Delete loading script

MANTRAGSC.py

@@ -1,308 +0,0 @@
-# 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.
-
-# pip install xmltodict
-
-import random
-from pathlib import Path
-from itertools import product
-from dataclasses import dataclass
-from typing import Dict, List, Tuple
-
-import xmltodict
-import numpy as np
-
-import datasets
-
-_CITATION = """\
-@article{10.1093/jamia/ocv037,
-    author = {Kors, Jan A and Clematide, Simon and Akhondi,
-    Saber A and van Mulligen, Erik M and Rebholz-Schuhmann, Dietrich},
-    title = "{A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC}",
-    journal = {Journal of the American Medical Informatics Association},
-    volume = {22},
-    number = {5},
-    pages = {948-956},
-    year = {2015},
-    month = {05},
-    abstract = "{Objective To create a multilingual gold-standard corpus for biomedical concept recognition.Materials
-    and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels,
-    biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language
-    independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and
-    covering a wide range of semantic groups. To reduce the annotation workload, automatically generated
-    preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and
-    cross-language consistency checks were carried out to arrive at the final annotations.Results The number of final
-    annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are
-    similar to those between individual annotators and the gold standard. The automatically generated harmonized
-    annotation set for each language performed equally well as the best annotator for that language.Discussion The use
-    of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation
-    efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance
-    of automatic annotation techniques.Conclusion To our knowledge, this is the first gold-standard corpus for
-    biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety
-    of semantic groups that are being covered, and the diversity of text genres that were annotated.}",
-    issn = {1067-5027},
-    doi = {10.1093/jamia/ocv037},
-    url = {https://doi.org/10.1093/jamia/ocv037},
-    eprint = {https://academic.oup.com/jamia/article-pdf/22/5/948/34146393/ocv037.pdf},
-}
-"""
-
-_DESCRIPTION = """\
-We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims)
-in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical
-concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups.
-"""
-
-_HOMEPAGE = "https://biosemantics.erasmusmc.nl/index.php/resources/mantra-gsc"
-
-_LICENSE = "CC_BY_4p0"
-
-_URL = "https://huggingface.co/datasets/DrBenchmark/MANTRAGSC/resolve/main/GSC-v1.1.zip"
-
-_LANGUAGES_2 = {
-    "es": "Spanish",
-    "fr": "French",
-    "de": "German",
-    "nl": "Dutch",
-    "en": "English",
-}
-
-_DATASET_TYPES = {
-    "emea": "EMEA",
-    "medline": "Medline",
-    "patents": "Patent",
-}
-
-
-@dataclass
-class DrBenchmarkConfig(datasets.BuilderConfig):
-    name: str = None
-    version: datasets.Version = None
-    description: str = None
-    schema: str = None
-    subset_id: str = None
-
-
-class MANTRAGSC(datasets.GeneratorBasedBuilder):
-
-    SOURCE_VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = []
-
-    for language, dataset_type in product(_LANGUAGES_2, _DATASET_TYPES):
-
-        name = f"{language}_{dataset_type}"
-        if name in ['nl_patents', 'es_patents', 'en_medline']:
-            continue
-
-        BUILDER_CONFIGS.append(
-            DrBenchmarkConfig(
-                name=name,
-                version=SOURCE_VERSION,
-                description=f"Mantra GSC {_LANGUAGES_2[language]} {_DATASET_TYPES[dataset_type]} source schema",
-                schema="source",
-                subset_id=f"{language}_{_DATASET_TYPES[dataset_type]}",
-            )
-        )
-
-    DEFAULT_CONFIG_NAME = "fr_medline"
-
-    def _info(self):
-        # Label definition for each task
-        # Goals:
-        # - Tasks must not have extra labels (not present in their corpus)
-        # - Labels should have (roughly) the same index
-
-        # Labels common to every task (ordered by name and B-I)
-        common_names = ['O', 'B-ANAT', 'B-CHEM', 'I-CHEM', 'B-DEVI', 'B-DISO', 'I-DISO', 'B-LIVB', 'I-LIVB', 'B-OBJC', 'B-PHEN', 'B-PHYS', 'I-PHYS', 'B-PROC', 'I-PROC']
-        # Adding labels not common to every task (in an order that maximises labels having the same index accross tasks)
-        names = common_names + ["I-ANAT", "I-DEVI", "B-GEOG", "I-PHEN", "I-OBJC"]
-        unused_name_map = {
-            'de_emea': {'B-GEOG', 'I-OBJC'},
-            'en_emea': {'B-GEOG', 'I-OBJC'},
-            'es_emea': {'B-GEOG', 'I-OBJC'},
-            'fr_emea': {'B-GEOG', 'I-OBJC'},
-            'nl_emea': {'B-GEOG', 'I-OBJC'},
-
-            'de_medline': {'I-DEVI', 'I-PHEN'},
-            'es_medline': {'I-DEVI', 'I-OBJC'},
-            'fr_medline': {'I-OBJC', 'I-PHEN'},
-            'nl_medline': {'I-DEVI'},
-
-            'fr_patents': {'B-GEOG', 'I-OBJC', 'I-PHEN'},
-            'de_patents': {'B-GEOG', 'I-OBJC', 'I-PHEN', 'I-ANAT', 'I-DEVI'},
-            'en_patents': {'B-GEOG', 'I-OBJC', 'I-PHEN'}
-        }
-        names = [n for n in names if n not in unused_name_map.get(self.config.name, {})]
-
-        print(self.config.name)
-
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "tokens": [datasets.Value("string")],
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=names,
-                    )
-                ),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        language, dataset_type = self.config.name.split("_")
-
-        data_dir = dl_manager.download_and_extract(_URL)
-        data_dir = Path(data_dir) / "GSC-v1.1" / f"{_DATASET_TYPES[dataset_type]}_GSC_{language}_man.xml"
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_dir": data_dir,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_dir": data_dir,
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "data_dir": data_dir,
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_dir, split):
-
-        with open(data_dir) as fd:
-            doc = xmltodict.parse(fd.read())
-
-        all_res = []
-
-        for d in doc["Corpus"]["document"]:
-
-            if not isinstance(d["unit"], list):
-                d["unit"] = [d["unit"]]
-
-            for u in d["unit"]:
-
-                text = u["text"]
-
-                if "e" in u.keys():
-
-                    if not isinstance(u["e"], list):
-                        u["e"] = [u["e"]]
-
-                    tags = [{
-                        "label": current["@grp"].upper(),
-                        "offset_start": int(current["@offset"]),
-                        "offset_end": int(current["@offset"]) + int(current["@len"]),
-                    } for current in u["e"]]
-
-                else:
-                    tags = []
-
-                _tokens = text.split(" ")
-                tokens = []
-                for i, t in enumerate(_tokens):
-
-                    concat = " ".join(_tokens[0:i + 1])
-
-                    offset_start = len(concat) - len(t)
-                    offset_end = len(concat)
-
-                    tokens.append({
-                        "token": t,
-                        "offset_start": offset_start,
-                        "offset_end": offset_end,
-                    })
-
-                ner_tags = [["O", 0] for o in tokens]
-
-                for tag in tags:
-
-                    cpt = 0
-
-                    for idx, token in enumerate(tokens):
-
-                        rtok = range(token["offset_start"], token["offset_end"] + 1)
-                        rtag = range(tag["offset_start"], tag["offset_end"] + 1)
-
-                        # Check if the ranges are overlapping
-                        if bool(set(rtok) & set(rtag)):
-
-                            # if ner_tags[idx] != "O" and ner_tags[idx] != tag['label']:
-                            #   print(f"{token} - currently: {ner_tags[idx]} - after: {tag['label']}")
-
-                            if ner_tags[idx][0] == "O":
-                                cpt += 1
-                                ner_tags[idx][0] = tag["label"]
-                                ner_tags[idx][1] = cpt
-
-                for i in range(len(ner_tags)):
-
-                    tag = ner_tags[i][0]
-
-                    if tag == "O":
-                        continue
-                    elif tag != "O" and ner_tags[i][1] == 1:
-                        ner_tags[i][0] = "B-" + tag
-                    elif tag != "O" and ner_tags[i][1] != 1:
-                        ner_tags[i][0] = "I-" + tag
-
-                obj = {
-                    "id": u["@id"],
-                    "tokens": [t["token"] for t in tokens],
-                    "ner_tags": [n[0] for n in ner_tags],
-                }
-
-                all_res.append(obj)
-
-        ids = [r["id"] for r in all_res]
-
-        random.seed(4)
-        random.shuffle(ids)
-        random.shuffle(ids)
-        random.shuffle(ids)
-
-        train, validation, test = np.split(ids, [int(len(ids) * 0.70), int(len(ids) * 0.80)])
-
-        if split == "train":
-            allowed_ids = list(train)
-        elif split == "validation":
-            allowed_ids = list(validation)
-        elif split == "test":
-            allowed_ids = list(test)
-
-        for r in all_res:
-            identifier = r["id"]
-            if identifier in allowed_ids:
-                yield identifier, r