-chemistry', 'gaokao-english']: |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": str(explanation_row), |
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} |
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) |
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elif self.config.name in ['gaokao-physics', 'jec-qa-ca', 'jec-qa-kd']: |
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features = datasets.Features( |
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{ |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": str(explanation_row), |
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} |
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) |
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except: |
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pass |
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else: |
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with open(filepath, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = json.loads(row) |
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if self.config.name == "aqua_rat": |
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yield key, { |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": data["other"]["solution"], |
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} |
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elif self.config.name in ["sat_en", "sat_math", "sat-en-without-passage"]: |
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label_index = "ABCDE".index(data["label"]) |
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if label_index > len(data["options"]) - 1: |
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continue |
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else: |
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yield key, { |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": data["other"]["solution"], |
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} |
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elif self.config.name in ['logiqa-en', 'logiqa-zh']: |
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yield key, { |
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"passage": data["passage"], |
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"question": data["question"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": data["label"], |
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} |
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elif self.config.name == 'math': |
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if not data.get("level"): |
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data["level"] = data['other']['level'] |
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if not data.get("type"): |
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data["type"] = data['other']['type'] |
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yield key, { |
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"question": data["question"], |
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"answer": data["answer"], |
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"solution": data["other"]["solution"], |
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"level": data["level"], |
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"type": data["type"], |
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} |
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elif self.config.name == 'gaokao-mathcloze': |
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yield key, { |
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"passage": data["passage"], |
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"question": data["question"], |
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"answer": data["answer"], |
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"solution": data["answer"], |
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} |
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elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']: |
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yield key, { |
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"passage": data["passage"], |
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"question": data["passquestionage"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": data["label"], |
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} |
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elif self.config.name in ['gaokao-mathqa', 'gaokao-chinese', 'gaokao-history', 'gaokao-geography', 'gaokao-biology', 'gaokao-chemistry', 'gaokao-english']: |
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yield key, { |
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"passage": data["passage"], |
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"question": data["passquestionage"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": data["label"], |
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} |
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elif self.config.name in ['gaokao-physics', 'jec-qa-ca', 'jec-qa-kd']: |
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yield key, { |
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"passage": data["passage"], |
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"question": data["passquestionage"], |
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"options": data["options"], |
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"label": data["label"], |
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"solution": data["label"], |
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} |
