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indoler.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+https://github.com/ir-nlp-csui/indoler/tree/main
+The dataset contains 993 annotated court decission document. 
+The document was taken from Decision of the Supreme Court of Indonesia. 
+The documents have also been tokenized and cleaned
+"""
+import os
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_CITATION = """\
+@INPROCEEDINGS{9263157,
+  author={Nuranti, Eka Qadri and Yulianti, Evi},
+  booktitle={2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, 
+  title={Legal Entity Recognition in Indonesian Court Decision Documents Using Bi-LSTM and CRF Approaches}, 
+  year={2020},
+  volume={},
+  number={},
+  pages={429-434},
+  keywords={Xenon;6G mobile communication;legal processing;legal entity recognition;legal document;name entity recognition;ner;bi-lstm;lstm;crf},
+  doi={10.1109/ICACSIS51025.2020.9263157}}
+"""
+
+_DATASETNAME = "indoler"
+
+_DESCRIPTION = """\
+https://github.com/ir-nlp-csui/indoler/tree/main
+The data can be used for NER Task in legal documents.
+The dataset contains 993 annotated court decission document. 
+The document was taken from Decision of the Supreme Court of Indonesia. 
+The documents have also been tokenized and cleaned
+"""
+
+_HOMEPAGE = "https://github.com/ir-nlp-csui/indoler/tree/main"
+
+_LANGUAGES = ['ind']  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.UNKNOWN.value 
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "test_idx": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/test.ids.csv",
+        "train_idx": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/train.ids.csv",
+        "valid_idx": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/val.ids.csv",
+        "full_data": "https://raw.githubusercontent.com/ir-nlp-csui/indoler/main/data.json"
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]  
+
+_SOURCE_VERSION = "2.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+
+class IndoLer(datasets.GeneratorBasedBuilder):
+    """https://github.com/ir-nlp-csui/indoler/tree/main
+The data can be used for NER Task in legal documents
+The dataset contains 993 annotated court decission document. 
+The document was taken from Decision of the Supreme Court of Indonesia. 
+The documents have also been tokenized and cleaned"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="indoler_source",
+            version=SOURCE_VERSION,
+            description="indoler source schema",
+            schema="source",
+            subset_id="indoler",
+        ),
+        SEACrowdConfig(
+            name="indoler_seacrowd_seq_label",
+            version=SEACROWD_VERSION,
+            description="indoler SEACrowd schema",
+            schema="seacrowd_seq_label",
+            subset_id="indoler",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indoler_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        NAMED_ENTITIES = ['O', 'B-Jenis Amar', 'B-Jenis Dakwaan', 'B-Jenis Perkara', 'B-Melanggar UU (Dakwaan)', 
+                    'B-Melanggar UU (Pertimbangan Hukum)', 'B-Melanggar UU (Tuntutan)', 'B-Nama Hakim Anggota', 'B-Nama Hakim Ketua', 
+                    'B-Nama Jaksa', 'B-Nama Panitera', 'B-Nama Pengacara', 'B-Nama Pengadilan', 
+                    'B-Nama Saksi', 'B-Nama Terdakwa', 'B-Nomor Putusan', 'B-Putusan Hukuman', 
+                    'B-Tanggal Kejadian', 'B-Tanggal Putusan', 'B-Tingkat Kasus', 'B-Tuntutan Hukuman', 
+                    'I-Jenis Amar', 'I-Jenis Dakwaan', 'I-Jenis Perkara', 'I-Melanggar UU (Dakwaan)', 
+                    'I-Melanggar UU (Pertimbangan Hukum)', 'I-Melanggar UU (Tuntutan)', 'I-Nama Hakim Anggota', 'I-Nama Hakim Ketua', 
+                    'I-Nama Jaksa', 'I-Nama Panitera', 'I-Nama Pengacara', 'I-Nama Pengadilan', 
+                    'I-Nama Saksi', 'I-Nama Terdakwa', 'I-Nomor Putusan', 'I-Putusan Hukuman', 
+                    'I-Tanggal Kejadian', 'I-Tanggal Putusan', 'I-Tingkat Kasus', 'I-Tuntutan Hukuman']
+
+        if self.config.schema == "source":
+            features = datasets.Features({
+                "id": datasets.Value("string"),
+                "owner": datasets.Value("string"),
+                "lawyer": datasets.ClassLabel(names=[False, True]),
+                "verdict": datasets.ClassLabel(names=["guilty", "bebas", "lepas"]),
+                "indictment": datasets.ClassLabel(names=["NA", "tunggal", "subsider", "komul", "alternatif", "kombinasi", "gabungan"]),
+                "text-tags": datasets.Sequence(datasets.ClassLabel(names=NAMED_ENTITIES)),
+                "text": datasets.Sequence(datasets.Value("string")),
+            })
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label.features(NAMED_ENTITIES)
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        test_path = dl_manager.download_and_extract(urls['test_idx'])
+        train_path = dl_manager.download_and_extract(urls['train_idx'])
+        valid_path = dl_manager.download_and_extract(urls['valid_idx'])
+        data_path = dl_manager.download_and_extract(urls['full_data'])
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "idx_path": train_path,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "idx_path": test_path,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "idx_path": valid_path,
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, idx_path: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        split_idxs = []
+        with open(idx_path, 'r', encoding="utf-8") as indexes:
+            for index in indexes.readlines():
+                split_idxs.append(int(index))
+        with open(filepath, 'r', encoding="utf-8") as file:
+            contents = json.load(file)
+            counter = 0
+            for content in contents:
+                if int(content['id']) in split_idxs:
+                    if self.config.schema == "source":
+                        if content['indictment'] not in ["NA", "tunggal", "subsider", "komul", "alternatif", "kombinasi", "gabungan"]:
+                            content['indictment'] = "NA"
+                        yield(
+                            counter,
+                            {
+                                "id"        : content['id'],
+                                "owner"     : content['owner'],
+                                "lawyer"    : content['lawyer'],
+                                "verdict"   : content['verdict'],
+                                "indictment": content['indictment'],
+                                "text-tags" : content['text-tags'],
+                                "text"      : content['text'],
+                            }
+                        )        
+                        counter += 1
+                    elif self.config.schema == "seacrowd_seq_label":
+                        yield(
+                            counter,
+                            {
+                                "id": content['id'],
+                                "tokens": content['text'],
+                                "labels": content['text-tags'],
+                            }
+                        )
+                        counter += 1