Update files from the datasets library (from 1.1.3)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.1.3

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"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}}

dummy/quail/1.3.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b26679db259bfe7c4b2fd9922fe898384934d5ac74720b16c58fdc70872f26c
+size 13604

quail.py

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+import logging
+import xml.etree.ElementTree as ET
+
+import datasets
+
+
+_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."""
+        logging.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,
+                }