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+ import os, datasets
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+
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+ _CITATION = """\
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+ @inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
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+ title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
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+ author = "Tjong Kim Sang, Erik F. and
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+ De Meulder, Fien",
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+ booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
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+ year = "2003",
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+ url = "https://www.aclweb.org/anthology/W03-0419",
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+ pages = "142--147",
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on
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+ four types of named entities: persons, locations, organizations and names of miscellaneous entities that do
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+ not belong to the previous three groups.
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+
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+ The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
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+ a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
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+ a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
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+ and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
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+ if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
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+ B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
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+ tagging scheme, whereas the original dataset uses IOB1.
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+
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+ For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
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+ """
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+
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+ _URL = "mydata.zip"
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+ _TRAINING_FILE = "train.txt"
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+ _DEV_FILE = "valid.txt"
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+ _TEST_FILE = "test.txt"
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+
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+
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+ class Conll2003Config(datasets.BuilderConfig):
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+ """BuilderConfig for Conll2003"""
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+
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+ def __init__(self, **kwargs):
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+ """BuilderConfig forConll2003.
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+
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(Conll2003Config, self).__init__(**kwargs)
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+
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+
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+ class Conll2003(datasets.GeneratorBasedBuilder):
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+ """Conll2003 dataset."""
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+
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+ BUILDER_CONFIGS = [
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+ Conll2003Config(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"),
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+ ]
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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("string"),
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+ "tokens": datasets.Sequence(datasets.Value("string")),
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+ "pos_tags": datasets.Sequence(
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+ datasets.features.ClassLabel(
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+ names=[
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+ '"',
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+ "''",
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+ "#",
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+ "$",
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+ "(",
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+ ")",
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+ ",",
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+ ".",
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+ ":",
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+ "``",
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+ "CC",
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+ "CD",
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+ "DT",
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+ "EX",
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+ "FW",
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+ "IN",
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+ "JJ",
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+ "JJR",
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+ "JJS",
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+ "LS",
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+ "MD",
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+ "NN",
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+ "NNP",
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+ "NNPS",
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+ "NNS",
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+ "NN|SYM",
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+ "PDT",
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+ "POS",
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+ "PRP",
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+ "PRP$",
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+ "RB",
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+ "RBR",
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+ "RBS",
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+ "RP",
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+ "SYM",
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+ "TO",
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+ "UH",
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+ "VB",
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+ "VBD",
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+ "VBG",
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+ "VBN",
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+ "VBP",
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+ "VBZ",
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+ "WDT",
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+ "WP",
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+ "WP$",
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+ "WRB",
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+ ]
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+ )
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+ ),
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+ "chunk_tags": datasets.Sequence(
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+ datasets.features.ClassLabel(
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+ names=[
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+ "O",
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+ "B-ADJP",
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+ "I-ADJP",
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+ "B-ADVP",
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+ "I-ADVP",
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+ "B-CONJP",
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+ "I-CONJP",
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+ "B-INTJ",
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+ "I-INTJ",
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+ "B-LST",
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+ "I-LST",
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+ "B-NP",
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+ "I-NP",
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+ "B-PP",
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+ "I-PP",
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+ "B-PRT",
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+ "I-PRT",
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+ "B-SBAR",
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+ "I-SBAR",
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+ "B-UCP",
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+ "I-UCP",
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+ "B-VP",
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+ "I-VP",
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+ ]
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+ )
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+ ),
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+ "ner_tags": datasets.Sequence(
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+ datasets.features.ClassLabel(
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+ names=[
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+ "PER",
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+ "ORG",
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+ "LOC",
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+ "MEDIA",
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+ "GEOPOLIT",
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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://www.aclweb.org/anthology/W03-0419/",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ downloaded_file = dl_manager.download_and_extract(_URL)
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+ data_files = {
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+ "train": os.path.join(downloaded_file, _TRAINING_FILE),
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+ "dev": os.path.join(downloaded_file, _DEV_FILE),
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+ "test": os.path.join(downloaded_file, _TEST_FILE),
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+ }
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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.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ with open(filepath, encoding="utf-8") as f:
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+ guid = 0
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+ tokens = []
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+ pos_tags = []
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+ chunk_tags = []
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+ ner_tags = []
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+ for line in f:
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+ if line.startswith("-DOCSTART-") or line == "" or line == "\n":
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+ if tokens:
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+ yield guid, {
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+ "id": str(guid),
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+ "tokens": tokens,
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+ "pos_tags": pos_tags,
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+ "chunk_tags": chunk_tags,
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+ "ner_tags": ner_tags,
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+ }
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+ guid += 1
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+ tokens = []
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+ pos_tags = []
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+ chunk_tags = []
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+ ner_tags = []
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+ else:
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+ # conll2003 tokens are space separated
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+ splits = line.split(" ")
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+ tokens.append(splits[0])
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+ ner_tags.append(splits[1].rstrip())
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+ # last example
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+ if tokens:
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+ yield guid, {
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+ "id": str(guid),
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+ "tokens": tokens,
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+ "pos_tags": pos_tags,
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+ "chunk_tags": chunk_tags,
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+ "ner_tags": ner_tags,
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+ }