Upload bioner_id.py with huggingface_hub

bioner_id.py

@@ -0,0 +1,166 @@
+# 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 = """\
+@article{abdillah2023pengenalan,
+  title={Pengenalan Entitas Biomedis dalam Teks Konsultasi Kesehatan Online Berbahasa Indonesia Berbasis Arsitektur Transformers},
+  author={Abdillah, Abid Famasya and Purwitasari, Diana and Juanita, Safitri and Purnomo, Mauridhi Hery},
+  year={2023},
+  month=feb,
+  journal={Jurnal Teknologi Informasi dan Ilmu Komputer},
+  volume={10},
+  number={1},
+  pages={131--140}
+}
+"""
+
+_DATASETNAME = "bioner_id"
+
+_DESCRIPTION = """\
+This dataset taken from online health consultation platform Alodokter.com which has been annotated by two medical doctors. Data were annotated using IOB in CoNLL format.
+
+Dataset contains 2600 medical answers by doctors from 2017-2020. Two medical experts were assigned to annotate the data into two entity types: DISORDERS and ANATOMY.
+The topics of answers are: diarrhea, HIV-AIDS, nephrolithiasis and TBC, which marked as high-risk dataset from WHO.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/abid/indonesia-bioner-dataset"
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.BSD_3_CLAUSE_CLEAR.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {k: f"https://huggingface.co/datasets/abid/indonesia-bioner-dataset/raw/main/{k}.conll" for k in ["train", "valid", "test"]},
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class BioNERIdDataset(datasets.GeneratorBasedBuilder):
+    """2600 conversations of patioent and medical doctors between 2017-2020.
+    Two medical annotated the data into two entity types: DISORDERS and ANATOMY"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    label_classes = ["B-ANAT", "B-DISO", "I-ANAT", "I-DISO", "O"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_seq_label",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_seq_label",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "sentence": [datasets.Value("string")],
+                    "label": [datasets.Value("string")],
+                }
+            )
+
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label_features(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."""
+        urls = _URLS[_DATASETNAME]
+        data_paths = dl_manager.download(urls)
+
+        # def _assert_data(msg):
+        #     cur_data = list(map(
+        #         lambda d: d.split(" "),
+        #         open(fp, "r", encoding="utf8").readlines()
+        #     ))
+        #     assert {1, 4} == set(map(len, cur_data)), msg    # length of 4 is due to uncommon delimiter of " _ _ "
+        #     assert {('_', '_')} == set(map(lambda _: (_[1], _[2]), filter(lambda _: len(_) == 4, cur_data))), msg
+
+        # Convert to tab-seperated value
+        for subset in ["train", "valid", "test"]:
+            fp = data_paths[subset]
+            # _assert_data(f"Invalid file for subset '{subset}'")
+            data = open(fp, "r", encoding="utf8").read()
+            # data_paths[subset] = f"{fp}.tsv"
+            open(data_paths[subset], "w", encoding="utf8").write(data.replace(" _ _ ", "\t"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_paths["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_paths["test"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_paths["valid"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        data = load_conll_data(filepath)
+
+        if self.config.schema == "source":
+            for key, ex in enumerate(data):
+                yield key, ex
+
+        elif self.config.schema == "seacrowd_seq_label":
+            for key, ex in enumerate(data):
+                yield key, {
+                    "id": str(key),
+                    "tokens": ex["sentence"],
+                    "labels": ex["label"],
+                }