Create EpiSet4BinaryClassification.py

EpiSet4BinaryClassification.py

@@ -0,0 +1,74 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""EpiClassify4GARD dataset."""
+
+
+import csv
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """\
+INSERT DESCRIPTION
+"""
+_CITATION = """\
+John JN, Sid E, Zhu Q. Recurrent Neural Networks to Automatically Identify Rare Disease Epidemiologic Studies from PubMed. AMIA Jt Summits Transl Sci Proc. 2021 May 17;2021:325-334. PMID: 34457147; PMCID: PMC8378621.
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/train.tsv"
+_VAL_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/val.tsv"
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/ncats/epi4GARD/master/dataset/test.tsv"
+
+
+class EpiClassify4GARD(datasets.GeneratorBasedBuilder):
+    """EpiClassify4GARD text classification dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "abstract": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["1 = IsEpi", "0 = IsNotEpi"]),
+                }
+            ),
+            homepage="https://github.com/ncats/epi4GARD/tree/master/Epi4GARD#epi4gard",
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="abstract", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        val_path = dl_manager.download_and_extract(_VAL_DOWNLOAD_URL)
+        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path }),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                abstract = row[0]
+                label = row[1]
+                yield id_, {"abstract": abstract, "label": int(label)}