remove python scripts (#7)

- remove python scripts (603f5135e70b8b94e73a66ee66975979177acb8b)

dataset_infos.json

@@ -1,224 +0,0 @@
-{
-  "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"
-      },
-      "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": "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"
-      },
-      "ending_3": {
-        "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,
-    "supervised_keys": null,
-    "builder_name": "kobest_v1",
-    "config_name": "copa",
-    "version": {
-      "version_str": "1.0.0",
-      "description": "",
-      "major": 1,
-      "minor": 0,
-      "patch": 0
-    }
-  }
-}

kobest_v1.py

@@ -1,242 +0,0 @@
-"""Korean Balanced Evaluation of Significant Tasks"""
-
-
-import csv
-import os
-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__":
-    for config_name in ["boolq", "copa", 'sentineg', 'hellaswag', 'wic']:
-        dataset = datasets.load_dataset("kobest_v1.py", config_name, ignore_verifications=True)
-        os.makedirs(config_name, exist_ok=True)
-        for split, split_dataset in dataset.items():
-            split_dataset.to_json(f"{config_name}/{split}.jsonl")
-    # 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'])
-