# 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 """NordicDSL: A language identification datasets for Nordic languages""" import csv import os import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @inproceedings{haas-derczynski-2021-discriminating, title = "Discriminating Between Similar Nordic Languages", author = "Haas, Ren{\'e} and Derczynski, Leon", booktitle = "Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.vardial-1.8", pages = "67--75", } """ _DESCRIPTION = """\ Automatic language identification is a challenging problem. Discriminating between closely related languages is especially difficult. This paper presents a machine learning approach for automatic language identification for the Nordic languages, which often suffer miscategorisation by existing state-of-the-art tools. Concretely we will focus on discrimination between six Nordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokmål), Faroese and Icelandic. This is the data for the tasks. Two variants are provided: 10K and 50K, with holding 10,000 and 50,000 examples for each language respectively. """ _URLS = { "10K": "nordic_dsl_10000", "50K": "nordic_dsl_50000", } class NordicLangIdConfig(datasets.BuilderConfig): """BuilderConfig for NordicLangId""" def __init__(self, **kwargs): """BuilderConfig NordicLangId. Args: **kwargs: keyword arguments forwarded to super. """ super(NordicLangIdConfig, self).__init__(**kwargs) class NordicLangId(datasets.GeneratorBasedBuilder): """NordicLangId dataset.""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ NordicLangIdConfig( name="10k", description="Data for distinguishing between similar Nordic languages: 10k examples per class", version=VERSION, ), NordicLangIdConfig( name="50k", description="Data for distinguishing between similar Nordic languages: 50k examples per class", version=VERSION, ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "sentence": datasets.Value("string"), "language": datasets.features.ClassLabel( names=[ "dk", "sv", "nb", "nn", "fo", "is", ] ), } ), supervised_keys=None, homepage="https://aclanthology.org/2021.vardial-1.8/", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" if self.config.name == "10k": downloaded_train = dl_manager.download(_URLS["10K"] + 'train.csv') downloaded_test = dl_manager.download(_URLS["10K"] + 'test.csv') elif self.config.name == "50k": downloaded_train = dl_manager.download(_URLS["50K"] + 'train.csv') downloaded_test = dl_manager.download(_URLS["50K"] + 'test.csv') return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_train}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_test}), ] def _generate_examples(self, filepath): logger.info("⏳ Generating examples from = %s", filepath) with open(filepath, encoding="utf-8") as f: guid = 0 for line in f: line = line.strip() if not line: continue if self.config.name == "10k": line = line.replace('dataset10000, ', '') if self.config.name == "50k": line = line.replace('dataset50000, ', '') instance = { "id": str(guid), "language": line[-2:], "sentence": line[:-3], } yield guid, instance guid += 1