# coding=utf-8 # Copyright 2020 HuggingFace Datasets Authors. # # 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. # Lint as: python3 """Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition""" import os import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{tjong-kim-sang-de-meulder-2003-introduction, title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", author = "Tjong Kim Sang, Erik F. and De Meulder, Fien", booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", year = "2003", url = "https://www.aclweb.org/anthology/W03-0419", pages = "142--147", } """ _DESCRIPTION = """\ The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag 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 tagging scheme, whereas the original dataset uses IOB1. For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419 """ _URL = "https://data.deepai.org/conll2003.zip" _TRAINING_FILE = "train.txt" _DEV_FILE = "valid.txt" _TEST_FILE = "test.txt" class Conll2003Config(datasets.BuilderConfig): """BuilderConfig for Conll2003""" def __init__(self, **kwargs): """BuilderConfig forConll2003. Args: **kwargs: keyword arguments forwarded to super. """ super(Conll2003Config, self).__init__(**kwargs) class Conll2003(datasets.GeneratorBasedBuilder): """Conll2003 dataset.""" BUILDER_CONFIGS = [ Conll2003Config(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "pos_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ '"', "''", "#", "$", "(", ")", ",", ".", ":", "``", "CC", "CD", "DT", "EX", "FW", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NN", "NNP", "NNPS", "NNS", "NN|SYM", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB", ] ) ), "chunk_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-ADJP", "I-ADJP", "B-ADVP", "I-ADVP", "B-CONJP", "I-CONJP", "B-INTJ", "I-INTJ", "B-LST", "I-LST", "B-NP", "I-NP", "B-PP", "I-PP", "B-PRT", "I-PRT", "B-SBAR", "I-SBAR", "B-UCP", "I-UCP", "B-VP", "I-VP", ] ) ), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ] ) ), } ), supervised_keys=None, homepage="https://www.aclweb.org/anthology/W03-0419/", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" downloaded_file = dl_manager.download_and_extract(_URL) data_files = { "train": os.path.join(downloaded_file, _TRAINING_FILE), "dev": os.path.join(downloaded_file, _DEV_FILE), "test": os.path.join(downloaded_file, _TEST_FILE), } return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}), ] def _generate_examples(self, filepath): logger.info("⏳ Generating examples from = %s", filepath) with open(filepath, encoding="utf-8") as f: guid = 0 tokens = [] pos_tags = [] chunk_tags = [] ner_tags = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if tokens: yield guid, { "id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags, "ner_tags": ner_tags, } guid += 1 tokens = [] pos_tags = [] chunk_tags = [] ner_tags = [] else: # conll2003 tokens are space separated splits = line.split(" ") tokens.append(splits[0]) pos_tags.append(splits[1]) chunk_tags.append(splits[2]) ner_tags.append(splits[3].rstrip()) # last example if tokens: yield guid, { "id": str(guid), "tokens": tokens, "pos_tags": pos_tags, "chunk_tags": chunk_tags, "ner_tags": ner_tags, }