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"""LILA: A Unified Benchmark for Mathematical Reasoning |
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Loading script author: Sean Welleck |
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""" |
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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@INPROCEEDINGS{Mishra2022Lila, |
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author = { |
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Swaroop Mishra |
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and Matthew Finlayson |
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and Pan Lu |
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and Leonard Tang |
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and Sean Welleck |
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and Chitta Baral |
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and Tanmay Rajpurohit |
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and Oyvind Tafjord |
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and Ashish Sabharwal |
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and Peter Clark |
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and Ashwin Kalyan}, |
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title = {Lila: A Unified Benchmark for Mathematical Reasoning}, |
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booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
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year = {2022} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Līla is a comprehensive benchmark for mathematical reasoning with over 140K natural language questions annotated with Python programs and natural language instructions. The data set comes with multiple splits: Līla-IID (train, dev, test), Līla-OOD (train, dev, test), and Līla-Robust. |
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""" |
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_HOMEPAGE = "https://lila.apps.allenai.org/" |
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_LICENSE = "Creative Commons Attribution 4.0 International" |
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_URL = "https://github.com/allenai/Lila/raw/b81117ac7e56cc1dfb0fcabf0005d1755177252b/lila.zip" |
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_BASEDIR = 'lila' |
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_MULTIDIR = 'multi' |
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_ALLDIR = 'all' |
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_NAMES = [ |
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'iid', |
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'ood', |
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'addsub', |
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'amps_algebra', |
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'amps_calculus', |
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'amps_counting_and_stats', |
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'amps_geometry', |
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'amps_linear_algebra', |
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'amps_number_theory', |
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'APPS_structured', |
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'asdiv', |
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'conala_structured', |
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'deepmind_mathematics_algebra', |
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'deepmind_mathematics_basicmath', |
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'deepmind_mathematics_calculus', |
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'deepmind_mathematics_muldiv', |
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'deepmind_mathematics_numbertheory', |
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'dolphin_t2_final', |
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'draw_structured', |
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'GSM8k_structured', |
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'MATH_algebra_crowdsourced', |
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'MATH_counting_and_probability_crowdsourced', |
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'MATH_intermediate_algebra_crowdsourced', |
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'mathqa_gain', |
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'mathqa_general', |
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'mathqa_geometry', |
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'mathqa_other', |
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'mathqa_physics', |
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'mathqa_probability', |
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'mbpp_structured', |
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'MCTaco_event_duration_structured', |
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'MCTaco_event_ordering_structured', |
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'MCTaco_event_typical_time_structured', |
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'MCTaco_frequency_structured', |
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'MCTaco_stationarity_structured', |
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'multiarith', |
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'Numersense_structured', |
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'NumGLUE_Type_1_crowdsourced', |
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'NumGLUE_Type_2_crowdsourced', |
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'NumGLUE_Type_3_crowdsourced', |
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'NumGLUE_Type_4_crowdsourced', |
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'NumGLUE_Type_5_crowdsourced', |
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'NumGLUE_Type_6_crowdsourced', |
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'NumGLUE_Type_7_crowdsourced', |
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'NumGLUE_Type_8_crowdsourced', |
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'simuleq', |
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'singleop', |
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'singleq', |
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'svamp_structured' |
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] |
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VERSION = "1.1.0" |
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class Lila(datasets.GeneratorBasedBuilder): |
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"""Lila: A Unified Benchmark for Mathematical Reasoning""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=name, version=VERSION, description=name) for name in _NAMES |
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] |
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DEFAULT_CONFIG_NAME = "iid" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"input": datasets.Value("string"), |
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"output_program": datasets.Value("string"), |
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"output_answer": datasets.Value("string"), |
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"split": datasets.Value("string"), |
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"dataset": 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 = _URL |
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data_dir = dl_manager.download_and_extract(urls) |
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if self.config.name in {'iid', 'ood'}: |
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train_filepath = os.path.join(data_dir, _BASEDIR, _MULTIDIR, self.config.name, "train.json") |
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dev_filepath = os.path.join(data_dir, _BASEDIR, _MULTIDIR, self.config.name, "dev.json") |
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test_filepath = os.path.join(data_dir, _BASEDIR, _MULTIDIR, self.config.name, "test.json") |
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else: |
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train_filepath = os.path.join(data_dir, _BASEDIR, _ALLDIR, self.config.name + ".json") |
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dev_filepath = train_filepath |
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test_filepath = train_filepath |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": train_filepath, |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": dev_filepath, |
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"split": "dev", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": test_filepath, |
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"split": "test" |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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if self.config.name in {"iid", "ood"}: |
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with open(filepath) as f: |
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for key, row in enumerate(f.readlines()): |
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data = json.loads(row) |
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yield key, { |
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"input": data["Input"], |
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"output_program": data["Output Program"][0], |
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"output_answer": data["Output Answer"][0], |
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"split": data["split"], |
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"dataset": data["dataset"], |
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} |
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else: |
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for key, data in enumerate(json.load(open(filepath))["Instances"]): |
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if data['split'] == split: |
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yield key, { |
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"input": data["Input"], |
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"output_program": data["Output Program"][0], |
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"output_answer": data["Output Answer"][0], |
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"split": data["split"], |
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"dataset": self.config.name, |
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} |
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