Convert dataset to Parquet (#6)

- Convert dataset to Parquet (167c717c7aed0d8300f27651433c35daff1440f2)
- Delete loading script (bc1432158a3d147e8bdda46a43cc84471ac2433d)
- Delete legacy dataset_infos.json (dccd52e142ef4ad27083cc78299b15ebccb8fd13)

README.md

@@ -1,5 +1,4 @@
 ---
-pretty_name: SQuAD2.0
 annotations_creators:
 - crowdsourced
 language_creators:
@@ -20,23 +19,9 @@ task_ids:
 - open-domain-qa
 - extractive-qa
 paperswithcode_id: squad
-train-eval-index:
-- config: squad_v2
-  task: question-answering
-  task_id: extractive_question_answering
-  splits:
-    train_split: train
-    eval_split: validation
-  col_mapping:
-    question: question
-    context: context
-    answers:
-      text: text
-      answer_start: answer_start
-  metrics:
-  - type: squad_v2
-    name: SQuAD v2
+pretty_name: SQuAD2.0
 dataset_info:
+  config_name: squad_v2
   features:
   - name: id
     dtype: string
@@ -52,16 +37,39 @@ dataset_info:
       dtype: string
     - name: answer_start
       dtype: int32
-  config_name: squad_v2
   splits:
   - name: train
-    num_bytes: 116699950
+    num_bytes: 116732025
     num_examples: 130319
   - name: validation
-    num_bytes: 11660302
+    num_bytes: 11661091
     num_examples: 11873
-  download_size: 46494161
-  dataset_size: 128360252
+  download_size: 17720493
+  dataset_size: 128393116
+configs:
+- config_name: squad_v2
+  data_files:
+  - split: train
+    path: squad_v2/train-*
+  - split: validation
+    path: squad_v2/validation-*
+  default: true
+train-eval-index:
+- config: squad_v2
+  task: question-answering
+  task_id: extractive_question_answering
+  splits:
+    train_split: train
+    eval_split: validation
+  col_mapping:
+    question: question
+    context: context
+    answers:
+      text: text
+      answer_start: answer_start
+  metrics:
+  - type: squad_v2
+    name: SQuAD v2
 ---
 
 # Dataset Card for "squad_v2"

dataset_infos.json

@@ -1 +0,0 @@
-{"squad_v2": {"description": "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\n to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but\n also determine when no answer is supported by the paragraph and abstain from answering.\n", "citation": "@article{2016arXiv160605250R,\n       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n                 Konstantin and {Liang}, Percy},\n        title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n      journal = {arXiv e-prints},\n         year = 2016,\n          eid = {arXiv:1606.05250},\n        pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n       eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "squad_v2", "config_name": "squad_v2", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 116699950, "num_examples": 130319, "dataset_name": "squad_v2"}, "validation": {"name": "validation", "num_bytes": 11660302, "num_examples": 11873, "dataset_name": "squad_v2"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json": {"num_bytes": 42123633, "checksum": "68dcfbb971bd3e96d5b46c7177b16c1a4e7d4bdef19fb204502738552dede002"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json": {"num_bytes": 4370528, "checksum": "80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8"}}, "download_size": 46494161, "post_processing_size": null, "dataset_size": 128360252, "size_in_bytes": 174854413}}

squad_v2.py

@@ -1,133 +0,0 @@
-"""TODO(squad_v2): Add a description here."""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-# TODO(squad_v2): BibTeX citation
-_CITATION = """\
-@article{2016arXiv160605250R,
-       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
-                 Konstantin and {Liang}, Percy},
-        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
-      journal = {arXiv e-prints},
-         year = 2016,
-          eid = {arXiv:1606.05250},
-        pages = {arXiv:1606.05250},
-archivePrefix = {arXiv},
-       eprint = {1606.05250},
-}
-"""
-
-_DESCRIPTION = """\
-combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
- to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
- also determine when no answer is supported by the paragraph and abstain from answering.
-"""
-
-_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
-_URLS = {
-    "train": _URL + "train-v2.0.json",
-    "dev": _URL + "dev-v2.0.json",
-}
-
-
-class SquadV2Config(datasets.BuilderConfig):
-    """BuilderConfig for SQUAD."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for SQUADV2.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(SquadV2Config, self).__init__(**kwargs)
-
-
-class SquadV2(datasets.GeneratorBasedBuilder):
-    """TODO(squad_v2): Short description of my dataset."""
-
-    # TODO(squad_v2): Set up version.
-    BUILDER_CONFIGS = [
-        SquadV2Config(name="squad_v2", version=datasets.Version("2.0.0"), description="SQuAD plaint text version 2"),
-    ]
-
-    def _info(self):
-        # TODO(squad_v2): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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="https://rajpurkar.github.io/SQuAD-explorer/",
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(squad_v2): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(squad_v2): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            squad = json.load(f)
-            for example in squad["data"]:
-                title = example.get("title", "")
-                for paragraph in example["paragraphs"]:
-                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
-                    for qa in paragraph["qas"]:
-                        question = qa["question"]
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"] for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }

squad_v2/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 16369982

squad_v2/validation-00000-of-00001.parquet

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+oid sha256:0560174ab095c5ac0a8c8dc8da05f1625453c45a77e4ce9cabc6947ddfdd24cb
+size 1350511