Convert dataset to Parquet (#1)

- Convert dataset to Parquet (2afbe3fbb5e25a3e9e667d43898d4a9cfe5ecbb4)
- Delete loading script (cbc69054432fb4f39d883e00946f6455080fe8c2)
- Delete data file (89dcb00f5b62c083aef90cdac77168840924d13a)
- Delete data file (8cfc5e4d61867931190908b9acf1419ce2ce1508)
- Delete data file (b467dd94d77320c09bc082a8e12a1ce467270874)
- Delete data file (149c2e898f90c5c0f73cadac97e49ef5c20e83b5)
- Delete data file (042802ce8e5d289cbfa4810ebe536902668c42d4)
- Delete data file (b938183677d3bf6e95c45312a4029a5e1d2c0cc1)

README.md

@@ -0,0 +1,29 @@
+---
+dataset_info:
+  features:
+  - name: query
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 13113502
+    num_examples: 3568
+  - name: validation
+    num_bytes: 1478861
+    num_examples: 411
+  - name: test
+    num_bytes: 2197300
+    num_examples: 480
+  download_size: 8317119
+  dataset_size: 16789663
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
+---

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d4361d36cd7c3d9da49a05c10dc8c37c3cf032c6e4346411481aa534a9a128b
+size 1087845

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b6f1b346b4ff10486e1afae9a000dacd799dd3a533baddbae79188fd70ed65d
+size 6484808

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:565daf7faec78e1e724d33b64369fc50a20204cf976724b89bb4957499fa99db
+size 744466

med_paragraph_simplification.py

@@ -1,156 +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.
-"""Covid Dialog dataset in English and Chinese"""
-
-
-import copy
-import os
-import re
-import textwrap
-
-import datasets
-
-
-# BibTeX citation
-_CITATION = """\
-    @inproceedings{devaraj-etal-2021-paragraph,
-        title = "Paragraph-level Simplification of Medical Texts",
-        author = "Devaraj, Ashwin and Marshall, Iain and Wallace, Byron and Li, Junyi Jessy",
-        booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for
-                     Computational Linguistics",
-        month = jun,
-        year = "2021",
-        publisher = "Association for Computational Linguistics",
-        url = "https://www.aclweb.org/anthology/2021.naacl-main.395",
-        pages = "4972--4984",
-
-"""
-
-# Official description of the dataset
-_DESCRIPTION = textwrap.dedent(
-    """
-    "Paragraph-level Simplification of Medical Texts" (Devaraj et al.) studies the problem of learning to simplify
-    medical texts. One of their contributions is a new corpus that is composed of technical abstracts and their
-    lay summaries on various clinical topics.
-
-    The author generated train/val/test splits, which are available in the GitHub repository linked in the paper.
-
-    The following is an example from the dataset:
-
-    {
-        "doi": "10.1002/14651858.CD011112.pub2",
-        "abstract": "We included six studies (reported as seven papers) involving 326 participants whose ages ranged
-        from 39 to 83 years, with a gender bias towards men (73% to 95% across studies), reflecting the characteristics
-        of patients with HNC. The risk of bias in the studies was generally high. We did not pool data from studies
-        because of significant differences in the interventions and outcomes evaluated. We found a lack of
-        standardisation and consistency in the outcomes measured and the endpoints at which they were evaluated.
-        We found no evidence that therapeutic exercises were better than TAU, or any other treatment, in improving the
-        safety and efficiency of oral swallowing (our primary outcome) or in improving any of the secondary outcomes.
-        Using the GRADE system, we classified the overall quality of the evidence for each outcome as very low, due to
-        the limited number of trials and their low quality. There were no adverse events reported that were directly
-        attributable to the intervention (swallowing exercises). We found no evidence that undertaking therapeutic
-        exercises before, during and/or immediately after HNC treatment leads to improvement in oral swallowing. This
-        absence of evidence may be due to the small participant numbers in trials, resulting in insufficient power to
-        detect any difference. Data from the identified trials could not be combined due to differences in the choice
-        of primary outcomes and in the measurement tools used to assess them, and the differing baseline and endpoints
-        across studies. Designing and implementing studies with stronger methodological rigour is essential. There needs
-        to be agreement about the key primary outcomes, the choice of validated assessment tools to measure them and the
-        time points at which those measurements are made.",
-        "pls": "We included six studies with 326 participants who undertook therapeutic exercises before, during and/or
-        after HNC treatment. We could not combine the results of the studies because of the variation in participants'
-        cancers, their treatments, the outcomes measured and the tools used to assess them, as well as the differing
-        time points for testing. Researchers have compared: (i) therapeutic exercises versus treatment as usual (TAU);
-        (ii) therapeutic exercises versus sham therapy; (iii) therapeutic exercises plus TAU versus TAU. The therapeutic
-        exercises varied in their design, timing and intensity. TAU involved managing patients' dysphagia when it
-        occurred, including inserting a tube for non-oral feeding. The evidence is up to date to 1 July 2016. We found
-        no evidence that therapeutic exercises were better than TAU, or any other treatment, in improving the safety and
-        efficiency of oral swallowing (our primary outcome) or in improving any of the secondary outcomes. However,
-        there is insufficient evidence to draw any clear conclusion about the effects of undertaking therapeutic
-        exercises before during and/or immediately after HNC treatment on preventing or reducing dysphagia. Studies had
-        small participant numbers, used complex interventions and varied in the choice of outcomes measured, making it
-        difficult to draw reliable conclusions. There were no reported adverse events directly attributable to the
-        intervention (swallowing exercises). The current quality of the evidence to support the use of therapeutic
-        exercises before, during and/or immediately after HNC treatment to prevent/reduce dysphagia is very low. We need
-        better designed, rigorous studies with larger participant numbers and agreed endpoints and outcome measurements
-        in order to draw clear(er) conclusions."
-    },
-
-    where "pls" stands for "plain-language summary".
-
-    Paper: http://arxiv.org/abs/2104.05767
-    Code: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts
-    """
-)
-
-# Link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts"
-
-_LICENSE = ""
-
-
-import datasets
-import os
-import json
-
-
-class Builder(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="default", version=datasets.Version("1.0.0"), description=_DESCRIPTION)]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "query": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=f"Covid Dialogue dataset, as preprocessed and shuffled in HELM",
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        test_target = dl_manager.download("test.source")
-        test_source = dl_manager.download("test.source")
-        train_source = dl_manager.download("train.source")
-        train_target = dl_manager.download("train.target")
-        val_source = dl_manager.download("valid.source")
-        val_target = dl_manager.download("valid.target")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"target": train_target, "source": train_source},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"target": val_target, "source": val_source},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"target": test_target, "source": test_source},
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, source, target):
-        with open(source, encoding="utf-8") as f_source:
-            with open(target, encoding="utf-8") as f_target:
-                for idx, (s, t) in enumerate(zip(f_source, f_target)):
-                    yield idx, {"query": s.rstrip(), "answer": t.rstrip()}