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import os.path |
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from itertools import count |
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from pathlib import Path |
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from typing import Dict, Iterable, List, Tuple |
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import datasets |
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from .bigbiohub import kb_features |
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from .bigbiohub import BigBioConfig |
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from .bigbiohub import Tasks |
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from .bigbiohub import parse_brat_file |
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_LANGUAGES = ["English"] |
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_PUBMED = True |
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_LOCAL = False |
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_CITATION = """\ |
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@inproceedings{Shardlow2018, |
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title = { |
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A New Corpus to Support Text Mining for the Curation of Metabolites in the |
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{ChEBI} Database |
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}, |
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author = { |
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Shardlow, M J and Nguyen, N and Owen, G and O'Donovan, C and Leach, A and |
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McNaught, J and Turner, S and Ananiadou, S |
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}, |
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year = 2018, |
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month = may, |
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booktitle = { |
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Proceedings of the Eleventh International Conference on Language Resources |
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and Evaluation ({LREC} 2018) |
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}, |
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location = {Miyazaki, Japan}, |
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pages = {280--285}, |
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conference = { |
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Eleventh International Conference on Language Resources and Evaluation |
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(LREC 2018) |
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}, |
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language = {en} |
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} |
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""" |
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_DATASETNAME = "chebi_nactem" |
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_DISPLAYNAME = "CHEBI Corpus" |
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_DESCRIPTION = """\ |
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The ChEBI corpus contains 199 annotated abstracts and 100 annotated full papers. |
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All documents in the corpus have been annotated for named entities and relations |
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between these. In total, our corpus provides over 15000 named entity annotations |
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and over 6,000 relations between entities. |
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""" |
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_HOMEPAGE = "http://www.nactem.ac.uk/chebi" |
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_LICENSE = "Creative Commons Attribution 4.0 International" |
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_URLS = { |
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_DATASETNAME: "http://www.nactem.ac.uk/chebi/ChEBI.zip", |
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} |
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_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION] |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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class ChebiNactemDatasset(datasets.GeneratorBasedBuilder): |
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"""Chemical Entities of Biological Interest (ChEBI) corpus.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
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BUILDER_CONFIGS = [] |
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for subset_id in ["abstr_ann1", "abstr_ann2", "fullpaper"]: |
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BUILDER_CONFIGS += [ |
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BigBioConfig( |
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name=f"chebi_nactem_{subset_id}_source", |
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version=SOURCE_VERSION, |
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description="chebi_nactem source schema", |
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schema="source", |
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subset_id=f"chebi_nactem_{subset_id}", |
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), |
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BigBioConfig( |
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name=f"chebi_nactem_{subset_id}_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="chebi_nactem BigBio schema", |
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schema="bigbio_kb", |
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subset_id=f"chebi_nactem_{subset_id}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "chebi_nactem_fullpaper_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"document_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"entities": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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} |
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], |
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"relations": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"arg1": datasets.Value("string"), |
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"arg2": datasets.Value("string"), |
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} |
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], |
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} |
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) |
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elif self.config.schema == "bigbio_kb": |
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features = kb_features |
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else: |
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raise NotImplementedError(self.config.schema) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=str(_LICENSE), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(urls) |
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subset_paths = { |
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"chebi_nactem_abstr_ann1": os.path.join("ChEBI", "abstracts", "Annotator1"), |
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"chebi_nactem_abstr_ann2": os.path.join("ChEBI", "abstracts", "Annotator2"), |
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"chebi_nactem_fullpaper": os.path.join("ChEBI", "fullpapers"), |
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} |
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subset_dir = os.path.join(data_dir, subset_paths[self.config.subset_id]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"file_paths": dl_manager.iter_files(subset_dir)}, |
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) |
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] |
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def _generate_examples(self, file_paths: Iterable[str]) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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uid = count(0) |
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for idx, file_path in enumerate(file_paths): |
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if os.path.basename(file_path).endswith(".txt"): |
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contents = parse_brat_file( |
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Path(file_path), annotation_file_suffixes=[".ann"] |
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) |
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if self.config.schema == "source": |
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yield idx, { |
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"document_id": contents["document_id"], |
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"text": contents["text"], |
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"entities": contents["text_bound_annotations"], |
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"relations": [ |
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{ |
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"id": relation["id"], |
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"type": relation["type"], |
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"arg1": relation["head"]["ref_id"], |
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"arg2": relation["tail"]["ref_id"], |
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} |
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for relation in contents["relations"] |
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], |
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} |
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elif self.config.schema == "bigbio_kb": |
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yield idx, { |
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"id": next(uid), |
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"document_id": contents["document_id"], |
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"passages": [ |
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{ |
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"id": next(uid), |
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"type": "", |
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"text": [contents["text"]], |
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"offsets": [(0, len(contents["text"]))], |
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} |
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], |
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"entities": [ |
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{ |
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"id": f"{idx}_{entity['id']}", |
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"type": entity["type"], |
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"offsets": entity["offsets"], |
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"text": entity["text"], |
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"normalized": [], |
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} |
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for entity in contents["text_bound_annotations"] |
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], |
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"events": [], |
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"coreferences": [], |
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"relations": [ |
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{ |
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"id": f"{idx}_{relation['id']}", |
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"type": relation["type"], |
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"arg1_id": f"{idx}_{relation['head']['ref_id']}", |
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"arg2_id": f"{idx}_{relation['tail']['ref_id']}", |
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"normalized": [], |
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} |
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for relation in contents["relations"] |
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], |
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} |
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else: |
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raise NotImplementedError(self.config.schema) |
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