Revert "remove python scripts (#7)"

This reverts commit 7ee869e8e272c6d29f7e46e46ceed2e266442f8f.

dataset_infos.json

@@ -0,0 +1,224 @@
+{
+  "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

@@ -0,0 +1,242 @@
+"""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'])
+