# 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 from datasets import load_dataset 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://github.com/lunesco/conll2003-v2/raw/0f150c8a0c7137def22655f46f5514aed8e09d24/conll2003_v2.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): # 49, 23, 42 dlugosc (vs 47, 23, 9) 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=['VAFIN', 'PPOSAT', 'NN', 'APPR', 'ADV', 'VVINF', '$.', 'NE', 'CARD', 'TRUNC', 'XY', 'ADJA', 'ART', 'VVFIN', 'PPER', 'APPRART', '$[', 'VVPP', 'KON', '$,', 'PTKVZ', 'ADJD', 'PIAT', 'PRELS', 'PTKNEG', 'VAINF', 'VMFIN', 'PTKZU', 'PROAV', 'PIDAT', 'PDS', 'PWAV', 'PWS', 'KOUS', 'PIS', 'PRF', 'FM', 'ITJ', 'PTKANT', 'PDAT', 'VVIZU', 'PWAT', 'APZR', 'KOKOM', 'VVIMP', 'PTKA', 'KOUI', 'APPO', 'VAPP', 'VMINF'] # 50 ) ), "chunk_tags": datasets.Sequence( datasets.features.ClassLabel( names=['I-VA', 'I-PP', 'I-NN', 'I-AP', 'I-AD', 'I-VV', 'I-$.', 'I-NE', '-X-', 'I-CA', 'I-TR', 'I-XY', 'I-AR', 'I-$[', 'I-KO', 'I-$,', 'I-PT', 'I-PI', 'I-PR', 'I-VM', 'I-PD', 'I-PW', 'I-FM', 'I-IT'] # 24 ) ), "ner_tags": datasets.Sequence( datasets.features.ClassLabel( names=['O', 'B-organization-company', 'B-location-route', 'B-trigger', 'B-location-stop', 'B-date', 'B-location-city', 'B-event-cause', 'I-event-cause', 'B-time', 'I-time', 'B-number', 'B-organization', 'I-organization', 'B-location-street', 'I-trigger', 'B-location', 'I-location', 'I-location-city', 'I-organization-company', 'B-duration', 'I-duration', 'I-location-street', 'I-location-stop', 'I-location-route', 'B-person', 'I-date', 'B-set', 'B-money', 'I-person', 'I-money', 'B-distance', 'I-distance', 'I-number', 'B-disaster-type', 'B-org-position', 'I-org-position', 'I-set', 'B-percent', 'I-percent', 'I-disaster-type'] # 41 ) ), } ), 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, }