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") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'train') | |
| # data = datasets.load_dataset('my_dataset', 'dev') | |
| # BUILDER_CONFIGS = [ | |
| # datasets.BuilderConfig(name="train", version=VERSION, description="This part of my dataset covers a first domain"), | |
| # datasets.BuilderConfig(name="dev", version=VERSION, description="This part of my dataset covers a second domain"), | |
| # ] | |
| # DEFAULT_CONFIG_NAME = "train" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| 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") | |
| # 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): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| # urls = _URLS[self.config.name] | |
| # data_dir = dl_manager.download_and_extract(urls) | |
| # return [ | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TRAIN, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": os.path.join(data_dir), | |
| # "split": "train", | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.TEST, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": os.path.join(data_dir), | |
| # "split": "test" | |
| # }, | |
| # ), | |
| # datasets.SplitGenerator( | |
| # name=datasets.Split.VALIDATION, | |
| # # These kwargs will be passed to _generate_examples | |
| # gen_kwargs={ | |
| # "filepath": os.path.join(data_dir), | |
| # "split": "dev", | |
| # }, | |
| # ), | |
| # ] | |
| 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"]}), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath): | |
| # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| 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"] == "facebook" or source["source"] == "satversme": | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "lv": sentence["lv"], | |
| "fr": sentence["fr"], | |
| "en": sentence["en"], | |
| } | |
| if source["source"] == "songs": | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "lv": sentence["lv"], | |
| "et": sentence["et"], | |
| "en": sentence["en"], | |
| } | |
| if source["source"] == "trilium" or source["source"] == "dictionary" or source["source"] == "stalte": | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "lv": sentence["lv"], | |
| "et": sentence["et"], | |
| } | |
| if source["source"] == "vaari" or source["source"] == "luule": | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "et": sentence["et"], | |
| } | |
| if source["source"] == "jeful" and "et" in sentence: | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "et": sentence["et"], | |
| } | |
| if source["source"] == "jeful" and "en" in sentence: | |
| yield n, { | |
| "source": source["source"], | |
| "liv": sentence["liv"], | |
| "en": sentence["en"], | |
| } | |