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from pathlib import Path |
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from typing import Dict, List |
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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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from .bigbiohub import brat_parse_to_bigbio_kb |
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_DATASETNAME = "bionlp_st_2011_ge" |
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_DISPLAYNAME = "BioNLP 2011 GE" |
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_SOURCE_VIEW_NAME = "source" |
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_UNIFIED_VIEW_NAME = "bigbio" |
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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{10.5555/2107691.2107693, |
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author = {Kim, Jin-Dong and Wang, Yue and Takagi, Toshihisa and Yonezawa, Akinori}, |
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title = {Overview of Genia Event Task in BioNLP Shared Task 2011}, |
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year = {2011}, |
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isbn = {9781937284091}, |
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publisher = {Association for Computational Linguistics}, |
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address = {USA}, |
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abstract = {The Genia event task, a bio-molecular event extraction task, |
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is arranged as one of the main tasks of BioNLP Shared Task 2011. |
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As its second time to be arranged for community-wide focused |
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efforts, it aimed to measure the advance of the community since 2009, |
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and to evaluate generalization of the technology to full text papers. |
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After a 3-month system development period, 15 teams submitted their |
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performance results on test cases. The results show the community has |
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made a significant advancement in terms of both performance improvement |
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and generalization.}, |
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booktitle = {Proceedings of the BioNLP Shared Task 2011 Workshop}, |
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pages = {7–15}, |
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numpages = {9}, |
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location = {Portland, Oregon}, |
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series = {BioNLP Shared Task '11} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical |
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documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE. |
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The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein" |
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without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components, |
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are not differentiated from each other, and their type is given as "Entity". |
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""" |
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_HOMEPAGE = "https://sites.google.com/site/bionlpst/bionlp-shared-task-2011/genia-event-extraction-genia" |
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_LICENSE = 'Creative Commons Attribution 3.0 Unported' |
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_URLs = { |
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"train": "data/train.zip", |
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"validation": "data/devel.zip", |
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"test": "data/test.zip", |
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} |
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_SUPPORTED_TASKS = [ |
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Tasks.EVENT_EXTRACTION, |
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Tasks.NAMED_ENTITY_RECOGNITION, |
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Tasks.COREFERENCE_RESOLUTION, |
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] |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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class bionlp_st_2011_ge(datasets.GeneratorBasedBuilder): |
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"""The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical |
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documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE""" |
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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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BigBioConfig( |
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name="bionlp_st_2011_ge_source", |
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version=SOURCE_VERSION, |
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description="bionlp_st_2011_ge source schema", |
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schema="source", |
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subset_id="bionlp_st_2011_ge", |
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), |
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BigBioConfig( |
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name="bionlp_st_2011_ge_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="bionlp_st_2011_ge BigBio schema", |
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schema="bigbio_kb", |
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subset_id="bionlp_st_2011_ge", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "bionlp_st_2011_ge_source" |
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_ROLE_MAPPING = { |
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"Theme2": "Theme", |
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"Theme3": "Theme", |
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"Theme4": "Theme", |
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"Site2": "Site", |
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} |
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def _info(self): |
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""" |
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- `features` defines the schema of the parsed data set. The schema depends on the |
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chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the |
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original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the |
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canonical KB-task schema defined in `biomedical/schemas/kb.py`. |
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""" |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"text_bound_annotations": [ |
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{ |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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"text": datasets.Sequence(datasets.Value("string")), |
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"type": datasets.Value("string"), |
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"id": datasets.Value("string"), |
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} |
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], |
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"events": [ |
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{ |
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"trigger": datasets.Value( |
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"string" |
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), |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"arguments": datasets.Sequence( |
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{ |
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"role": datasets.Value("string"), |
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"ref_id": datasets.Value("string"), |
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} |
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), |
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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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"head": { |
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"ref_id": datasets.Value("string"), |
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"role": datasets.Value("string"), |
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}, |
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"tail": { |
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"ref_id": datasets.Value("string"), |
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"role": datasets.Value("string"), |
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}, |
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"type": datasets.Value("string"), |
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} |
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], |
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"equivalences": [ |
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{ |
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"id": datasets.Value("string"), |
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"ref_ids": datasets.Sequence(datasets.Value("string")), |
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} |
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], |
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"attributes": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"ref_id": datasets.Value("string"), |
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"value": datasets.Value("string"), |
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} |
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], |
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"normalizations": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"ref_id": datasets.Value("string"), |
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"resource_name": datasets.Value( |
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"string" |
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), |
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"cuid": datasets.Value( |
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"string" |
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), |
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"text": datasets.Value( |
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"string" |
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), |
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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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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( |
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self, dl_manager: datasets.DownloadManager |
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) -> List[datasets.SplitGenerator]: |
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data_files = dl_manager.download_and_extract(_URLs) |
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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={"data_files": dl_manager.iter_files(data_files["train"])}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"data_files": dl_manager.iter_files(data_files["validation"])}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"data_files": dl_manager.iter_files(data_files["test"])}, |
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), |
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] |
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def _standardize_arguments_roles(self, kb_example: Dict) -> Dict: |
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for event in kb_example["events"]: |
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for argument in event["arguments"]: |
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role = argument["role"] |
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argument["role"] = self._ROLE_MAPPING.get(role, role) |
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return kb_example |
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|
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def _generate_examples(self, data_files: Path): |
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|
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if self.config.schema == "source": |
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guid = 0 |
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for data_file in data_files: |
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txt_file = Path(data_file) |
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if txt_file.suffix != ".txt": |
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continue |
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example = parse_brat_file(txt_file) |
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example["id"] = str(guid) |
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yield guid, example |
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guid += 1 |
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elif self.config.schema == "bigbio_kb": |
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guid = 0 |
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for data_file in data_files: |
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txt_file = Path(data_file) |
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if txt_file.suffix != ".txt": |
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continue |
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example = brat_parse_to_bigbio_kb( |
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parse_brat_file(txt_file) |
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) |
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example = self._standardize_arguments_roles(example) |
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example["id"] = str(guid) |
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yield guid, example |
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guid += 1 |
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else: |
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raise ValueError(f"Invalid config: {self.config.name}") |
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