Convert dataset to Parquet

README.md

@@ -34,10 +34,15 @@ dataset_info:
           '1': 1
   splits:
   - name: train
-    num_bytes: 701650
+    num_bytes: 701642
     num_examples: 3048
-  download_size: 665688
-  dataset_size: 701650
+  download_size: 313704
+  dataset_size: 701642
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for [medical_questions_pairs]

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fdd6d8ac7bc25f0a4c6ae1b324dd3e730b78965b14abd830f6cb1869b27594f
+size 313704

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.\n", "citation": "", "homepage": "https://github.com/curai/medical-question-pair-dataset", "license": "", "features": {"dr_id": {"dtype": "int32", "id": null, "_type": "Value"}, "question_1": {"dtype": "string", "id": null, "_type": "Value"}, "question_2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": [0, 1], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "medical_questions_pairs", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 701650, "num_examples": 3048, "dataset_name": "medical_questions_pairs"}}, "download_checksums": {"https://raw.githubusercontent.com/curai/medical-question-pair-dataset/master/mqp.csv": {"num_bytes": 665688, "checksum": "94ecd609a9ca9350e1cff2438aa55a034762f01c1a68732aaae3e4be7b03cf57"}}, "download_size": 665688, "post_processing_size": null, "dataset_size": 701650, "size_in_bytes": 1367338}}

medical_questions_pairs.py

@@ -1,83 +0,0 @@
-# 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 (MQP) Dataset"""
-
-
-import csv
-
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@misc{mccreery2020effective,
-      title={Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
-      author={Clara H. McCreery and Namit Katariya and Anitha Kannan and Manish Chablani and Xavier Amatriain},
-      year={2020},
-      eprint={2008.13546},
-      archivePrefix={arXiv},
-      primaryClass={cs.IR}
-}
-"""
-
-
-_DESCRIPTION = """\
-This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors.
-"""
-
-_HOMEPAGE = "https://github.com/curai/medical-question-pair-dataset"
-
-_LICENSE = ""
-
-
-_URL = "https://raw.githubusercontent.com/curai/medical-question-pair-dataset/master/mqp.csv"
-
-
-class MedicalQuestionsPairs(datasets.GeneratorBasedBuilder):
-    """Medical Question Pairs (MQP) Dataset"""
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "dr_id": datasets.Value("int32"),
-                "question_1": datasets.Value("string"),
-                "question_2": datasets.Value("string"),
-                "label": datasets.features.ClassLabel(num_classes=2, names=[0, 1]),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_file = dl_manager.download_and_extract(_URL)
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file})]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            data = csv.reader(f)
-            for id_, row in enumerate(data):
-                yield id_, {
-                    "dr_id": row[0],
-                    "question_1": row[1],
-                    "question_2": row[2],
-                    "label": row[3],
-                }