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README.md

@@ -1,246 +0,0 @@
----
-pretty_name: KoBEST
-annotations_creators:
-- expert-generated
-language_creators:
-- expert-generated
-language:
-- ko
-license:
-- cc-by-sa-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
----
-
-# Dataset Card for KoBEST
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#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
-
-- **Repository:** https://github.com/SKT-LSL/KoBEST_datarepo
-- **Paper:**
-- **Point of Contact:** https://github.com/SKT-LSL/KoBEST_datarepo/issues
-
-### Dataset Summary
-
-KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean.
-
-### Supported Tasks and Leaderboards
-
-Boolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition
-
-### Languages
-
-`ko-KR`
-
-## Dataset Structure
-
-### Data Instances
-
-#### KB-BoolQ
-An example of a data point looks as follows.
-```
-{'paragraph': '두아 리파(Dua Lipa, 1995년 8월 22일 ~ )는 잉글랜드의 싱어송라이터, 모델이다. BBC 사운드 오브 2016 명단에 노미닛되었다. 싱글 "Be the One"가 영국 싱글 차트 9위까지 오르는 등 성과를 보여주었다.',
- 'question': '두아 리파는 영국인인가?',
- 'label': 1}
-```
-
-#### KB-COPA
-An example of a data point looks as follows.
-```
-{'premise': '물을 오래 끓였다.',
- 'question': '결과',
- 'alternative_1': '물의 양이 늘어났다.',
- 'alternative_2': '물의 양이 줄어들었다.',
- 'label': 1}
-```
-
-#### KB-WiC
-An example of a data point looks as follows.
-```
-{'word': '양분',
- 'context_1': '토양에 [양분]이 풍부하여 나무가 잘 자란다.	',
- 'context_2': '태아는 모체로부터 [양분]과 산소를 공급받게 된다.',
- 'label': 1}
-```
-
-#### KB-HellaSwag
-An example of a data point looks as follows.
-```
-{'context': '모자를 쓴 투수가 타자에게 온 힘을 다해 공을 던진다. 공이 타자에게 빠른 속도로 다가온다. 타자가 공을 배트로 친다. 배트에서 깡 소리가 난다. 공이 하늘 위로 날아간다.',
- 'ending_1': '외야수가 떨어지는 공을 글러브로 잡는다.',
- 'ending_2': '외야수가 공이 떨어질 위치에 자리를 잡는다.',
- 'ending_3': '심판이 아웃을 외친다.',
- 'ending_4': '외야수가 공을 따라 뛰기 시작한다.',
- 'label': 3}
-```
-
-#### KB-SentiNeg
-An example of a data point looks as follows.
-```
-{'sentence': '택배사 정말 마음에 듬',
- 'label': 1}
-```
-
-### Data Fields
-
-### KB-BoolQ
-+ `paragraph`: a `string` feature
-+ `question`: a `string` feature
-+ `label`: a classification label, with possible values `False`(0) and `True`(1)
-
-
-### KB-COPA
-+ `premise`: a `string` feature
-+ `question`: a `string` feature
-+ `alternative_1`: a `string` feature
-+ `alternative_2`: a `string` feature
-+ `label`: an answer candidate label, with possible values `alternative_1`(0) and `alternative_2`(1)
-
-
-### KB-WiC
-+ `target_word`: a `string` feature
-+ `context_1`: a `string` feature
-+ `context_2`: a `string` feature
-+ `label`: a classification label, with possible values `False`(0) and `True`(1)
-
-### KB-HellaSwag
-+ `target_word`: a `string` feature
-+ `context_1`: a `string` feature
-+ `context_2`: a `string` feature
-+ `label`: a classification label, with possible values `False`(0) and `True`(1)
-
-### KB-SentiNeg
-+ `sentence`: a `string` feature
-+ `label`: a classification label, with possible values `Negative`(0) and `Positive`(1)
-
-
-### Data Splits
-
-#### KB-BoolQ
-
-+ train: 3,665
-+ dev: 700
-+ test: 1,404
-
-#### KB-COPA
-
-+ train: 3,076
-+ dev: 1,000
-+ test: 1,000
-
-#### KB-WiC
-
-+ train: 3,318
-+ dev: 1,260
-+ test: 1,260
-
-#### KB-HellaSwag
-
-+ train: 3,665
-+ dev: 700
-+ test: 1,404
-
-#### KB-SentiNeg
-
-+ train: 3,649
-+ dev: 400
-+ test: 397
-+ test_originated: 397 (Corresponding training data where the test set is originated from.)
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### 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
-
-```
-@misc{https://doi.org/10.48550/arxiv.2204.04541,
-  doi = {10.48550/ARXIV.2204.04541},
-  url = {https://arxiv.org/abs/2204.04541},
-  author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric},
-  title = {KOBEST: Korean Balanced Evaluation of Significant Tasks},
-  publisher = {arXiv},
-  year = {2022},
-}
-```
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@MJ-Jang](https://github.com/MJ-Jang) for adding this dataset.

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dataset_infos.json

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-{
-  "boolq": {
-    "description": "    Korean Balanced Evaluation of Significant Tasks Benchmark\n",
-    "citation": "    TBD\n",
-    "homepage": "https://github.com/SKT-LSL/KoBEST_datarepo",
-    "license": "",
-    "features": {
-      "paragraph": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "question": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "label": {
-        "num_classes": 2,
-        "names": [
-          "False",
-          "True"
-        ],
-        "names_file": null,
-        "id": null,
-        "_type": "ClassLabel"
-      }
-    },
-    "post_processed": null,
-    "supervised_keys": null,
-    "builder_name": "kobest_v1",
-    "config_name": "boolq",
-    "version": {
-      "version_str": "1.0.0",
-      "description": "",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    }
-  },
-    "copa": {
-    "description": "    Korean Balanced Evaluation of Significant Tasks Benchmark\n",
-    "citation": "    TBD\n",
-    "homepage": "https://github.com/SKT-LSL/KoBEST_datarepo",
-    "license": "",
-    "features": {
-      "premise": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "question": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "alternative_1": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "alternative_2": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "label": {
-        "num_classes": 2,
-        "names": [
-          "alternative_1",
-          "alternative_2"
-        ],
-        "names_file": null,
-        "id": null,
-        "_type": "ClassLabel"
-      }
-    },
-    "post_processed": null,
-    "supervised_keys": null,
-    "builder_name": "kobest_v1",
-    "config_name": "copa",
-    "version": {
-      "version_str": "1.0.0",
-      "description": "",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    }
-  },
-  "wic": {
-    "description": "    Korean Balanced Evaluation of Significant Tasks Benchmark\n",
-    "citation": "    TBD\n",
-    "homepage": "https://github.com/SKT-LSL/KoBEST_datarepo",
-    "license": "",
-    "features": {
-      "word": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "context_1": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "context_2": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
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-        "num_classes": 2,
-        "names": [
-          "False",
-          "True"
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-        "id": null,
-        "_type": "ClassLabel"
-      }
-    },
-    "post_processed": null,
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-    "builder_name": "kobest_v1",
-    "config_name": "copa",
-    "version": {
-      "version_str": "1.0.0",
-      "description": "",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    }
-  },
-  "hellaswag": {
-    "description": "    Korean Balanced Evaluation of Significant Tasks Benchmark\n",
-    "citation": "    TBD\n",
-    "homepage": "https://github.com/SKT-LSL/KoBEST_datarepo",
-    "license": "",
-    "features": {
-      "context": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "ending_1": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "ending_2": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
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-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "ending_4": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "label": {
-        "num_classes": 4,
-        "names": [
-          "ending_1",
-          "ending_2",
-          "ending_3",
-          "ending_4"
-        ],
-        "names_file": null,
-        "id": null,
-        "_type": "ClassLabel"
-      }
-    },
-    "post_processed": null,
-    "supervised_keys": null,
-    "builder_name": "kobest_v1",
-    "config_name": "copa",
-    "version": {
-      "version_str": "1.0.0",
-      "description": "",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    }
-  },
-  "sentineg": {
-    "description": "    Korean Balanced Evaluation of Significant Tasks Benchmark\n",
-    "citation": "    TBD\n",
-    "homepage": "https://github.com/SKT-LSL/KoBEST_datarepo",
-    "license": "",
-    "features": {
-      "sentence": {
-        "dtype": "string",
-        "id": null,
-        "_type": "Value"
-      },
-      "label": {
-        "num_classes": 2,
-        "names": [
-          "negative",
-          "positive"
-        ],
-        "names_file": null,
-        "id": null,
-        "_type": "ClassLabel"
-      }
-    },
-    "post_processed": null,
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-    "builder_name": "kobest_v1",
-    "config_name": "copa",
-    "version": {
-      "version_str": "1.0.0",
-      "description": "",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    }
-  }
-}

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kobest_v1.py

@@ -1,241 +0,0 @@
-"""Korean Balanced Evaluation of Significant Tasks"""
-
-
-import csv
-
-import pandas as pd
-
-import datasets
-
-
-_CITATAION = """\
-@misc{https://doi.org/10.48550/arxiv.2204.04541,
-  doi = {10.48550/ARXIV.2204.04541},
-  url = {https://arxiv.org/abs/2204.04541},
-  author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric},
-  title = {KOBEST: Korean Balanced Evaluation of Significant Tasks},
-  publisher = {arXiv},
-  year = {2022},
-}
-"""
-
-_DESCRIPTION = """\
-    The dataset contains data for KoBEST dataset
-"""
-
-_URL = "https://github.com/SKT-LSL/KoBEST_datarepo/raw/main"
-
-
-_DATA_URLS = {
-    "boolq": {
-        "train": _URL + "/v1.0/BoolQ/train.tsv",
-        "dev": _URL + "/v1.0/BoolQ/dev.tsv",
-        "test": _URL + "/v1.0/BoolQ/test.tsv",
-    },
-    "copa": {
-        "train": _URL + "/v1.0/COPA/train.tsv",
-        "dev": _URL + "/v1.0/COPA/dev.tsv",
-        "test": _URL + "/v1.0/COPA/test.tsv",
-    },
-    "sentineg": {
-        "train": _URL + "/v1.0/SentiNeg/train.tsv",
-        "dev": _URL + "/v1.0/SentiNeg/dev.tsv",
-        "test": _URL + "/v1.0/SentiNeg/test.tsv",
-        "test_originated": _URL + "/v1.0/SentiNeg/test.tsv",
-    },
-    "hellaswag": {
-        "train": _URL + "/v1.0/HellaSwag/train.tsv",
-        "dev": _URL + "/v1.0/HellaSwag/dev.tsv",
-        "test": _URL + "/v1.0/HellaSwag/test.tsv",
-    },
-    "wic": {
-        "train": _URL + "/v1.0/WiC/train.tsv",
-        "dev": _URL + "/v1.0/WiC/dev.tsv",
-        "test": _URL + "/v1.0/WiC/test.tsv",
-    },
-}
-
-_LICENSE = "CC-BY-SA-4.0"
-
-
-class KoBESTConfig(datasets.BuilderConfig):
-    """Config for building KoBEST"""
-
-    def __init__(self, description, data_url, citation, url, **kwargs):
-        """
-        Args:
-            description: `string`, brief description of the dataset
-            data_url: `dictionary`, dict with url for each split of data.
-            citation: `string`, citation for the dataset.
-            url: `string`, url for information about the dataset.
-            **kwrags: keyword arguments frowarded to super
-        """
-        super(KoBESTConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.description = description
-        self.data_url = data_url
-        self.citation = citation
-        self.url = url
-
-
-class KoBEST(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        KoBESTConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL)
-        for name in ["boolq", "copa", 'sentineg', 'hellaswag', 'wic']
-    ]
-    BUILDER_CONFIG_CLASS = KoBESTConfig
-
-    def _info(self):
-        features = {}
-        if self.config.name == "boolq":
-            labels = ["False", "True"]
-            features["paragraph"] = datasets.Value("string")
-            features["question"] = datasets.Value("string")
-            features["label"] = datasets.features.ClassLabel(names=labels)
-
-        if self.config.name == "copa":
-            labels = ["alternative_1", "alternative_2"]
-            features["premise"] = datasets.Value("string")
-            features["question"] = datasets.Value("string")
-            features["alternative_1"] = datasets.Value("string")
-            features["alternative_2"] = datasets.Value("string")
-            features["label"] = datasets.features.ClassLabel(names=labels)
-
-        if self.config.name == "wic":
-            labels = ["False", "True"]
-            features["word"] = datasets.Value("string")
-            features["context_1"] = datasets.Value("string")
-            features["context_2"] = datasets.Value("string")
-            features["label"] = datasets.features.ClassLabel(names=labels)
-
-        if self.config.name == "hellaswag":
-            labels = ["ending_1", "ending_2", "ending_3", "ending_4"]
-
-            features["context"] = datasets.Value("string")
-            features["ending_1"] = datasets.Value("string")
-            features["ending_2"] = datasets.Value("string")
-            features["ending_3"] = datasets.Value("string")
-            features["ending_4"] = datasets.Value("string")
-            features["label"] = datasets.features.ClassLabel(names=labels)
-
-        if self.config.name == "sentineg":
-            labels = ["negative", "positive"]
-            features["sentence"] = datasets.Value("string")
-            features["label"] = datasets.features.ClassLabel(names=labels)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION, features=datasets.Features(features), homepage=_URL, citation=_CITATAION
-        )
-
-    def _split_generators(self, dl_manager):
-
-        train = dl_manager.download_and_extract(self.config.data_url["train"])
-        dev = dl_manager.download_and_extract(self.config.data_url["dev"])
-        test = dl_manager.download_and_extract(self.config.data_url["test"])
-
-        if self.config.data_url.get("test_originated"):
-            test_originated = dl_manager.download_and_extract(self.config.data_url["test_originated"])
-
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
-                datasets.SplitGenerator(name="test_originated", gen_kwargs={"filepath": test_originated, "split": "test_originated"}),
-            ]
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        if self.config.name == "boolq":
-            df = pd.read_csv(filepath, sep="\t")
-            df = df.dropna()
-            df = df[['Text', 'Question', 'Answer']]
-
-            df = df.rename(columns={
-                'Text': 'paragraph',
-                'Question': 'question',
-                'Answer': 'label',
-            })
-            df['label'] = [0 if str(s) == 'False' else 1 for s in df['label'].tolist()]
-
-        elif self.config.name == "copa":
-            df = pd.read_csv(filepath, sep="\t")
-            df = df.dropna()
-            df = df[['sentence', 'question', '1', '2', 'Answer']]
-
-            df = df.rename(columns={
-                'sentence': 'premise',
-                'question': 'question',
-                '1': 'alternative_1',
-                '2': 'alternative_2',
-                'Answer': 'label',
-            })
-            df['label'] = [i-1 for i in df['label'].tolist()]
-
-        elif self.config.name == "wic":
-            df = pd.read_csv(filepath, sep="\t")
-            df = df.dropna()
-            df = df[['Target', 'SENTENCE1', 'SENTENCE2', 'ANSWER']]
-
-            df = df.rename(columns={
-                'Target': 'word',
-                'SENTENCE1': 'context_1',
-                'SENTENCE2': 'context_2',
-                'ANSWER': 'label',
-            })
-            df['label'] = [0 if str(s) == 'False' else 1 for s in df['label'].tolist()]
-
-        elif self.config.name == "hellaswag":
-            df = pd.read_csv(filepath, sep="\t")
-            df = df.dropna()
-            df = df[['context', 'choice1', 'choice2', 'choice3', 'choice4', 'label']]
-
-            df = df.rename(columns={
-                'context': 'context',
-                'choice1': 'ending_1',
-                'choice2': 'ending_2',
-                'choice3': 'ending_3',
-                'choice4': 'ending_4',
-                'label': 'label',
-            })
-
-        elif self.config.name == "sentineg":
-            df = pd.read_csv(filepath, sep="\t")
-            df = df.dropna()
-
-            if split == "test_originated":
-                df = df[['Text_origin', 'Label_origin']]
-
-                df = df.rename(columns={
-                    'Text_origin': 'sentence',
-                    'Label_origin': 'label',
-                })
-            else:
-                df = df[['Text', 'Label']]
-
-                df = df.rename(columns={
-                    'Text': 'sentence',
-                    'Label': 'label',
-                })
-
-        else:
-            raise NotImplementedError
-
-        for id_, row in df.iterrows():
-            features = {key: row[key] for key in row.keys()}
-            yield id_, features
-
-
-if __name__ == "__main__":
-    dataset = datasets.load_dataset("kobest_v1.py", 'sentineg', ignore_verifications=True)
-    ds = dataset['test_originated']
-    print(ds)
-
-    # for task in ['boolq', 'copa', 'wic', 'hellaswag', 'sentineg']:
-    #     dataset = datasets.load_dataset("kobest_v1.py", task, ignore_verifications=True)
-    #     print(dataset)
-    #     print(dataset['train']['label'])
-

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