Convert dataset to Parquet (#2)

- Convert dataset to Parquet (d10d2fe422209dee24e62895b693261fbbdd6b33)
- Add mixed data files (48d5f7848a9237f5e3684ae120ae2cbd385f929e)
- Delete loading script (7aa834eb963987f401d634363819183729d026cb)
- Delete legacy dataset_infos.json (b88dac81a26d715e7ce8739e70e656db5d7633a4)

README.md

@@ -20,7 +20,7 @@ task_ids:
 paperswithcode_id: c3
 pretty_name: C3
 dataset_info:
-- config_name: mixed
+- config_name: dialog
   features:
   - name: documents
     sequence: string
@@ -36,17 +36,17 @@ dataset_info:
       sequence: string
   splits:
   - name: train
-    num_bytes: 2710513
-    num_examples: 3138
+    num_bytes: 2039779
+    num_examples: 4885
   - name: test
-    num_bytes: 891619
-    num_examples: 1045
+    num_bytes: 646955
+    num_examples: 1627
   - name: validation
-    num_bytes: 910799
-    num_examples: 1046
-  download_size: 5481785
-  dataset_size: 4512931
-- config_name: dialog
+    num_bytes: 611106
+    num_examples: 1628
+  download_size: 2073256
+  dataset_size: 3297840
+- config_name: mixed
   features:
   - name: documents
     sequence: string
@@ -62,16 +62,33 @@ dataset_info:
       sequence: string
   splits:
   - name: train
-    num_bytes: 2039819
-    num_examples: 4885
+    num_bytes: 2710473
+    num_examples: 3138
   - name: test
-    num_bytes: 646995
-    num_examples: 1627
+    num_bytes: 891579
+    num_examples: 1045
   - name: validation
-    num_bytes: 611146
-    num_examples: 1628
-  download_size: 4352392
-  dataset_size: 3297960
+    num_bytes: 910759
+    num_examples: 1046
+  download_size: 3183780
+  dataset_size: 4512811
+configs:
+- config_name: dialog
+  data_files:
+  - split: train
+    path: dialog/train-*
+  - split: test
+    path: dialog/test-*
+  - split: validation
+    path: dialog/validation-*
+- config_name: mixed
+  data_files:
+  - split: train
+    path: mixed/train-*
+  - split: test
+    path: mixed/test-*
+  - split: validation
+    path: mixed/validation-*
 ---
 # Dataset Card for C3
 

c3.py

@@ -1,149 +0,0 @@
-# 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"""
-
-
-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

@@ -1 +0,0 @@
-{"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. 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": "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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