Datasets:

mnaguib
/

QuaeroFrenchMed

@@ -1,160 +0,0 @@
-# Copyright 2020 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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-import csv
-import json
-import os
-import datasets
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """
-@article{neveol2014quaero,
-  title={The QUAERO French medical corpus: A ressource for medical entity recognition and normalization},
-  author={N{\'e}v{\'e}ol, Aur{\'e}lie and Grouin, Cyril and Leixa, Jeremy and Rosset, Sophie and Zweigenbaum, Pierre},
-  journal={Proc of BioTextMining Work},
-  pages={24--30},
-  year={2014}
-}
-"""
-# You can copy an official description
-_DESCRIPTION = """\
-The QUAEROFrenchMed is a manually annotated corpus developed as a resource for named entity named recognition and normalization.
-"""
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://quaerofrenchmed.limsi.fr/"
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset.
-    The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].
-    A selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS):
-    1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy (ANAT), Chemical and Drugs (CHEM), Devices (DEVI), Disorders (DISO), Geographic Areas (GEOG), Living Beings (LIVB), Objects (OBJC), Phenomena (PHEN), Physiology (PHYS), Procedures (PROC).
-    2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051)
-    """
-    VERSION = datasets.Version("1.1.0")
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-    # You will be able to load one or the other configurations in the following list with
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="EMEA", version=VERSION, description="information on marketed drugs from the European Medicines Agency (EMEA)"),
-        datasets.BuilderConfig(name="MEDLINE", version=VERSION, description="The titles of MEDLINE-indexed articles"),
-    ]
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "EMEA":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "docid": datasets.Value("string"),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(datasets.Value("int32")),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "docid": datasets.Value("string"),
-                    "words": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(datasets.Value("int32")),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-    # def _split_generators(self, dl_manager):
-    #     # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-    #     # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-    #     # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-    #     # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-    #     # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-    #     urls = _URLS[self.config.name]
-    #     data_dir = dl_manager.download_and_extract(urls)
-    #     return [
-    #         datasets.SplitGenerator(
-    #             name=datasets.Split.TRAIN,
-    #             # These kwargs will be passed to _generate_examples
-    #             gen_kwargs={
-    #                 "filepath": os.path.join(data_dir, "train.jsonl"),
-    #                 "split": "train",
-    #             },
-    #         ),
-    #         datasets.SplitGenerator(
-    #             name=datasets.Split.VALIDATION,
-    #             # These kwargs will be passed to _generate_examples
-    #             gen_kwargs={
-    #                 "filepath": os.path.join(data_dir, "dev.jsonl"),
-    #                 "split": "dev",
-    #             },
-    #         ),
-    #         datasets.SplitGenerator(
-    #             name=datasets.Split.TEST,
-    #             # These kwargs will be passed to _generate_examples
-    #             gen_kwargs={
-    #                 "filepath": os.path.join(data_dir, "test.jsonl"),
-    #                 "split": "test"
-    #             },
-    #         ),
-    #     ]
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "EMEA":
-                    yield key, {
-                        "docid": data["docid"],
-                        "words": data["words"],
-                        "ner_tags": data["ner_tags"],
-                    }
-                else:
-                    yield key, {
-                        "docid": data["docid"],
-                        "words": data["words"],
-                        "ner_tags": data["ner_tags"],
-                    }