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competition_math / competition_math.py

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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.
	"""Accuracy metric for the Mathematics Aptitude Test of Heuristics (MATH) dataset."""

	import datasets
	import math_equivalence # From: git+https://github.com/hendrycks/math.git

	import evaluate


	_CITATION = """\
	@article{hendrycksmath2021,
	title={Measuring Mathematical Problem Solving With the MATH Dataset},
	author={Dan Hendrycks
	and Collin Burns
	and Saurav Kadavath
	and Akul Arora
	and Steven Basart
	and Eric Tang
	and Dawn Song
	and Jacob Steinhardt},
	journal={arXiv preprint arXiv:2103.03874},
	year={2021}
	}
	"""


	_DESCRIPTION = """\
	This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
	It first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.
	"""


	_KWARGS_DESCRIPTION = r"""
	Calculates accuracy after canonicalizing inputs.

	Args:
	predictions: list of predictions to score. Each prediction
	is a string that contains natural language and LaTex.
	references: list of reference for each prediction. Each
	reference is a string that contains natural language
	and LaTex.
	Returns:
	accuracy: accuracy after canonicalizing inputs
	(e.g., converting "1/2" to "\\frac{1}{2}")

	Examples:
	>>> metric = evaluate.load("competition_math")
	>>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])
	>>> print(results)
	{'accuracy': 1.0}
	"""


	@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
	class CompetitionMathMetric(evaluate.Metric):
	"""Accuracy metric for the MATH dataset."""

	def _info(self):
	return evaluate.MetricInfo(
	description=_DESCRIPTION,
	citation=_CITATION,
	inputs_description=_KWARGS_DESCRIPTION,
	features=datasets.Features(
	{
	"predictions": datasets.Value("string"),
	"references": datasets.Value("string"),
	}
	),
	# Homepage of the metric for documentation
	homepage="https://github.com/hendrycks/math",
	# Additional links to the codebase or references
	codebase_urls=["https://github.com/hendrycks/math"],
	)

	def _compute(self, predictions, references):
	"""Returns the scores"""
	n_correct = 0.0
	for i, j in zip(predictions, references):
	n_correct += 1.0 if math_equivalence.is_equiv(i, j) else 0.0
	accuracy = n_correct / len(predictions)
	return {
	"accuracy": accuracy,
	}