garrethlee
commited on
Delete loading script
Browse files- math_dataset.py +0 -275
math_dataset.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Mathematics database."""
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """
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@article{2019arXiv,
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author = {Saxton, Grefenstette, Hill, Kohli},
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title = {Analysing Mathematical Reasoning Abilities of Neural Models},
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year = {2019},
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journal = {arXiv:1904.01557}
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}
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"""
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_DESCRIPTION = """
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Mathematics database.
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This dataset code generates mathematical question and answer pairs,
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from a range of question types at roughly school-level difficulty.
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This is designed to test the mathematical learning and algebraic
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reasoning skills of learning models.
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Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
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(Saxton, Grefenstette, Hill, Kohli).
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Example usage:
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train_examples, val_examples = datasets.load_dataset(
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'math_dataset/arithmetic__mul',
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split=['train', 'test'],
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as_supervised=True)
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"""
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_DATA_URL = "https://storage.googleapis.com/mathematics-dataset/mathematics_dataset-v1.0.tar.gz"
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_TRAIN_CATEGORY = [
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"train-easy",
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"train-medium",
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"train-hard",
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]
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_INTERPOLATE_CATEGORY = [
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"interpolate",
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]
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_MODULES = [
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# extrapolate
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"measurement__conversion",
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# interpolate
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"algebra__linear_1d",
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"algebra__linear_1d_composed",
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"algebra__linear_2d",
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"algebra__linear_2d_composed",
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"algebra__polynomial_roots",
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"algebra__polynomial_roots_composed",
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"algebra__sequence_next_term",
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"algebra__sequence_nth_term",
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"arithmetic__add_or_sub",
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"arithmetic__add_or_sub_in_base",
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"arithmetic__add_sub_multiple",
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"arithmetic__div",
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"arithmetic__mixed",
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"arithmetic__mul",
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"arithmetic__mul_div_multiple",
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"arithmetic__nearest_integer_root",
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"arithmetic__simplify_surd",
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"calculus__differentiate",
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"calculus__differentiate_composed",
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"comparison__closest",
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"comparison__closest_composed",
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"comparison__kth_biggest",
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"comparison__kth_biggest_composed",
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"comparison__pair",
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"comparison__pair_composed",
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"comparison__sort",
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"comparison__sort_composed",
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"measurement__conversion",
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"measurement__time",
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"numbers__base_conversion",
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"numbers__div_remainder",
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"numbers__div_remainder_composed",
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"numbers__gcd",
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"numbers__gcd_composed",
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"numbers__is_factor",
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"numbers__is_factor_composed",
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"numbers__is_prime",
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"numbers__is_prime_composed",
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"numbers__lcm",
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"numbers__lcm_composed",
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"numbers__list_prime_factors",
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"numbers__list_prime_factors_composed",
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"numbers__place_value",
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"numbers__place_value_composed",
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"numbers__round_number",
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"numbers__round_number_composed",
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"polynomials__add",
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"polynomials__coefficient_named",
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"polynomials__collect",
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"polynomials__compose",
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"polynomials__evaluate",
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"polynomials__evaluate_composed",
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"polynomials__expand",
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"polynomials__simplify_power",
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"probability__swr_p_level_set",
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"probability__swr_p_sequence",
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# train-easy train-medium train-hard
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"algebra__linear_1d",
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"algebra__linear_1d_composed",
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"algebra__linear_2d",
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"algebra__linear_2d_composed",
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"algebra__polynomial_roots",
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"algebra__polynomial_roots_composed",
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"algebra__sequence_next_term",
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"algebra__sequence_nth_term",
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"arithmetic__add_or_sub",
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"arithmetic__add_or_sub_in_base",
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"arithmetic__add_sub_multiple",
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"arithmetic__div",
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"arithmetic__mixed",
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"arithmetic__mul",
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"arithmetic__mul_div_multiple",
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"arithmetic__nearest_integer_root",
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"arithmetic__simplify_surd",
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"calculus__differentiate",
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"calculus__differentiate_composed",
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"comparison__closest",
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"comparison__closest_composed",
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"comparison__kth_biggest",
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"comparison__kth_biggest_composed",
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"comparison__pair",
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"comparison__pair_composed",
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"comparison__sort",
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"comparison__sort_composed",
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"measurement__conversion",
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"measurement__time",
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"numbers__base_conversion",
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"numbers__div_remainder",
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"numbers__div_remainder_composed",
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"numbers__gcd",
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"numbers__gcd_composed",
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"numbers__is_factor",
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"numbers__is_factor_composed",
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"numbers__is_prime",
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"numbers__is_prime_composed",
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"numbers__lcm",
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"numbers__lcm_composed",
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"numbers__list_prime_factors",
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"numbers__list_prime_factors_composed",
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"numbers__place_value",
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"numbers__place_value_composed",
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"numbers__round_number",
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"numbers__round_number_composed",
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"polynomials__add",
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"polynomials__coefficient_named",
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"polynomials__collect",
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"polynomials__compose",
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"polynomials__evaluate",
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"polynomials__evaluate_composed",
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"polynomials__expand",
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"polynomials__simplify_power",
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"probability__swr_p_level_set",
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"probability__swr_p_sequence",
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]
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_QUESTION = "question"
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_ANSWER = "answer"
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_DATASET_VERSION = "mathematics_dataset-v1.0"
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def _generate_builder_configs():
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"""Generate configs with different subsets of mathematics dataset."""
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configs = []
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for module in sorted(set(_MODULES)):
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configs.append(
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datasets.BuilderConfig(
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name=module,
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version=datasets.Version("1.0.0"),
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description=_DESCRIPTION,
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)
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)
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return configs
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class MathDataset(datasets.GeneratorBasedBuilder):
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"""Math Dataset."""
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BUILDER_CONFIGS = _generate_builder_configs()
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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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_QUESTION: datasets.Value("string"),
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_ANSWER: datasets.Value("string"),
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}
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),
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supervised_keys=(_QUESTION, _ANSWER),
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homepage="https://github.com/deepmind/mathematics_dataset",
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citation=_CITATION,
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)
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def _get_filepaths_from_categories(self, config, categories):
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filepaths = []
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for category in categories:
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data_file = "/".join([_DATASET_VERSION, category, config])
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filepaths.append(data_file)
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return set(filepaths)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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archive = dl_manager.download(_DATA_URL)
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config = self.config.name + ".txt"
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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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"files": dl_manager.iter_archive(archive),
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"config": config,
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"categories": _TRAIN_CATEGORY,
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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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"files": dl_manager.iter_archive(archive),
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"config": config,
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"categories": _INTERPOLATE_CATEGORY,
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},
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),
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]
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def _generate_examples(self, files, config, categories):
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"""Yields examples based on directory, module file.."""
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idx = 0
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filepaths = self._get_filepaths_from_categories(config, categories)
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for path, f in files:
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if not filepaths:
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break
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elif path in filepaths:
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for question in f:
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if not question:
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continue
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else:
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for answer in f:
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if not answer:
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continue
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else:
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yield idx, {_QUESTION: question, _ANSWER: answer}
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idx += 1
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break
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filepaths.remove(path)
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