Update files from the datasets library (from 1.3.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- ko
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+---
+
+# Dataset Card for Structured Argument Extraction for Korean
+
+## 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-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage: [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)**
+- **Repository: [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)**
+- **Paper: [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342)**
+- **Point of Contact: [Won Ik Cho](wicho@hi.snu.ac.kr)**
+
+### Dataset Summary
+
+The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech.
+
+### Supported Tasks and Leaderboards
+
+* `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore. 
+
+### Languages
+
+The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
+
+## Dataset Structure
+
+### Data Instances
+
+An example data instance contains a question or command pair and its label:
+
+```
+{
+  "intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘"
+  "intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기"
+  "label": 4
+}
+```
+
+### Data Fields
+
+* `intent_pair1`: a question/command pair
+* `intent_pair2`: a corresponding question/command pair
+* `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5)
+
+### Data Splits
+
+The corpus contains 30,837 examples.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives. 
+
+#### Who are the source language producers?
+
+Korean speakers are the source language producers.
+
+### Annotations
+
+#### Annotation process
+
+Utterances were categorized as question or command arguments and then further classified according to their intent argument.
+
+#### Who are the annotators?
+
+The annotation was done by three Korean natives with a background in computational linguistics.
+
+### 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
+
+The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim.
+
+### Licensing Information
+
+The dataset is licensed under the CC BY-SA-4.0.
+
+### Citation Information
+
+```
+@article{cho2019machines,
+  title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
+  author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
+  journal={arXiv preprint arXiv:1912.00342},
+  year={2019}
+}
+```
+
+### Contributions
+
+Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "This new dataset is designed to extract intent from non-canonical directives which will help dialog managers\nextract intent from user dialog that may have no clear objective or are paraphrased forms of utterances.\n", "citation": "@article{cho2019machines,\n  title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},\n  author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},\n  journal={arXiv preprint arXiv:1912.00342},\n  year={2019}\n}\n", "homepage": "https://github.com/warnikchow/sae4k", "license": "CC-BY-SA-4.0", "features": {"intent_pair1": {"dtype": "string", "id": null, "_type": "Value"}, "intent_pair2": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 6, "names": ["yes/no", "alternative", "wh- questions", "prohibitions", "requirements", "strong requirements"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kor_sae", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2885167, "num_examples": 30837, "dataset_name": "kor_sae"}}, "download_checksums": {"https://raw.githubusercontent.com/warnikchow/sae4k/master/data/sae4k_v1.txt": {"num_bytes": 2545926, "checksum": "529361e1aa760ca90db71fc70a93215f45938028735aba1291a907f764fe1f36"}}, "download_size": 2545926, "post_processing_size": null, "dataset_size": 2885167, "size_in_bytes": 5431093}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:201218de592216b69d0ec5b294f7039d9c5c24d51d296a09ecd4186b5f1a9d9b
+size 478

kor_sae.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.
+"""Structured Argument Extraction for Korean"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@article{cho2019machines,
+  title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
+  author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
+  journal={arXiv preprint arXiv:1912.00342},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+This new dataset is designed to extract intent from non-canonical directives which will help dialog managers
+extract intent from user dialog that may have no clear objective or are paraphrased forms of utterances.
+"""
+
+_HOMEPAGE = "https://github.com/warnikchow/sae4k"
+
+_LICENSE = "CC-BY-SA-4.0"
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/warnikchow/sae4k/master/data/sae4k_v1.txt"
+
+
+class KorSae(datasets.GeneratorBasedBuilder):
+    """Structured Argument Extraction for Korean"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "intent_pair1": datasets.Value("string"),
+                    "intent_pair2": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(
+                        names=[
+                            "yes/no",
+                            "alternative",
+                            "wh- questions",
+                            "prohibitions",
+                            "requirements",
+                            "strong requirements",
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate KorSAE examples"""
+
+        with open(filepath, encoding="utf-8") as csv_file:
+            data = csv.reader(csv_file, delimiter="\t")
+            for id_, row in enumerate(data):
+                intent_pair1, intent_pair2, label = row
+                yield id_, {"intent_pair1": intent_pair1, "intent_pair2": intent_pair2, "label": int(label)}