Create indo_law.py

indo_law.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+from dataclasses import dataclass
+
+import datasets
+import json
+import xml.etree.ElementTree as ET
+
+
+_CITATION = """\
+@article{nuranti2022predicting,
+  title={Predicting the Category and the Length of Punishment in Indonesian Courts Based on Previous Court Decision Documents},
+  author={Nuranti, Eka Qadri and Yulianti, Evi and Husin, Husna Sarirah},
+  journal={Computers},
+  volume={11},
+  number={6},
+  pages={88},
+  year={2022},
+  publisher={Multidisciplinary Digital Publishing Institute}
+}
+"""
+
+_LANGUAGES = ["id"]
+_LOCAL = False
+
+_DATASETNAME = "indo_law"
+
+_DESCRIPTION = """\
+This study presents predictions of first-level judicial decisions by utilizing a collection of Indonesian court decision documents. 
+We propose using multi-level learning, namely, CNN+attention, using decision document sections as features to predict the category and the length of punishment in Indonesian courts. 
+Our results demonstrate that the decision document sections that strongly affected the accuracy of the prediction model were prosecution history, facts, legal facts, and legal considerations.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "Unknown"
+
+_URLS = {
+    _DATASETNAME: "https://github.com/ir-nlp-csui/indo-law/zipball/master",
+}
+
+_SOURCE_VERSION = "1.0.0"
+
+
+
+@dataclass
+class IndoLawConfig(datasets.BuilderConfig):
+
+    name: str = None
+    version: datasets.Version = None
+    description: str = None
+    schema: str = None
+    subset_id: str = None
+
+class IndoLaw(datasets.GeneratorBasedBuilder):
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+
+
+    _LABELS = ["pidana-khusus", "pidana-umum"]
+
+    BUILDER_CONFIGS = [
+        IndoLawConfig(
+            name="indo_law_source",
+            version=SOURCE_VERSION,
+            description="Indo-Law source schema",
+            schema="source",
+            subset_id="indo_law",
+        ),
+        IndoLawConfig(
+            name="indo_law_nusantara_text",
+            version=SOURCE_VERSION,
+            description="Indo-Law Nusantara schema",
+            schema="nusantara_text",
+            subset_id="indo_law",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indo_law_source"
+
+    def _get_features(self, label_names):
+        return datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "label": datasets.ClassLabel(names=label_names),
+            }
+        )
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "klasifikasi": datasets.Value("string"),
+                    "sub_klasifikasi": datasets.Value("string"),
+                    "paragraphs": datasets.Sequence({
+                        "tag": datasets.Value("string"),
+                        "value": datasets.Value("string"),
+                    }),
+                }
+            )
+
+        elif self.config.schema == "nusantara_text":
+            features = self._get_features(self._LABELS)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[
+        datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        data_dir = os.path.join(data_dir, "ir-nlp-csui-indo-law-6734033", "dataset")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        files = os.listdir(filepath)
+
+        results = []
+
+        for file in files:
+            data = self._parse_file(os.path.join(filepath, file))
+            results.append(data)
+
+        if self.config.schema == "source":
+            key = 0
+            for result in results:
+                example = {
+                    "id": result["id"],
+                    "klasifikasi": result["klasifikasi"],
+                    "sub_klasifikasi": result["klasifikasi"],
+                    "paragraphs": [],
+                }
+                for tag in result["paragraphs"]:
+                    example["paragraphs"].append({
+                        "tag": tag,
+                        "value": result["paragraphs"][tag]
+                    })
+                yield key, example
+                key += 1
+
+        elif self.config.schema == "nusantara_text":
+            key = 0
+            for result in results:
+                example = {
+                    "id": result["id"],
+                    "text": json.dumps(result["paragraphs"]),
+                    "label": result["klasifikasi"],
+                }
+                yield key, example
+                key += 1
+
+    def _parse_file(self, file_path):
+        root = ET.parse(file_path).getroot()
+
+        data = {
+            "id": root.attrib["id"],
+            "klasifikasi": root.attrib["klasifikasi"],
+            "sub_klasifikasi": root.attrib["sub_klasifikasi"],
+            "paragraphs": {}
+        }
+
+        for child in root:
+            data["paragraphs"].update({
+                child.tag: child.text
+            })
+
+        return data
+