Create ESPOSITO.py

ESPOSITO.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.
+import os
+
+import datasets
+import pandas as pd
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = """\
+"""
+
+_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
+
+domains = {
+    "Human Science": [
+        "Human Science",
+        "Art & Architecture",
+    ],
+    "Medicine": [
+        "Medical Science",
+        "Veterinary Science",
+    ],
+    "Science & engineering": [
+        "Agriculture & Natural Resources",
+        "Engineering & Technology",
+        "Basic Science",
+    ],
+}
+
+
+class ESPOSITOConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+
+
+class ESPOSITO(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        ESPOSITOConfig(
+            name=domain,
+        )
+        for domain in domains.keys()
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id":datasets.Value("int32"),
+                "question": datasets.Value("string"),
+                "A": datasets.Value("string"),
+                "B": datasets.Value("string"),
+                "C": datasets.Value("string"),
+                "D": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                "explanation":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(_URL)
+        task_name = self.config.name
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "test", f"{task_name}_test.csv"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("val"),
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir,  "val", f"{task_name}_val.csv"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("dev"),
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "dev", f"{task_name}_dev.csv"
+                    ),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        df = pd.read_csv(filepath,encoding="utf-8")
+        for i, instance in enumerate(df.to_dict(orient="records")):
+            if "answer" not in instance.keys():
+                instance["answer"]=""
+            if "explanation" not in instance.keys():
+                instance["explanation"]=""
+            yield i, instance