Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
math-word-problems
License:
File size: 4,468 Bytes
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Grade School Math 8k dataset."""
import json
import textwrap
import datasets
_CITATION = """\
@misc{cobbe2021training,
title={Training Verifiers to Solve Math Word Problems},
author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
year={2021},
eprint={2110.14168},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
"""
_DESCRIPTION = """\
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality
linguistically diverse grade school math word problems. The
dataset was created to support the task of question answering
on basic mathematical problems that require multi-step reasoning.
"""
_HOMEPAGE = "https://openai.com/blog/grade-school-math"
_LICENSE = "MIT"
_BASE_URL = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/"
class Gsm8kConfig(datasets.BuilderConfig):
"""BuilderConfig for GSM8K."""
def __init__(self, urls, **kwargs):
"""BuilderConfig for GSM8K.
Args:
urls: *dict[string]*, the urls for each split of the GSM8k set.
"""
super().__init__(version=datasets.Version("1.1.0"), **kwargs)
self.urls = urls
class Gsm8k(datasets.GeneratorBasedBuilder):
"""Grade School Math 8k (GSM8K)"""
BUILDER_CONFIGS = [
Gsm8kConfig(
name="main",
description=textwrap.dedent(
"""
It is segmented into 7.5K training problems and 1K test problems.
These problems take between 2 and 8 steps to solve, and solutions
primarily involve performing a sequence of elementary calculations
using basic arithmetic operations (+ - / *) to reach the final
answer. A bright middle school student should be able to solve
every problem.
""",
),
urls={
"train": _BASE_URL + "train.jsonl",
"test": _BASE_URL + "test.jsonl",
},
),
Gsm8kConfig(
name="socratic",
description=textwrap.dedent(
"""
Additionally, there is a modified solution format that injects
automatically generated "Socratic subquestions" before each step.
"""
),
urls={
"train": _BASE_URL + "train_socratic.jsonl",
"test": _BASE_URL + "test_socratic.jsonl",
},
),
]
def _info(self):
features = datasets.Features(
{
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(self.config.urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir["test"],
},
),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
yield key, {
"question": data["question"],
"answer": data["answer"],
}
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