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"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition""" |
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import re |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@Article{SETH2016, |
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Title= {SETH detects and normalizes genetic variants in text.}, |
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Author= {Thomas, Philippe and Rockt{\"{a}}schel, Tim and Hakenberg, J{\"{o}}rg and Lichtblau, Yvonne and Leser, Ulf}, |
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Journal= {Bioinformatics}, |
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Year= {2016}, |
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Month= {Jun}, |
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Doi= {10.1093/bioinformatics/btw234}, |
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Language = {eng}, |
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Medline-pst = {aheadofprint}, |
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Pmid = {27256315}, |
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Url = {http://dx.doi.org/10.1093/bioinformatics/btw234} Titel anhand dieser DOI in Citavi-Projekt übernehmen |
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} |
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""" |
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_DESCRIPTION = """\ |
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This Dataset is used to for the Advanced Machine Learning and XAI course of the DHBW CAS in Heilbronn |
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""" |
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class SethConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Seth Dataset""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Seth. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(SethConfig, self).__init__(**kwargs) |
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class Seth(datasets.GeneratorBasedBuilder): |
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"""Seth dataset.""" |
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BUILDER_CONFIGS = [ |
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SethConfig(name="Seth", version=datasets.Version("1.0.0"), description="Seth dataset"), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("int32"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"labels": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-Gene", |
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"B-SNP", |
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"I-SNP", |
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"I-Gene", |
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"B-RS", |
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"I-RS" |
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] |
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) |
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) |
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} |
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), |
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supervised_keys=None, |
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homepage="https://rockt.github.io/SETH/", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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data_files = { |
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"train": "./SETH-train.iob", |
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"test": "./SETH-test.iob", |
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} |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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guid = 0 |
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document = {"id":None, |
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"tokens":[], |
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"labels":[] |
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} |
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documents = [] |
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pattern = r"#\d+" |
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for idx, line in enumerate(f): |
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match = re.match(pattern, line) |
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if idx == 0: |
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continue |
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if match: |
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if document["id"] != None: |
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documents.append(document) |
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yield guid,document |
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guid+=1 |
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document = {"id":int(line[1:]), |
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"tokens":[], |
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"labels":[] |
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} |
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else: |
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document = {"id":int(line[1:]), |
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"tokens":[], |
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"labels":[] |
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} |
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elif len(line.split(",")) >2: |
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if(line.split(",")[0] == "" and line.split(",")[1]==""): |
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document["tokens"].append(",") |
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document["labels"].append(line.split(",")[2].split("\n")[0]) |
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else: |
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document["tokens"].append(",".join(line.split(",")[0:-1])) |
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document["labels"].append(line.split(",")[-1].split("\n")[0]) |
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else: |
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word_tag = line.split(",") |
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if word_tag[0] == " " and word_tag[1] == " \n": |
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continue |
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document["tokens"].append(word_tag[0]) |
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document["labels"].append(word_tag[1].split("\n")[0]) |
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documents.append(document) |
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yield guid,document |