Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- zh
+licenses:
+- other-non-commercial-research
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- multiple-choice-qa
+---
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** []()
+- **Repository:** [link]()
+- **Paper:** []()
+- **Leaderboard:** []()
+- **Point of Contact:** []()
+
+### Dataset Summary
+
+Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
+We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+[More Information Needed]
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@article{sun2019investigating,
+  title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
+  author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
+  journal={Transactions of the Association for Computational Linguistics},
+  year={2020},
+  url={https://arxiv.org/abs/1904.09679v3}
+}
+```
+

c3.py

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+# 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.
+"""C3 Parallel Corpora"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@article{sun2019investigating,
+  title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
+  author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
+  journal={Transactions of the Association for Computational Linguistics},
+  year={2020},
+  url={https://arxiv.org/abs/1904.09679v3}
+}
+"""
+
+_DESCRIPTION = """\
+Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.
+We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.
+"""
+
+_URL = "https://raw.githubusercontent.com/nlpdata/c3/master/data/"
+
+
+class C3Config(datasets.BuilderConfig):
+    """ BuilderConfig for NewDataset"""
+
+    def __init__(self, type_, **kwargs):
+        """
+
+        Args:
+            pair: the language pair to consider
+            zip_file: The location of zip file containing original data
+            **kwargs: keyword arguments forwarded to super.
+        """
+        self.type_ = type_
+        super().__init__(**kwargs)
+
+
+class C3(datasets.GeneratorBasedBuilder):
+    """C3 is the first free-form multiple-Choice Chinese machine reading Comprehension dataset, containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second language examinations."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    BUILDER_CONFIG_CLASS = C3Config
+    BUILDER_CONFIGS = [
+        C3Config(
+            name="mixed",
+            description="Mixed genre questions",
+            version=datasets.Version("1.0.0"),
+            type_="mixed",
+        ),
+        C3Config(
+            name="dialog",
+            description="Dialog questions",
+            version=datasets.Version("1.0.0"),
+            type_="dialog",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "documents": datasets.Sequence(datasets.Value("string")),
+                    "document_id": datasets.Value("string"),
+                    "questions": datasets.Sequence(
+                        {
+                            "question": datasets.Value("string"),
+                            "answer": datasets.Value("string"),
+                            "choice": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ),
+                }
+            ),
+            # 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://github.com/nlpdata/c3",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # m or d
+        T = self.config.type_[0]
+        files = [_URL + f"c3-{T}-{split}.json" for split in ["train", "test", "dev"]]
+        dl_dir = dl_manager.download_and_extract(files)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filename": dl_dir[0],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filename": dl_dir[1],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filename": dl_dir[2],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filename, split):
+        """ Yields examples. """
+        with open(filename, "r", encoding="utf-8") as sf:
+            data = json.load(sf)
+            for id_, (documents, questions, document_id) in enumerate(data):
+                yield id_, {
+                    "documents": documents,
+                    "questions": questions,
+                    "document_id": document_id,
+                }

dataset_infos.json

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+{"mixed": {"description": "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations.\nWe present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.\n", "citation": "@article{sun2019investigating,\n  title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},\n  author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},\n  journal={Transactions of the Association for Computational Linguistics},\n  year={2020},\n  url={https://arxiv.org/abs/1904.09679v3}\n}\n", "homepage": "https://github.com/nlpdata/c3", "license": "", "features": {"documents": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "document_id": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "choice": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "c3", "config_name": "mixed", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2710513, "num_examples": 3138, "dataset_name": "c3"}, "test": {"name": "test", "num_bytes": 891619, "num_examples": 1045, "dataset_name": "c3"}, "validation": {"name": "validation", "num_bytes": 910799, "num_examples": 1046, "dataset_name": "c3"}}, "download_checksums": {"https://raw.githubusercontent.com/nlpdata/c3/master/data/c3-m-train.json": {"num_bytes": 3292571, "checksum": "4c84a534f1eec2c72e5f60f0c044cc39e2e42a88df01134e677e03217472d6af"}, "https://raw.githubusercontent.com/nlpdata/c3/master/data/c3-m-test.json": {"num_bytes": 1085489, "checksum": "7d8074be56cf574536a3284bc2d6b04d137694d5e5f5b1368143c0cf3e336822"}, "https://raw.githubusercontent.com/nlpdata/c3/master/data/c3-m-dev.json": {"num_bytes": 1103725, "checksum": "357d0d8d2a29bc845cbe50e048c263629f5e527b70f24c3e0838c387c8d3cb54"}}, "download_size": 5481785, "post_processing_size": null, "dataset_size": 4512931, "size_in_bytes": 9994716}, "dialog": {"description": "Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. 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We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text.\n", "citation": "@article{sun2019investigating,\n  title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},\n  author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},\n  journal={Transactions of the Association for Computational Linguistics},\n  year={2020},\n  url={https://arxiv.org/abs/1904.09679v3}\n}\n", "homepage": "https://github.com/nlpdata/c3", "license": "", "features": {"documents": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "document_id": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "choice": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "c3", "config_name": "dialog", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2039819, "num_examples": 4885, "dataset_name": "c3"}, "test": {"name": "test", "num_bytes": 646995, "num_examples": 1627, "dataset_name": "c3"}, "validation": {"name": "validation", "num_bytes": 611146, "num_examples": 1628, "dataset_name": "c3"}}, "download_checksums": {"https://raw.githubusercontent.com/nlpdata/c3/master/data/c3-d-train.json": {"num_bytes": 2683529, "checksum": "baf81f327dee84c6f451c9a4dd662e6193c67473b8791ffb72cce75cdb528f20"}, "https://raw.githubusercontent.com/nlpdata/c3/master/data/c3-d-test.json": {"num_bytes": 855404, "checksum": "e9920491b31f9d00ecf31e51727b495dd6b0d05f4a96f273a343e81b6775a8f0"}, "https://raw.githubusercontent.com/nlpdata/c3/master/data/c3-d-dev.json": {"num_bytes": 813459, "checksum": "8c7054930a40aeb288ad7c51c42fa93d54aef678ccab29c75d46a7432f4f6278"}}, "download_size": 4352392, "post_processing_size": null, "dataset_size": 3297960, "size_in_bytes": 7650352}}

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