Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+languages:
+- zh
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n>1M
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-judgement-prediction---
+---
+# Dataset Card for CAIL 2018
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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)
+
+## Dataset Description
+
+- **Homepage:** [Github](https://github.com/thunlp/CAIL/blob/master/README_en.md)
+- **Repository:** [Github](https://github.com/thunlp/CAIL)
+- **Paper:** [Arxiv](https://arxiv.org/abs/1807.02478)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## 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
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

cail2018.py

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+from __future__ import absolute_import, division, print_function
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{xiao2018cail2018,
+      title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},
+      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},
+      year={2018},
+      eprint={1807.02478},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),
+the first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million
+criminal cases published by the Supreme People's Court of China, which are several times larger than other
+datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more
+detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected
+to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional
+text classification baselines for judgment prediction and experimental results show that it is still a
+challenge for current models to predict the judgment results of legal cases, especially on prison terms.
+To help the researchers make improvements on legal judgment prediction.
+"""
+_URL = "https://cail.oss-cn-qingdao.aliyuncs.com/CAIL2018_ALL_DATA.zip"
+
+
+class Cail2018(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "fact": datasets.Value("string"),
+                "relevant_articles": datasets.Sequence(datasets.Value("int32")),
+                "accusation": datasets.Sequence(datasets.Value("string")),
+                "punish_of_money": datasets.Value("float"),
+                "criminals": datasets.Sequence(datasets.Value("string")),
+                "death_penalty": datasets.Value("bool"),
+                "imprisonment": datasets.Value("float"),
+                "life_imprisonment": datasets.Value("bool"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage="https://arxiv.org/abs/1807.02478",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        dl_dir = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split("exercise_contest_train"),
+                gen_kwargs={
+                    "filepath": os.path.join(dl_dir, "final_all_data/exercise_contest/data_train.json"),
+                    "split": "exercise_contest_train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("exercise_contest_valid"),
+                gen_kwargs={
+                    "filepath": os.path.join(dl_dir, "final_all_data/exercise_contest/data_valid.json"),
+                    "split": "exercise_contest_valid",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("exercise_contest_test"),
+                gen_kwargs={
+                    "filepath": os.path.join(dl_dir, "final_all_data/exercise_contest/data_test.json"),
+                    "split": "exercise_contest_test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("first_stage_train"),
+                gen_kwargs={
+                    "filepath": os.path.join(dl_dir, "final_all_data/first_stage/train.json"),
+                    "split": "first_stage_train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("first_stage_test"),
+                gen_kwargs={
+                    "filepath": os.path.join(dl_dir, "final_all_data/first_stage/test.json"),
+                    "split": "first_stage_test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split("final_test"),
+                gen_kwargs={"filepath": os.path.join(dl_dir, "final_all_data/final_test.json"), "split": "final_test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for idx, row in enumerate(f):
+                data = json.loads(row)
+                yield idx, {
+                    "fact": data["fact"],
+                    "relevant_articles": data["meta"]["relevant_articles"],
+                    "accusation": data["meta"]["accusation"],
+                    "punish_of_money": data["meta"]["punish_of_money"],
+                    "criminals": data["meta"]["criminals"],
+                    "death_penalty": data["meta"]["term_of_imprisonment"]["death_penalty"],
+                    "imprisonment": data["meta"]["term_of_imprisonment"]["imprisonment"],
+                    "life_imprisonment": data["meta"]["term_of_imprisonment"]["life_imprisonment"],
+                }

dataset_infos.json

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+{"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}}

dummy/1.0.0/dummy_data.zip

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+size 14736