Delete legacy dataset_infos.json

dataset_infos.json

@@ -1,144 +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",
-                    "_type": "Value"
-                },
-                "_type": "Sequence"
-            },
-            "document_id": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "questions": {
-                "feature": {
-                    "question": {
-                        "dtype": "string",
-                        "_type": "Value"
-                    },
-                    "answer": {
-                        "dtype": "string",
-                        "_type": "Value"
-                    },
-                    "choice": {
-                        "feature": {
-                            "dtype": "string",
-                            "_type": "Value"
-                        },
-                        "_type": "Sequence"
-                    }
-                },
-                "_type": "Sequence"
-            }
-        },
-        "builder_name": "parquet",
-        "dataset_name": "c3",
-        "config_name": "mixed",
-        "version": {
-            "version_str": "1.0.0",
-            "major": 1,
-            "minor": 0,
-            "patch": 0
-        },
-        "splits": {
-            "train": {
-                "name": "train",
-                "num_bytes": 2710473,
-                "num_examples": 3138,
-                "dataset_name": null
-            },
-            "test": {
-                "name": "test",
-                "num_bytes": 891579,
-                "num_examples": 1045,
-                "dataset_name": null
-            },
-            "validation": {
-                "name": "validation",
-                "num_bytes": 910759,
-                "num_examples": 1046,
-                "dataset_name": null
-            }
-        },
-        "download_size": 3183780,
-        "dataset_size": 4512811,
-        "size_in_bytes": 7696591
-    },
-    "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",
-                    "_type": "Value"
-                },
-                "_type": "Sequence"
-            },
-            "document_id": {
-                "dtype": "string",
-                "_type": "Value"
-            },
-            "questions": {
-                "feature": {
-                    "question": {
-                        "dtype": "string",
-                        "_type": "Value"
-                    },
-                    "answer": {
-                        "dtype": "string",
-                        "_type": "Value"
-                    },
-                    "choice": {
-                        "feature": {
-                            "dtype": "string",
-                            "_type": "Value"
-                        },
-                        "_type": "Sequence"
-                    }
-                },
-                "_type": "Sequence"
-            }
-        },
-        "builder_name": "parquet",
-        "dataset_name": "c3",
-        "config_name": "dialog",
-        "version": {
-            "version_str": "1.0.0",
-            "major": 1,
-            "minor": 0,
-            "patch": 0
-        },
-        "splits": {
-            "train": {
-                "name": "train",
-                "num_bytes": 2039779,
-                "num_examples": 4885,
-                "dataset_name": null
-            },
-            "test": {
-                "name": "test",
-                "num_bytes": 646955,
-                "num_examples": 1627,
-                "dataset_name": null
-            },
-            "validation": {
-                "name": "validation",
-                "num_bytes": 611106,
-                "num_examples": 1628,
-                "dataset_name": null
-            }
-        },
-        "download_size": 2073256,
-        "dataset_size": 3297840,
-        "size_in_bytes": 5371096
-    }
-}