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ind_proner.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.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.common_parser import load_conll_data
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@INPROCEEDINGS{9212879,
+  author={Akmal, Muhammad and Romadhony, Ade},
+  booktitle={2020 International Conference on Data Science and Its Applications (ICoDSA)},
+  title={Corpus Development for Indonesian Product Named Entity Recognition Using Semi-supervised Approach},
+  year={2020},
+  volume={},
+  number={},
+  pages={1-5},
+  keywords={Feature extraction;Labeling;Buildings;Semisupervised learning;Training data;Text recognition;Manuals;proner;semi-supervised learning;crf},
+  doi={10.1109/ICoDSA50139.2020.9212879}
+}
+"""
+
+_DATASETNAME = "ind_proner"
+
+_DESCRIPTION = """\
+Indonesian PRONER is a corpus for Indonesian product named entity recognition . It contains data was labeled manually
+and data that was labeled automatically through a semi-supervised learning approach of conditional random fields (CRF).
+"""
+
+_HOMEPAGE = "https://github.com/dziem/proner-labeled-text"
+
+_LANGUAGES = {"ind": "id"}
+
+_LANGUAGE_CODES = list(_LANGUAGES.values())
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "automatic": "https://raw.githubusercontent.com/dziem/proner-labeled-text/master/automatically_labeled.tsv",
+    "manual": "https://raw.githubusercontent.com/dziem/proner-labeled-text/master/manually_labeled.tsv",
+}
+
+_ANNOTATION_TYPES = list(_URLS.keys())
+_ANNOTATION_IDXS = {"l1": 0, "l2": 1}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+logger = datasets.logging.get_logger(__name__)
+
+
+class IndPRONERDataset(datasets.GeneratorBasedBuilder):
+    """
+    Indonesian PRONER is a product named entity recognition dataset from https://github.com/dziem/proner-labeled-text.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = (
+        [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{annotation_type}_source",
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME}_{annotation_type} source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}_{annotation_type}",
+            )
+            for annotation_type in _ANNOTATION_TYPES
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{annotation_type}_l1_seacrowd_seq_label",
+                version=datasets.Version(_SEACROWD_VERSION),
+                description=f"{_DATASETNAME}_{annotation_type}_l1 SEACrowd schema",
+                schema="seacrowd_seq_label",
+                subset_id=f"{_DATASETNAME}_{annotation_type}_l1",
+            )
+            for annotation_type in _ANNOTATION_TYPES
+        ]
+        + [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{annotation_type}_l2_seacrowd_seq_label",
+                version=datasets.Version(_SEACROWD_VERSION),
+                description=f"{_DATASETNAME}_{annotation_type}_l2 SEACrowd schema",
+                schema="seacrowd_seq_label",
+                subset_id=f"{_DATASETNAME}_{annotation_type}_l2",
+            )
+            for annotation_type in _ANNOTATION_TYPES
+        ]
+    )
+
+    label_classes = [
+        "B-PRO",
+        "B-BRA",
+        "B-TYP",
+        "I-PRO",
+        "I-BRA",
+        "I-TYP",
+        "O",
+    ]
+
+    def _extract_label(self, text: str, idx: int) -> str:
+        split = text.split("|")
+        if len(split) > 1 and idx != -1:
+            return split[idx]
+        else:
+            return text
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label_features(label_names=self.label_classes)
+
+        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.
+        """
+
+        annotation_type = self.config.subset_id.split("_")[2]
+        path = dl_manager.download_and_extract(_URLS[annotation_type])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """
+        Yields examples as (key, example) tuples.
+        """
+        label_idx = -1
+        subset_id = self.config.subset_id.split("_")
+        if len(subset_id) > 3:
+            if subset_id[3] in _ANNOTATION_IDXS:
+                label_idx = _ANNOTATION_IDXS[subset_id[3]]
+
+        idx = 0
+        conll_dataset = load_conll_data(filepath)
+        if self.config.schema == "source":
+            for _, row in enumerate(conll_dataset):
+                x = {"id": str(idx), "tokens": row["sentence"], "ner_tags": list(map(self._extract_label, row["label"], [label_idx] * len(row["label"])))}
+                yield idx, x
+                idx += 1
+        elif self.config.schema == "seacrowd_seq_label":
+            for _, row in enumerate(conll_dataset):
+                x = {"id": str(idx), "tokens": row["sentence"], "labels": list(map(self._extract_label, row["label"], [label_idx] * len(row["label"])))}
+                yield idx, x
+                idx += 1