Convert dataset to Parquet

README.md

@@ -1,15 +1,14 @@
 ---
 annotations_creators:
 - crowdsourced
-language:
-- en
 language_creators:
 - found
+language:
+- en
 license:
 - cc-by-nc-sa-4.0
 multilinguality:
 - monolingual
-pretty_name: Question Answering for Artificial Intelligence (QuAIL)
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -19,7 +18,9 @@ task_categories:
 task_ids:
 - multiple-choice-qa
 paperswithcode_id: quail
+pretty_name: Question Answering for Artificial Intelligence (QuAIL)
 dataset_info:
+  config_name: quail
   features:
   - name: id
     dtype: string
@@ -47,19 +48,28 @@ dataset_info:
     sequence: string
   - name: correct_answer_id
     dtype: int32
-  config_name: quail
   splits:
   - name: train
-    num_bytes: 23432697
+    num_bytes: 23432601
     num_examples: 10246
   - name: validation
-    num_bytes: 4989579
+    num_bytes: 4989531
     num_examples: 2164
   - name: challenge
-    num_bytes: 1199840
+    num_bytes: 1199792
     num_examples: 556
-  download_size: 6402933
-  dataset_size: 29622116
+  download_size: 2286403
+  dataset_size: 29621924
+configs:
+- config_name: quail
+  data_files:
+  - split: train
+    path: quail/train-*
+  - split: validation
+    path: quail/validation-*
+  - split: challenge
+    path: quail/challenge-*
+  default: true
 ---
 
 # Dataset Card for "quail"

dataset_infos.json

@@ -1 +0,0 @@
-{"quail": {"description": "QuAIL is a  reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types.", "citation": "@inproceedings{DBLP:conf/aaai/RogersKDR20,\n  author    = {Anna Rogers and\n               Olga Kovaleva and\n               Matthew Downey and\n               Anna Rumshisky},\n  title     = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite\n               Real Tasks},\n  booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}\n               2020, The Thirty-Second Innovative Applications of Artificial Intelligence\n               Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational\n               Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,\n               February 7-12, 2020},\n  pages     = {8722--8731},\n  publisher = {{AAAI} Press},\n  year      = {2020},\n  url       = {https://aaai.org/ojs/index.php/AAAI/article/view/6398},\n  timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},\n  biburl    = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://text-machine-lab.github.io/blog/2020/quail/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context_id": {"dtype": "string", "id": null, "_type": "Value"}, "question_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "metadata": {"author": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "question_type": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "correct_answer_id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "quail", "config_name": "quail", "version": {"version_str": "1.3.0", "description": "", "major": 1, "minor": 3, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23432697, "num_examples": 10246, "dataset_name": "quail"}, "validation": {"name": "validation", "num_bytes": 4989579, "num_examples": 2164, "dataset_name": "quail"}, "challenge": {"name": "challenge", "num_bytes": 1199840, "num_examples": 556, "dataset_name": "quail"}}, "download_checksums": {"https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.3/xml/randomized/quail_1.3_train_randomized.xml": {"num_bytes": 5064067, "checksum": "faf7849a4397485fc6134919b9ce55e40ca623915be86bce16eccfb6c4186fac"}, "https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.3/xml/randomized/quail_1.3_dev_randomized.xml": {"num_bytes": 1075073, "checksum": "e39b848db1a13533ee264c9eaa21a309aebf3c0394967848acb89b83ae96b8c4"}, "https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.3/xml/randomized/quail_1.3_challenge_randomized.xml": {"num_bytes": 263793, "checksum": "1e140e6e6b9b820e70a75a79f8bc111db6472fd0027bbcfb760e6841045478ae"}}, "download_size": 6402933, "post_processing_size": null, "dataset_size": 29622116, "size_in_bytes": 36025049}}

quail.py

@@ -1,142 +0,0 @@
-import xml.etree.ElementTree as ET
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{DBLP:conf/aaai/RogersKDR20,
-  author    = {Anna Rogers and
-               Olga Kovaleva and
-               Matthew Downey and
-               Anna Rumshisky},
-  title     = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
-               Real Tasks},
-  booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
-               2020, The Thirty-Second Innovative Applications of Artificial Intelligence
-               Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
-               Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
-               February 7-12, 2020},
-  pages     = {8722--8731},
-  publisher = {{AAAI} Press},
-  year      = {2020},
-  url       = {https://aaai.org/ojs/index.php/AAAI/article/view/6398},
-  timestamp = {Thu, 04 Jun 2020 13:18:48 +0200},
-  biburl    = {https://dblp.org/rec/conf/aaai/RogersKDR20.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-"""
-
-_DESCRIPTION = """\
-QuAIL is a  reading comprehension dataset. \
-QuAIL contains 15K multi-choice questions in texts 300-350 tokens \
-long 4 domains (news, user stories, fiction, blogs).\
-QuAIL is balanced and annotated for question types.\
-"""
-
-
-class QuailConfig(datasets.BuilderConfig):
-    """BuilderConfig for QuAIL."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for QuAIL.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(QuailConfig, self).__init__(**kwargs)
-
-
-class Quail(datasets.GeneratorBasedBuilder):
-    """QuAIL: The Stanford Question Answering Dataset. Version 1.1."""
-
-    _CHALLENGE_SET = "https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.3/xml/randomized/quail_1.3_challenge_randomized.xml"
-    _DEV_SET = "https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.3/xml/randomized/quail_1.3_dev_randomized.xml"
-    _TRAIN_SET = "https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.3/xml/randomized/quail_1.3_train_randomized.xml"
-
-    BUILDER_CONFIGS = [
-        QuailConfig(
-            name="quail",
-            version=datasets.Version("1.3.0", ""),
-            description="Quail dataset 1.3.0",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "context_id": datasets.Value("string"),
-                    "question_id": datasets.Value("string"),
-                    "domain": datasets.Value("string"),
-                    "metadata": {
-                        "author": datasets.Value("string"),
-                        "title": datasets.Value("string"),
-                        "url": datasets.Value("string"),
-                    },
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "question_type": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        datasets.Value("string"),
-                    ),
-                    "correct_answer_id": datasets.Value("int32"),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage="https://text-machine-lab.github.io/blog/2020/quail/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = {"train": self._TRAIN_SET, "dev": self._DEV_SET, "challenge": self._CHALLENGE_SET}
-        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"]}),
-            datasets.SplitGenerator(name="challenge", gen_kwargs={"filepath": downloaded_files["challenge"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        root = ET.parse(filepath).getroot()
-        for text_tag in root.iterfind("text"):
-            text_id = text_tag.get("id")
-            domain = text_tag.get("domain")
-            metadata_tag = text_tag.find("metadata")
-            author = metadata_tag.find("author").text.strip()
-            title = metadata_tag.find("title").text.strip()
-            url = metadata_tag.find("url").text.strip()
-            text_body = text_tag.find("text_body").text.strip()
-            questions_tag = text_tag.find("questions")
-            for q_tag in questions_tag.iterfind("q"):
-                question_type = q_tag.get("type", None)
-                question_text = q_tag.text.strip()
-                question_id = q_tag.get("id")
-                answers = []
-                answer_id = None
-                for i, a_tag in enumerate(q_tag.iterfind("a")):
-                    if a_tag.get("correct") == "True":
-                        answer_id = i
-                    answers.append(a_tag.text.strip())
-
-                id_ = f"{text_id}_{question_id}"
-                yield id_, {
-                    "id": id_,
-                    "context_id": text_id,
-                    "question_id": question_id,
-                    "question_type": question_type,
-                    "domain": domain,
-                    "metadata": {"author": author, "title": title, "url": url},
-                    "context": text_body,
-                    "question": question_text,
-                    "answers": answers,
-                    "correct_answer_id": answer_id,
-                }

quail/challenge-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe47785617f8248c27318282891a55b4cebf8bd5f3fef13f1dbd107c2bfb5797
+size 96956

quail/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e760cd0359e3aa59444bc8e551864d4fa518e06987380dabd50eed49179091ee
+size 1791229

quail/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8ba2dd760c9c53a2642de0d8213ee66b6d252d5c279f0f0ac8e06602bf20bf1
+size 398218