qiskit_humaneval.py

# 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.
""" qiskit_humaneval dataset"""

import json
import datasets
import os
import requests
logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@misc{2406.14712,
Author = {Sanjay Vishwakarma and Francis Harkins and Siddharth Golecha and Vishal Sharathchandra Bajpe and Nicolas Dupuis and Luca Buratti and David Kremer and Ismael Faro and Ruchir Puri and Juan Cruz-Benito},
Title = {Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models},
Year = {2024},
Eprint = {arXiv:2406.14712},
}
"""

_DESCRIPTION = """\
qiskit_humaneval is a dataset for evaluating LLM's at writing Qiskit code.
"""

_HOMEPAGE = "https://github.com/qiskit-community/qiskit-human-eval"

_LICENSE = "apache-2.0"

_URL = "https://raw.githubusercontent.com/qiskit-community/qiskit-human-eval/"\
        "refs/heads/main/dataset/dataset_qiskit_test_human_eval.json"

class QiskitHumanEval(datasets.GeneratorBasedBuilder):
    """ qiskit_humaneval dataset

    0.1.0: first version of the dataset
    """

    VERSION = datasets.Version("0.1.0")

    def _info(self):
        features = datasets.Features(
            {
                'task_id': datasets.Value('string'),
                'prompt': datasets.Value('string'),
                'canonical_solution': datasets.Value('string'),
                'test': datasets.Value('string'),
                'entry_point': datasets.Value('string'),
                'difficulty_scale': datasets.Value('string')
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        filepath = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": filepath,
                },
            ),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, 'r', encoding="UTF-8") as in_json:
            for row in json.load(in_json):
                id_ = row['task_id']
                yield id_, {
                    'task_id': row['task_id'],
                    'prompt': row['prompt'],
                    'canonical_solution': row['canonical_solution'],
                    'test': row['test'],
                    'entry_point': row['entry_point'],
                    'difficulty_scale': row['difficulty_scale']
                }