Convert dataset to Parquet

README.md

@@ -26,6 +26,7 @@ task_ids:
 paperswithcode_id: mrqa-2019
 pretty_name: MRQA 2019
 dataset_info:
+  config_name: plain_text
   features:
   - name: subset
     dtype: string
@@ -65,19 +66,28 @@ dataset_info:
         dtype: int32
   - name: answers
     sequence: string
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 4090681873
+    num_bytes: 4090677713
     num_examples: 516819
-  - name: test
-    num_bytes: 57712177
-    num_examples: 9633
   - name: validation
-    num_bytes: 484107026
+    num_bytes: 484106546
     num_examples: 58221
-  download_size: 1479518355
-  dataset_size: 4632501076
+  - name: test
+    num_bytes: 57712097
+    num_examples: 9633
+  download_size: 1679161250
+  dataset_size: 4632496356
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  - split: validation
+    path: plain_text/validation-*
+  - split: test
+    path: plain_text/test-*
+  default: true
 ---
 
 # Dataset Card for MRQA 2019

mrqa.py

@@ -1,196 +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.
-"""MRQA 2019 Shared task dataset."""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{fisch2019mrqa,
-    title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
-    author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
-    booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP},
-    year={2019},
-}
-"""
-
-_DESCRIPTION = """\
-The MRQA 2019 Shared Task focuses on generalization in question answering.
-An effective question answering system should do more than merely
-interpolate from the training set to answer test examples drawn
-from the same distribution: it should also be able to extrapolate
-to out-of-distribution examples — a significantly harder challenge.
-
-The dataset is a collection of 18 existing QA dataset (carefully selected
-subset of them) and converted to the same format (SQuAD format). Among
-these 18 datasets, six datasets were made available for training,
-six datasets were made available for development, and the final six
-for testing. The dataset is released as part of the MRQA 2019 Shared Task.
-"""
-
-_HOMEPAGE = "https://mrqa.github.io/2019/shared.html"
-
-_LICENSE = "Unknwon"
-
-_URLs = {
-    # Train sub-datasets
-    "train+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SQuAD.jsonl.gz",
-    "train+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NewsQA.jsonl.gz",
-    "train+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/TriviaQA-web.jsonl.gz",
-    "train+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/SearchQA.jsonl.gz",
-    "train+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/HotpotQA.jsonl.gz",
-    "train+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/train/NaturalQuestionsShort.jsonl.gz",
-    # Validation sub-datasets
-    "validation+SQuAD": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SQuAD.jsonl.gz",
-    "validation+NewsQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NewsQA.jsonl.gz",
-    "validation+TriviaQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TriviaQA-web.jsonl.gz",
-    "validation+SearchQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/SearchQA.jsonl.gz",
-    "validation+HotpotQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/HotpotQA.jsonl.gz",
-    "validation+NaturalQuestions": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/NaturalQuestionsShort.jsonl.gz",
-    # Test sub-datasets
-    "test+BioASQ": "http://participants-area.bioasq.org/MRQA2019/",  # BioASQ.jsonl.gz
-    "test+DROP": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DROP.jsonl.gz",
-    "test+DuoRC": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/DuoRC.ParaphraseRC.jsonl.gz",
-    "test+RACE": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RACE.jsonl.gz",
-    "test+RelationExtraction": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/RelationExtraction.jsonl.gz",
-    "test+TextbookQA": "https://s3.us-east-2.amazonaws.com/mrqa/release/v2/dev/TextbookQA.jsonl.gz",
-}
-
-
-class Mrqa(datasets.GeneratorBasedBuilder):
-    """MRQA 2019 Shared task dataset."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="plain_text", description="Plain text", version=VERSION),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            # Format is derived from https://github.com/mrqa/MRQA-Shared-Task-2019#mrqa-format
-            features=datasets.Features(
-                {
-                    "subset": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "context_tokens": datasets.Sequence(
-                        {
-                            "tokens": datasets.Value("string"),
-                            "offsets": datasets.Value("int32"),
-                        }
-                    ),
-                    "qid": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "question_tokens": datasets.Sequence(
-                        {
-                            "tokens": datasets.Value("string"),
-                            "offsets": datasets.Value("int32"),
-                        }
-                    ),
-                    "detected_answers": datasets.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "char_spans": datasets.Sequence(
-                                {
-                                    "start": datasets.Value("int32"),
-                                    "end": datasets.Value("int32"),
-                                }
-                            ),
-                            "token_spans": datasets.Sequence(
-                                {
-                                    "start": datasets.Value("int32"),
-                                    "end": datasets.Value("int32"),
-                                }
-                            ),
-                        }
-                    ),
-                    "answers": datasets.Sequence(datasets.Value("string")),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URLs)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths_dict": data_dir,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths_dict": data_dir,
-                    "split": "test",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepaths_dict": data_dir,
-                    "split": "validation",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepaths_dict, split):
-        """Yields examples."""
-        for source, filepath in filepaths_dict.items():
-            if split not in source:
-                continue
-            with open(filepath, encoding="utf-8") as f:
-                header = next(f)
-                subset = json.loads(header)["header"]["dataset"]
-
-                for row in f:
-                    paragraph = json.loads(row)
-                    context = paragraph["context"].strip()
-                    context_tokens = [{"tokens": t[0], "offsets": t[1]} for t in paragraph["context_tokens"]]
-                    for qa in paragraph["qas"]:
-                        qid = qa["qid"]
-                        question = qa["question"].strip()
-                        question_tokens = [{"tokens": t[0], "offsets": t[1]} for t in qa["question_tokens"]]
-                        detected_answers = []
-                        for detect_ans in qa["detected_answers"]:
-                            detected_answers.append(
-                                {
-                                    "text": detect_ans["text"].strip(),
-                                    "char_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["char_spans"]],
-                                    "token_spans": [{"start": t[0], "end": t[1]} for t in detect_ans["token_spans"]],
-                                }
-                            )
-                        answers = qa["answers"]
-                        yield f"{source}_{qid}", {
-                            "subset": subset,
-                            "context": context,
-                            "context_tokens": context_tokens,
-                            "qid": qid,
-                            "question": question,
-                            "question_tokens": question_tokens,
-                            "detected_answers": detected_answers,
-                            "answers": answers,
-                        }

plain_text/test-00000-of-00001.parquet

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