Convert dataset to Parquet (#4)

- Convert dataset to Parquet (14b7c9894d4841cdea2b75852c3aa62156dccb77)
- Delete loading script (5a76f3049b63dc5392c75e6cd49fc77926466f68)

README.md

@@ -48,10 +48,15 @@ dataset_info:
     sequence: int32
   splits:
   - name: train
-    num_bytes: 1423090
+    num_bytes: 1423066
     num_examples: 1458
-  download_size: 118173
-  dataset_size: 1423090
+  download_size: 218736
+  dataset_size: 1423066
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for OneStopQA

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe10e4a2bd7488614df3d2084436b2050e6b8bed8ff23aed53da1da785baa48e
+size 218736

onestop_qa.py

@@ -1,166 +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.
-"""OneStopQA - a multiple choice reading comprehension dataset annotated
-according to the STARC (Structured Annotations for Reading Comprehension) scheme"""
-
-
-import json
-import os
-
-import datasets
-
-
-# from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@inproceedings{starc2020,
-      author    = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},
-      title     = {STARC: Structured Annotations for Reading Comprehension},
-      booktitle = {ACL},
-      year      = {2020},
-      publisher = {Association for Computational Linguistics}
-      }
-"""
-
-_DESCRIPTION = """\
-OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC \
-(Structured Annotations for Reading Comprehension) scheme. \
-The reading materials are Guardian articles taken from the \
-[OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). \
-Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. \
-Each paragraph is annotated with three multiple choice reading comprehension questions. \
-The reading comprehension questions can be answered based on any of the three paragraph levels.
-"""
-
-_HOMEPAGE = "https://github.com/berzak/onestop-qa"
-
-_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"
-
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "https://github.com/berzak/onestop-qa/raw/master/annotations/onestop_qa.zip"
-
-
-class OneStopQA(datasets.GeneratorBasedBuilder):
-    """OneStopQA - a multiple choice reading comprehension dataset annotated
-    according to the STARC (Structured Annotations for Reading Comprehension) scheme"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "title": datasets.Value("string"),
-                "paragraph": datasets.Value("string"),
-                "level": datasets.ClassLabel(names=["Adv", "Int", "Ele"]),
-                "question": datasets.Value("string"),
-                "paragraph_index": datasets.Value("int32"),
-                "answers": datasets.features.Sequence(datasets.Value("string"), length=4),
-                "a_span": datasets.features.Sequence(datasets.Value("int32")),
-                "d_span": datasets.features.Sequence(datasets.Value("int32")),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-            task_templates=[]
-            #     QuestionAnsweringExtractive(
-            #         question_column="question", context_column="context", answers_column="answers"
-            #     )
-            # ], # When issue #2434 is resolved uncomment task_templates and the QuestionAnsweringExtractive (or similar)
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "onestop_qa.json"),
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    ):
-        """Yields examples as (key, example) tuples."""
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is here for legacy reason (tfds) and is not important in itself.
-        # Based on the squad dataset
-        logger.info("generating examples from = %s", filepath)
-        key = 0
-        with open(filepath, encoding="utf-8") as f:
-            onestop_qa = json.load(f)
-            for article in onestop_qa["data"]:
-                title = article.get("title", "")
-                for paragraph_index, paragraph in enumerate(article["paragraphs"]):
-                    for level in ["Adv", "Int", "Ele"]:
-                        paragraph_context_and_spans = paragraph[level]
-                        paragraph_context = paragraph_context_and_spans["context"]
-                        a_spans = paragraph_context_and_spans["a_spans"]
-                        d_spans = paragraph_context_and_spans["d_spans"]
-                        qas = paragraph["qas"]
-                        for qa, a_span, d_span in zip(qas, a_spans, d_spans):
-                            yield key, {
-                                "title": title,
-                                "paragraph": paragraph_context,
-                                "question": qa["question"],
-                                "paragraph_index": paragraph_index,
-                                "answers": qa["answers"],
-                                "level": level,
-                                "a_span": a_span,
-                                "d_span": d_span,
-                            },
-
-                            key += 1