upload hubscripts/mqp_hub.py to hub from bigbio repo

mqp.py

@@ -0,0 +1,171 @@
+# coding=utf-8
+# 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.
+"""
+Medical Question Pairs dataset by McCreery et al (2020) contains pairs of medical questions and paraphrased versions of
+the question prepared by medical professional.
+"""
+
+import csv
+import os
+from typing import Dict, Tuple
+
+import datasets
+from datasets import load_dataset
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@article{DBLP:journals/biodb/LiSJSWLDMWL16,
+  author    = {Krallinger, M., Rabal, O., Lourenço, A.},
+  title     = {Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
+  journal   = {KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
+  volume    = {3458–3465},
+  year      = {2020},
+  url       = {https://github.com/curai/medical-question-pair-dataset},
+  doi       = {},
+  biburl    = {},
+  bibsource = {}
+}
+"""
+
+_DATASETNAME = "mqp"
+_DISPLAYNAME = "MQP"
+
+_DESCRIPTION = """\
+Medical Question Pairs dataset by McCreery et al (2020) contains pairs of medical questions and paraphrased versions of 
+the question prepared by medical professional. Paraphrased versions were labelled as similar (syntactically dissimilar 
+but contextually similar ) or dissimilar (syntactically may look similar but contextually dissimilar). Labels 1: similar, 0: dissimilar
+"""
+
+_HOMEPAGE = "https://github.com/curai/medical-question-pair-dataset"
+
+_LICENSE = 'License information unavailable'
+_URLs = {
+    _DATASETNAME: "https://raw.githubusercontent.com/curai/medical-question-pair-dataset/master/mqp.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MQPDataset(datasets.GeneratorBasedBuilder):
+    """Medical Question Pairing dataset"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="mqp_source",
+            version=SOURCE_VERSION,
+            description="MQP source schema",
+            schema="source",
+            subset_id="mqp",
+        ),
+        BigBioConfig(
+            name="mqp_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="MQP BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="mqp",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mqp_source"
+
+    def _info(self):
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text_1": datasets.Value("string"),
+                    "text_2": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+
+        # Using in pairs schema
+        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):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(my_urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples as (key, example) tuples."""
+
+        if split == "train":  # There's only training dataset available atm
+            with open(filepath, encoding="utf-8") as csv_file:
+                csv_reader = csv.reader(
+                    csv_file,
+                    quotechar='"',
+                    delimiter=",",
+                    quoting=csv.QUOTE_ALL,
+                    skipinitialspace=True,
+                )
+
+                if self.config.schema == "source":
+                    for id_, row in enumerate(csv_reader):
+                        document_id, text_1, text_2, label = row
+                        yield id_, {
+                            "document_id": document_id,
+                            "text_1": text_1,
+                            "text_2": text_2,
+                            "label": label,
+                        }
+
+                elif self.config.schema == "bigbio_pairs":
+                    # global id (uid) starts from 1
+                    uid = 0
+                    for id_, row in enumerate(csv_reader):
+                        uid += 1
+                        document_id, text_1, text_2, label = row
+                        yield id_, {
+                            "id": uid,  # uid is an unique identifier for every record that starts from 1
+                            "document_id": document_id,
+                            "text_1": text_1,
+                            "text_2": text_2,
+                            "label": label,
+                        }
+        else:
+            print("There's no test/val split available for the given dataset")
+            return