a sythetic generate dataset for testing numerical reasoning
Browse files- README.md +1 -0
- numerical_reasoning.py +157 -0
README.md
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# Numerical Reasoning
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numerical_reasoning.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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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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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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import csv
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import json
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import os
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import datasets
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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Generated dataset for testing numerical reasoning
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLS = {
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"first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
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"second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
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}
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class NewDataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("0.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="arithmetic_multiplication", version=VERSION, description="x1 x x2 = y"),
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datasets.BuilderConfig(name="arithmetic_addition", version=VERSION, description="x1 + x2 = y"),
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datasets.BuilderConfig(name="op_infer_mult", version=VERSION, description="x1 # x2 = y, must infer that # is multiplication"),
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datasets.BuilderConfig(name="op_infer_add", version=VERSION, description="x1 # x2 = y, must infer that # is addition"),
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datasets.BuilderConfig(name="time_unit_min_sec", version=VERSION, description="x minutes equals y seconds"),
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datasets.BuilderConfig(name="time_unit_hour_min", version=VERSION, description="x hours equals y minutes"),
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datasets.BuilderConfig(name="time_unit_day_hour", version=VERSION, description="x days equals y hours"),
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datasets.BuilderConfig(name="time_unit_week_day", version=VERSION, description="x minutes equals y seconds"),
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datasets.BuilderConfig(name="time_unit_month_week", version=VERSION, description="x months equals y weeks"),
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datasets.BuilderConfig(name="time_unit_year_month", version=VERSION, description="x years equals y months"),
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datasets.BuilderConfig(name="time_unit_decade_year", version=VERSION, description="x decades equals y years"),
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]
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DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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if ("arithmetic" in self.config.name) or ("op_infer" in self.config.name): # This is the name of the configuration selected in BUILDER_CONFIGS above
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features = datasets.Features(
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{
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"x1": datasets.Value("int32"),
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"x2": datasets.Value("int32"),
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"y": datasets.Value("int32")
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}
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)
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else:
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features = datasets.Features(
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{
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"x1": datasets.Value("int32"),
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"time_unit": datasets.Value("string"),
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"y": datasets.Value("int32")
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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, # Here we define them above because they are different between the two configurations
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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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return [
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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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, split):
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if ("arithmetic" in self.config.name) or ("op_infer" in self.config.name):
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key = 0
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for x1 in range(0,100):
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for x2 in range(1,51):
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key += 1
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# Yields examples as (key, example) tuples
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yield key, {
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"x1": x1,
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"x2": x2,
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"y": x1*x2,
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}
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else:
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if "min_sec" in self.config.name:
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time_unit = "minutes"
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multiplier = 60
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elif "hour_min" in self.config.name:
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time_unit = "hours"
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multiplier = 60
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elif "day_hour" in self.config.name:
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time_unit = "days"
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multiplier = 24
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elif "week_day" in self.config.name:
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time_unit = "weeks"
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multiplier = 7
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elif "month_week" in self.config.name:
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time_unit = "months"
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multiplier = 30
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elif "year_month" in self.config.name:
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time_unit = "years"
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multiplier = 12
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elif "decade_year" in self.config.name:
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time_unit = "decades"
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multiplier = 10
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for key, x1 in enumerate(range(0, 100)):
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yield key, {
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"x1": x1,
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"time_unit": time_unit,
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"y": multiplier*x1,
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}
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