upload hubscripts/mediqa_rqe_hub.py to hub from bigbio repo

mediqa_rqe.py

@@ -0,0 +1,155 @@
+# coding=utf-8
+# Copyright 2022 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 glob
+import json
+import os
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Iterator, Tuple
+from xml.etree import ElementTree as ET
+
+import datasets
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@inproceedings{MEDIQA2019,
+  author    = {Asma {Ben Abacha} and Chaitanya Shivade and Dina Demner{-}Fushman},
+  title     = {Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering},
+  booktitle = {ACL-BioNLP 2019},
+  year      = {2019}
+}
+"""
+
+_DATASETNAME = "mediqa_rqe"
+_DISPLAYNAME = "MEDIQA RQE"
+
+_DESCRIPTION = """\
+The MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA).
+Mailing List: https://groups.google.com/forum/#!forum/bionlp-mediqa
+
+The objective of the RQE task is to identify entailment between two questions in the context of QA. We use the following definition of question entailment: “a question A entails a question B if every answer to B is also a complete or partial answer to A” [1]
+    [1] A. Ben Abacha & D. Demner-Fushman. “Recognizing Question Entailment for Medical Question Answering”. AMIA 2016.
+"""
+
+_HOMEPAGE = "https://sites.google.com/view/mediqa2019"
+_LICENSE = 'License information unavailable'
+_URLS = {
+    _DATASETNAME: "https://github.com/abachaa/MEDIQA2019/archive/refs/heads/master.zip"
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_PAIRS_CLASSIFICATION]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MediqaRQEDataset(datasets.GeneratorBasedBuilder):
+    """MediqaRQE Dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        # Source Schema
+        BigBioConfig(
+            name="mediqa_rqe_source",
+            version=SOURCE_VERSION,
+            description="MEDIQA RQE source schema",
+            schema="source",
+            subset_id="mediqa_rqe_source",
+        ),
+        # BigBio Schema
+        BigBioConfig(
+            name="mediqa_rqe_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="MEDIQA RQE BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="mediqa_rqe_bigbio_pairs",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mediqa_rqe_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pid": datasets.Value("string"),
+                    "value": datasets.Value("string"),
+                    "chq": datasets.Value("string"),
+                    "faq": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "bigbio_pairs":
+            features = pairs_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir
+                    / "MEDIQA2019-master/MEDIQA_Task2_RQE/MEDIQA2019-Task2-RQE-TrainingSet-AMIA2016.xml"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir
+                    / "MEDIQA2019-master/MEDIQA_Task2_RQE/MEDIQA2019-Task2-RQE-ValidationSet-AMIA2016.xml"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir
+                    / "MEDIQA2019-master/MEDIQA_Task2_RQE/MEDIQA2019-Task2-RQE-TestSet-wLabels.xml"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Iterator[Tuple[str, Dict]]:
+        dom = ET.parse(filepath).getroot()
+        for row in dom.iterfind("pair"):
+            pid = row.attrib["pid"]
+            value = row.attrib["value"]
+            chq = row.find("chq").text.strip()
+            faq = row.find("faq").text.strip()
+
+            if self.config.schema == "source":
+                yield pid, {"pid": pid, "value": value, "chq": chq, "faq": faq}
+            elif self.config.schema == "bigbio_pairs":
+                yield pid, {
+                    "id": pid,
+                    "document_id": pid,
+                    "text_1": chq,
+                    "text_2": faq,
+                    "label": value,
+                }