Datasets:
License:
| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # 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. | |
| # TODO: Address all TODOs and remove all explanatory comments | |
| """Liv4ever dataset.""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @inproceedings{rikters-etal-2022, | |
| title = "Machine Translation for Livonian: Catering for 20 Speakers", | |
| author = "Rikters, Matīss and | |
| Tomingas, Marili and | |
| Tuisk, Tuuli and | |
| Valts, Ernštreits and | |
| Fishel, Mark", | |
| booktitle = "Proceedings of ACL 2022", | |
| year = "2022", | |
| address = "Dublin, Ireland", | |
| publisher = "Association for Computational Linguistics" | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. | |
| In this paper we tackle the task of developing neural machine translation (NMT) between Livonian and English, with a two-fold aim: on one hand, | |
| preserving the language and on the other – enabling access to Livonian folklore, lifestories and other textual intangible heritage as well as | |
| making it easier to create further parallel corpora. We rely on Livonian's linguistic similarity to Estonian and Latvian and collect parallel | |
| and monolingual data for the four languages for translation experiments. We combine different low-resource NMT techniques like zero-shot translation, | |
| cross-lingual transfer and synthetic data creation to reach the highest possible translation quality as well as to find which base languages are | |
| empirically more helpful for transfer to Livonian. The resulting NMT systems and the collected monolingual and parallel data, including a manually | |
| translated and verified translation benchmark, are publicly released. | |
| Fields: | |
| - source: source of the data | |
| - en: sentence in English | |
| - liv: sentence in Livonian | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "https://huggingface.co/datasets/tartuNLP/liv4ever" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "CC BY-NC-SA 4.0" | |
| # TODO: Add link to the official dataset URLs here | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URL = "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/" | |
| _URLS = { | |
| "train": _URL + "train.json", | |
| "dev": _URL + "dev.json", | |
| "test": _URL + "test.json", | |
| } | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class liv4ever(datasets.GeneratorBasedBuilder): | |
| """Liv4ever dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| features = datasets.Features( | |
| { | |
| "source": datasets.Value("string"), | |
| "en": datasets.Value("string"), | |
| "liv": datasets.Value("string"), | |
| "lv": datasets.Value("string"), | |
| "et": datasets.Value("string") | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| urls_to_download = _URLS | |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| with open(filepath, encoding="utf-8") as f: | |
| jsondata = json.load(f) | |
| n = 0 | |
| for source in jsondata: | |
| for sentence in source["sentences"]: | |
| # Yields examples as (key, example) tuples | |
| n=n+1 | |
| if source["source"] in ["trilium", "dictionary", "stalte"]: | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "lv": sentence["lv"], | |
| "et": sentence["et"], | |
| } | |
| elif source["source"] in ["facebook", "satversme"]: | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "lv": sentence["lv"], | |
| "et": sentence["et"], | |
| "en": sentence["en"], | |
| } | |