Update files from the datasets library (from 1.0.2)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.2

.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
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dataset_infos.json

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+{"age_classification": {"description": "MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.\nMATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question \ndescriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, \nquestion answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to \ninspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the \nmerits held by MATINF.\n", "citation": "@inproceedings{xu-etal-2020-matinf,\n    title = \"{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization\",\n    author = \"Xu, Canwen  and\n      Pei, Jiaxin  and\n      Wu, Hongtao  and\n      Liu, Yiyu  and\n      Li, Chenliang\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.330\",\n    pages = \"3586--3596\",\n}\n\n", "homepage": "https://github.com/WHUIR/MATINF", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "description": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["0-1\u5c81", "1-2\u5c81", "2-3\u5c81"], "names_file": null, "id": null, "_type": "ClassLabel"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "matinf", "config_name": "age_classification", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33901977, "num_examples": 134852, "dataset_name": "matinf"}, "test": {"name": "test", "num_bytes": 9616194, "num_examples": 38318, "dataset_name": "matinf"}, "validation": {"name": "validation", "num_bytes": 4869685, "num_examples": 19323, "dataset_name": "matinf"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 48387856, "size_in_bytes": 48387856}, "topic_classification": {"description": "MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.\nMATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question \ndescriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, \nquestion answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to \ninspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the \nmerits held by MATINF.\n", "citation": "@inproceedings{xu-etal-2020-matinf,\n    title = \"{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization\",\n    author = \"Xu, Canwen  and\n      Pei, Jiaxin  and\n      Wu, Hongtao  and\n      Liu, Yiyu  and\n      Li, Chenliang\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.330\",\n    pages = \"3586--3596\",\n}\n\n", "homepage": "https://github.com/WHUIR/MATINF", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "description": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 18, "names": ["\u4ea7\u8925\u671f\u4fdd\u5065", "\u513f\u7ae5\u8fc7\u654f", "\u52a8\u4f5c\u53d1\u80b2", "\u5a74\u5e7c\u4fdd\u5065", "\u5a74\u5e7c\u5fc3\u7406", "\u5a74\u5e7c\u65e9\u6559", "\u5a74\u5e7c\u671f\u5582\u517b", "\u5a74\u5e7c\u8425\u517b", "\u5b55\u671f\u4fdd\u5065", "\u5bb6\u5ead\u6559\u80b2", "\u5e7c\u513f\u56ed", "\u672a\u51c6\u7236\u6bcd", "\u6d41\u4ea7\u548c\u4e0d\u5b55", "\u75ab\u82d7\u63a5\u79cd", "\u76ae\u80a4\u62a4\u7406", "\u5b9d\u5b9d\u4e0a\u706b", "\u8179\u6cfb", "\u5a74\u5e7c\u5e38\u89c1\u75c5"], "names_file": null, "id": null, "_type": "ClassLabel"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "matinf", "config_name": "topic_classification", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 153326538, "num_examples": 613036, "dataset_name": "matinf"}, "test": {"name": "test", "num_bytes": 43877443, "num_examples": 175363, "dataset_name": "matinf"}, "validation": {"name": "validation", "num_bytes": 21834951, "num_examples": 87519, "dataset_name": "matinf"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 219038932, "size_in_bytes": 219038932}, "summarization": {"description": "MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.\nMATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question \ndescriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, \nquestion answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to \ninspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the \nmerits held by MATINF.\n", "citation": "@inproceedings{xu-etal-2020-matinf,\n    title = \"{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization\",\n    author = \"Xu, Canwen  and\n      Pei, Jiaxin  and\n      Wu, Hongtao  and\n      Liu, Yiyu  and\n      Li, Chenliang\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.330\",\n    pages = \"3586--3596\",\n}\n\n", "homepage": "https://github.com/WHUIR/MATINF", "license": "", "features": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "matinf", "config_name": "summarization", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 181245403, "num_examples": 747888, "dataset_name": "matinf"}, "test": {"name": "test", "num_bytes": 51784189, "num_examples": 213681, "dataset_name": "matinf"}, "validation": {"name": "validation", "num_bytes": 25849900, "num_examples": 106842, "dataset_name": "matinf"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 258879492, "size_in_bytes": 258879492}, "qa": {"description": "MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.\nMATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question \ndescriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification, \nquestion answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to \ninspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the \nmerits held by MATINF.\n", "citation": "@inproceedings{xu-etal-2020-matinf,\n    title = \"{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization\",\n    author = \"Xu, Canwen  and\n      Pei, Jiaxin  and\n      Wu, Hongtao  and\n      Liu, Yiyu  and\n      Li, Chenliang\",\n    booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.acl-main.330\",\n    pages = \"3586--3596\",\n}\n\n", "homepage": "https://github.com/WHUIR/MATINF", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "matinf", "config_name": "qa", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 188047511, "num_examples": 747888, "dataset_name": "matinf"}, "test": {"name": "test", "num_bytes": 53708532, "num_examples": 213681, "dataset_name": "matinf"}, "validation": {"name": "validation", "num_bytes": 26931809, "num_examples": 106842, "dataset_name": "matinf"}}, "download_checksums": {}, "download_size": 0, "post_processing_size": null, "dataset_size": 268687852, "size_in_bytes": 268687852}}

dummy/age_classification/1.0.0/dummy_data.zip

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dummy/qa/1.0.0/dummy_data.zip

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dummy/summarization/1.0.0/dummy_data.zip

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dummy/topic_classification/1.0.0/dummy_data.zip

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matinf.py

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+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import six
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{xu-etal-2020-matinf,
+    title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
+    author = "Xu, Canwen  and
+      Pei, Jiaxin  and
+      Wu, Hongtao  and
+      Liu, Yiyu  and
+      Li, Chenliang",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.acl-main.330",
+    pages = "3586--3596",
+}
+
+"""
+
+_DESCRIPTION = """\
+MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.
+ MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question
+ descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification,
+ question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to
+ inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the
+ merits held by MATINF.
+"""
+
+
+class MatinfConfig(datasets.BuilderConfig):
+    """BuilderConfig for MATINF."""
+
+    def __init__(
+        self,
+        text_features,
+        label_column,
+        label_classes=None,
+        **kwargs,
+    ):
+        """BuilderConfig for MATINF.
+
+        Args:
+          text_features: `dict[string, string]`, map from the name of the feature
+            dict for each text field to the name of the column in the tsv file
+          label_column: `string`, name of the column in the tsv file corresponding
+            to the label
+          label_classes: `list[string]`, the list of classes if the label is
+            categorical. If not provided, then the label will be of type
+            `datasets.Value('float32')`.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(MatinfConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.text_features = text_features
+        self.label_column = label_column
+        self.label_classes = label_classes
+
+
+class Matinf(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        MatinfConfig(
+            name="age_classification",
+            text_features=["question", "description"],
+            label_column="class",
+            label_classes=["0-1岁", "1-2岁", "2-3岁"],
+        ),
+        MatinfConfig(
+            name="topic_classification",
+            text_features=["question", "description"],
+            label_column="class",
+            label_classes=[
+                "产褥期保健",
+                "儿童过敏",
+                "动作发育",
+                "婴幼保健",
+                "婴幼心理",
+                "婴幼早教",
+                "婴幼期喂养",
+                "婴幼营养",
+                "孕期保健",
+                "家庭教育",
+                "幼儿园",
+                "未准父母",
+                "流产和不孕",
+                "疫苗接种",
+                "皮肤护理",
+                "宝宝上火",
+                "腹泻",
+                "婴幼常见病",
+            ],
+        ),
+        MatinfConfig(
+            name="summarization",
+            text_features=["description", "question"],
+            label_column=None,
+        ),
+        MatinfConfig(
+            name="qa",
+            text_features=["question", "answer"],
+            label_column=None,
+        ),
+    ]
+
+    @property
+    def manual_download_instructions(self):
+        return (
+            "To use MATINF you have to download it manually. Please fill this google form ("
+            "https://forms.gle/nkH4LVE4iNQeDzsc9). You will receive a download link and a password once you "
+            "complete the form. Please extract all files in one folder and load the dataset with: "
+            "`datasets.load_dataset('matinf', data_dir='path/to/folder/folder_name')`"
+        )
+
+    def _info(self):
+        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
+        if self.config.label_classes:
+            features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
+        features["id"] = datasets.Value("int32")
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            homepage="https://github.com/WHUIR/MATINF",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('matinf', data_dir=...)` that includes files unzipped from the MATINF zip. Manual download instructions: {}".format(
+                    data_dir, self.manual_download_instructions
+                )
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(data_dir, "dev.csv")},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        label_classes = self.config.label_classes
+
+        with open(filepath, encoding="utf8") as f:
+            reader = csv.DictReader(f)
+
+            for n, row in enumerate(reader):
+                example = {feat: row[feat] for feat in self.config.text_features}
+                example["id"] = row["id"]
+
+                if self.config.label_column:
+                    label = row[self.config.label_column]
+                    if label_classes and label not in label_classes:
+                        continue  # Split age/topic classification
+                    example["label"] = label
+
+                # Filter out corrupted rows.
+                for value in six.itervalues(example):
+                    if value is None:
+                        break
+                else:
+                    yield example["id"], example