Convert dataset to Parquet

Convert dataset to Parquet.

README.md

@@ -40,25 +40,40 @@ dataset_info:
     dtype: bool
   splits:
   - name: exercise_contest_train
-    num_bytes: 220112732
+    num_bytes: 220112348
     num_examples: 154592
   - name: exercise_contest_valid
-    num_bytes: 21702157
+    num_bytes: 21702109
     num_examples: 17131
   - name: exercise_contest_test
-    num_bytes: 41057634
+    num_bytes: 41057538
     num_examples: 32508
   - name: first_stage_train
-    num_bytes: 1779657510
+    num_bytes: 1779653382
     num_examples: 1710856
   - name: first_stage_test
-    num_bytes: 244335194
+    num_bytes: 244334666
     num_examples: 217016
   - name: final_test
-    num_bytes: 44194707
+    num_bytes: 44194611
     num_examples: 35922
-  download_size: 984551626
-  dataset_size: 2351059934
+  download_size: 1167828091
+  dataset_size: 2351054654
+configs:
+- config_name: default
+  data_files:
+  - split: exercise_contest_train
+    path: data/exercise_contest_train-*
+  - split: exercise_contest_valid
+    path: data/exercise_contest_valid-*
+  - split: exercise_contest_test
+    path: data/exercise_contest_test-*
+  - split: first_stage_train
+    path: data/first_stage_train-*
+  - split: first_stage_test
+    path: data/first_stage_test-*
+  - split: final_test
+    path: data/final_test-*
 ---
 ---
 # Dataset Card for CAIL 2018

data/exercise_contest_test-00000-of-00001.parquet

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+size 21036060

data/exercise_contest_train-00000-of-00001.parquet

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data/exercise_contest_valid-00000-of-00001.parquet

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data/final_test-00000-of-00001.parquet

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+size 20383366

data/first_stage_test-00000-of-00001.parquet

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+size 120551158

data/first_stage_train-00000-of-00004.parquet

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data/first_stage_train-00001-of-00004.parquet

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data/first_stage_train-00002-of-00004.parquet

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data/first_stage_train-00003-of-00004.parquet

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dataset_infos.json

@@ -1 +1,101 @@
-{"default": {"description": "In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),\nthe first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million\ncriminal cases published by the Supreme People's Court of China, which are several times larger than other\ndatasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more\ndetailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected\nto be inferred according to the fact descriptions of cases. For comparison, we implement several conventional\ntext classification baselines for judgment prediction and experimental results show that it is still a\nchallenge for current models to predict the judgment results of legal cases, especially on prison terms.\nTo help the researchers make improvements on legal judgment prediction.\n", "citation": "@misc{xiao2018cail2018,\n      title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction}, \n      author={Chaojun Xiao and Haoxi Zhong and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},\n      year={2018},\n      eprint={1807.02478},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n", "homepage": "https://arxiv.org/abs/1807.02478", "license": "", "features": {"fact": {"dtype": "string", "id": null, "_type": "Value"}, "relevant_articles": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "accusation": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "punish_of_money": {"dtype": "float32", "id": null, "_type": "Value"}, "criminals": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "death_penalty": {"dtype": "bool", "id": null, "_type": "Value"}, "imprisonment": {"dtype": "float32", "id": null, "_type": "Value"}, "life_imprisonment": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "cail2018", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"exercise_contest_train": {"name": "exercise_contest_train", "num_bytes": 220112732, "num_examples": 154592, "dataset_name": "cail2018"}, "exercise_contest_valid": {"name": "exercise_contest_valid", "num_bytes": 21702157, "num_examples": 17131, "dataset_name": "cail2018"}, "exercise_contest_test": {"name": "exercise_contest_test", "num_bytes": 41057634, "num_examples": 32508, "dataset_name": "cail2018"}, "first_stage_train": {"name": "first_stage_train", "num_bytes": 1779657510, "num_examples": 1710856, "dataset_name": "cail2018"}, "first_stage_test": {"name": "first_stage_test", "num_bytes": 244335194, "num_examples": 217016, "dataset_name": "cail2018"}, "final_test": {"name": "final_test", "num_bytes": 44194707, "num_examples": 35922, "dataset_name": "cail2018"}}, "download_checksums": {"https://cail.oss-cn-qingdao.aliyuncs.com/CAIL2018_ALL_DATA.zip": {"num_bytes": 984551626, "checksum": "3c05dfdade742f8b0d5e782d174475e7769448a5f407bfb7f14f0aed72d61d4a"}}, "download_size": 984551626, "post_processing_size": null, "dataset_size": 2351059934, "size_in_bytes": 3335611560}}
+{
+    "default": {
+        "description": "In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),\nthe first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million\ncriminal cases published by the Supreme People's Court of China, which are several times larger than other\ndatasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more\ndetailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected\nto be inferred according to the fact descriptions of cases. For comparison, we implement several conventional\ntext classification baselines for judgment prediction and experimental results show that it is still a\nchallenge for current models to predict the judgment results of legal cases, especially on prison terms.\nTo help the researchers make improvements on legal judgment prediction.\n",
+        "citation": "@misc{xiao2018cail2018,\n      title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},\n      author={Chaojun Xiao and Haoxi Zhong and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},\n      year={2018},\n      eprint={1807.02478},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n",
+        "homepage": "https://arxiv.org/abs/1807.02478",
+        "license": "",
+        "features": {
+            "fact": {
+                "dtype": "string",
+                "_type": "Value"
+            },
+            "relevant_articles": {
+                "feature": {
+                    "dtype": "int32",
+                    "_type": "Value"
+                },
+                "_type": "Sequence"
+            },
+            "accusation": {
+                "feature": {
+                    "dtype": "string",
+                    "_type": "Value"
+                },
+                "_type": "Sequence"
+            },
+            "punish_of_money": {
+                "dtype": "float32",
+                "_type": "Value"
+            },
+            "criminals": {
+                "feature": {
+                    "dtype": "string",
+                    "_type": "Value"
+                },
+                "_type": "Sequence"
+            },
+            "death_penalty": {
+                "dtype": "bool",
+                "_type": "Value"
+            },
+            "imprisonment": {
+                "dtype": "float32",
+                "_type": "Value"
+            },
+            "life_imprisonment": {
+                "dtype": "bool",
+                "_type": "Value"
+            }
+        },
+        "builder_name": "parquet",
+        "dataset_name": "cail2018",
+        "config_name": "default",
+        "version": {
+            "version_str": "1.0.0",
+            "major": 1,
+            "minor": 0,
+            "patch": 0
+        },
+        "splits": {
+            "exercise_contest_train": {
+                "name": "exercise_contest_train",
+                "num_bytes": 220112348,
+                "num_examples": 154592,
+                "dataset_name": null
+            },
+            "exercise_contest_valid": {
+                "name": "exercise_contest_valid",
+                "num_bytes": 21702109,
+                "num_examples": 17131,
+                "dataset_name": null
+            },
+            "exercise_contest_test": {
+                "name": "exercise_contest_test",
+                "num_bytes": 41057538,
+                "num_examples": 32508,
+                "dataset_name": null
+            },
+            "first_stage_train": {
+                "name": "first_stage_train",
+                "num_bytes": 1779653382,
+                "num_examples": 1710856,
+                "dataset_name": null
+            },
+            "first_stage_test": {
+                "name": "first_stage_test",
+                "num_bytes": 244334666,
+                "num_examples": 217016,
+                "dataset_name": null
+            },
+            "final_test": {
+                "name": "final_test",
+                "num_bytes": 44194611,
+                "num_examples": 35922,
+                "dataset_name": null
+            }
+        },
+        "download_size": 1167828091,
+        "dataset_size": 2351054654,
+        "size_in_bytes": 3518882745
+    }
+}