Datasets:
Languages:
German
Multilinguality:
monolingual
| # 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. | |
| import json | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import pandas as pd | |
| import datasets | |
| from .bigbiohub import kb_features | |
| from .bigbiohub import BigBioConfig | |
| from .bigbiohub import Tasks | |
| _LOCAL = True | |
| _CITATION = """\ | |
| @inproceedings{borchert-etal-2022-ggponc, | |
| title = "{GGPONC} 2.0 - The {G}erman Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline {NER} Taggers", | |
| author = "Borchert, Florian and | |
| Lohr, Christina and | |
| Modersohn, Luise and | |
| Witt, Jonas and | |
| Langer, Thomas and | |
| Follmann, Markus and | |
| Gietzelt, Matthias and | |
| Arnrich, Bert and | |
| Hahn, Udo and | |
| Schapranow, Matthieu-P.", | |
| booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", | |
| month = jun, | |
| year = "2022", | |
| address = "Marseille, France", | |
| publisher = "European Language Resources Association", | |
| url = "https://aclanthology.org/2022.lrec-1.389", | |
| pages = "3650--3660", | |
| } | |
| """ | |
| _DATASETNAME = "ggponc2" | |
| _DESCRIPTION = """\ | |
| The GGPONC project aims to provide a freely distributable corpus of German medical text for NLP researchers. | |
| Clinical guidelines are particularly suitable to create such corpora, as they contain no protected health information | |
| (PHI), which distinguishes them from other kinds of medical text. | |
| The second version of the corpus (GGPONC 2.0) consists of 30 German oncology guidelines with 1.87 million tokens. | |
| It has been completely manually annotated on the entity level by 7 medical students using the INCEpTION platform over a | |
| time frame of 6 months in more than 1200 hours of work. This makes GGPONC 2.0 the largest annotated, freely | |
| distributable corpus of German medical text at the moment. | |
| Annotated entities are Findings (Diagnosis / Pathology, Other Finding), Substances (Clinical Drug, Nutrients / Body | |
| Substances, External Substances) and Procedures (Therapeutic, Diagnostic), as well as Specifications for these entities. | |
| In total, annotators have created more than 200000 entity annotations. In addition, fragment relationships have been | |
| annotated to explicitly indicate elliptical coordinated noun phrases, a common phenomenon in German text.""" | |
| _HOMEPAGE = "https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english/" | |
| _LANGUAGES = ['German'] | |
| _URLS = {} | |
| _PUBMED = False | |
| _SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION] | |
| _SOURCE_VERSION = "2.0.0" | |
| _BIGBIO_VERSION = "1.0.0" | |
| _DISPLAYNAME = "GGPONC 2.0" | |
| _DATASETNAME = "ggponc2" | |
| _LICENSE = "DUA" | |
| class Ggponc(datasets.GeneratorBasedBuilder): | |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | |
| BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) | |
| DEFAULT_CONFIG_NAME = "ggponc2_fine_long_bigbio_kb" | |
| BUILDER_CONFIGS = [ | |
| # source | |
| BigBioConfig( | |
| name="ggponc2_fine_long_source", | |
| version=SOURCE_VERSION, | |
| description="GGPONC 2.0 (fine grained categories and long spans) schema", | |
| schema="source", | |
| subset_id="ggponc2", | |
| ), | |
| BigBioConfig( | |
| name="ggponc2_fine_short_source", | |
| version=SOURCE_VERSION, | |
| description="GGPONC 2.0 (fine grained categories and short spans) schema", | |
| schema="source", | |
| subset_id="ggponc2", | |
| ), | |
| BigBioConfig( | |
| name="ggponc2_coarse_long_source", | |
| version=SOURCE_VERSION, | |
| description="GGPONC 2.0 (coarse categories and long spans) schema", | |
| schema="source", | |
| subset_id="ggponc2", | |
| ), | |
| BigBioConfig( | |
| name="ggponc2_coarse_short_source", | |
| version=SOURCE_VERSION, | |
| description="GGPONC 2.0 (coarse categories and short spans) schema", | |
| schema="source", | |
| subset_id="ggponc2", | |
| ), | |
| # bigbio | |
| BigBioConfig( | |
| name="ggponc2_fine_long_bigbio_kb", | |
| version=BIGBIO_VERSION, | |
| description="GGPONC 2.0 (fine grained categories and long spans) schema", | |
| schema="bigbio_kb", | |
| subset_id="ggponc2", | |
| ), | |
| BigBioConfig( | |
| name="ggponc2_fine_short_bigbio_kb", | |
| version=BIGBIO_VERSION, | |
| description="GGPONC 2.0 (fine grained categories and short spans) schema", | |
| schema="bigbio_kb", | |
| subset_id="ggponc2", | |
| ), | |
| BigBioConfig( | |
| name="ggponc2_coarse_long_bigbio_kb", | |
| version=BIGBIO_VERSION, | |
| description="GGPONC 2.0 (coarse categories and long spans) schema", | |
| schema="bigbio_kb", | |
| subset_id="ggponc2", | |
| ), | |
| BigBioConfig( | |
| name="ggponc2_coarse_short_bigbio_kb", | |
| version=BIGBIO_VERSION, | |
| description="GGPONC 2.0 (coarse categories and short spans) schema", | |
| schema="bigbio_kb", | |
| subset_id="ggponc2", | |
| ), | |
| ] | |
| def _info(self) -> datasets.DatasetInfo: | |
| # return the same info for source and bigbio versions because `source == bigbio_kb` | |
| features = kb_features | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: | |
| """Returns SplitGenerators.""" | |
| if self.config.data_dir is None: | |
| raise ValueError( | |
| "This is a local dataset. Please pass the data_dir kwarg to load_dataset." | |
| ) | |
| else: | |
| data_dir = Path(self.config.data_dir) | |
| split_dir = data_dir / "annotations/splits.csv" | |
| dir_lookup = { | |
| "ggponc2_fine_long_bigbio_kb": data_dir | |
| / "annotations/json/fine/long/all.json", | |
| "ggponc2_fine_short_bigbio_kb": data_dir | |
| / "annotations/json/fine/short/all.json", | |
| "ggponc2_coarse_long_bigbio_kb": data_dir | |
| / "annotations/json/coarse/long/all.json", | |
| "ggponc2_coarse_short_bigbio_kb": data_dir | |
| / "annotations/json/coarse/short/all.json", | |
| } | |
| # return the same info for source and bigbio versions because `source == bigbio_kb` | |
| data_dir = dir_lookup[self.config.name.replace('source', 'bigbio_kb')] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "split": "train", | |
| "filepath": data_dir, | |
| "split_dir": split_dir, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "split": "test", | |
| "filepath": data_dir, | |
| "split_dir": split_dir, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "split": "dev", | |
| "filepath": data_dir, | |
| "split_dir": split_dir, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples( | |
| self, filepath: str, split: str, split_dir: str | |
| ) -> Tuple[int, Dict]: | |
| """Yields examples as (key, example) tuples.""" | |
| splits = pd.read_csv(split_dir) | |
| with open(filepath, encoding="utf8") as file: | |
| data = json.load(file) | |
| for uid, row in enumerate(data): | |
| file_name = row["document_id"].replace("tsv", "txt") | |
| file_split = splits.loc[splits["file"] == file_name]["split"].item() | |
| if file_split == split: | |
| out = { | |
| "id": uid, | |
| "document_id": row["document_id"], | |
| "passages": [], | |
| "entities": row["entities"], | |
| } | |
| for j, passage in enumerate(row["passages"]): | |
| passage_id = passage["id"] | |
| out["passages"].append( | |
| { | |
| "id": f"{uid}-{j}", | |
| "type": passage["type"], | |
| "text": [passage["text"]], | |
| "offsets": passage["offsets"], | |
| } | |
| ) | |
| for i, _ in enumerate(out["entities"]): | |
| out["entities"][i]["id"] = f"{uid}-{i}" | |
| out["entities"][i]["normalized"] = [] | |
| out["events"] = [] | |
| out["coreferences"] = [] | |
| out["relations"] = [] | |
| yield uid, out | |