Add dataset loading script

qiskit_humaneval.py

@@ -0,0 +1,92 @@
+# 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']
+                }