| # coding=utf-8 | |
| # 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: Add a description here.""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| import bz2 | |
| # Add BibTeX citation | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {A great new dataset}, | |
| author={huggingface, Inc. | |
| }, | |
| year={2020} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| Test adding a dataset with challenge set to GEM benchmark . | |
| """ | |
| _HOMEPAGE = "" | |
| _LICENSE = "" | |
| # The HuggingFace dataset library doesn't host the datasets but only point to the original files | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URLs = { | |
| "validation": "validation.jsonl", | |
| "test": "test.jsonl", | |
| "validation.full": "validation.jsonl", | |
| "test.full": "test.jsonl", | |
| # NB: the "train" split file is defined dynamically inside the `_split_generators` method | |
| } | |
| _VERSION = datasets.Version("1.0.0", "") | |
| class OpusparcusConfig(datasets.BuilderConfig): | |
| """BuilderConfig for Opusparcus.""" | |
| def __init__(self, lang=None, quality=100, **kwargs): | |
| """BuilderConfig for Wikipedia. | |
| Args: | |
| language: string, the language code for the Wikipedia dump to use. | |
| date: string, date of the Wikipedia dump in YYYYMMDD format. A list of | |
| available dates can be found at https://dumps.wikimedia.org/enwiki/. | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(OpusparcusConfig, self).__init__( | |
| name="{0}.{1}".format(lang, quality), | |
| description="Opusparcus dataset for {0}".format(lang), | |
| **kwargs, | |
| ) | |
| self.lang = lang | |
| self.quality = quality | |
| LANGS = [ "de", "en", "fi", "fr", "ru", "sv" ] | |
| QUALITIES = [ 100, 95, 90, 85, 80, 75, 70, 65, 60 ] | |
| class Opusparcus(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| # 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 = OpusparcusConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| BUILDER_CONFIGS = [ | |
| OpusparcusConfig(lang=lang, quality=quality, version=_VERSION) for lang in LANGS for quality in QUALITIES | |
| ] | |
| #DEFAULT_CONFIG_NAME = "test" # 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 | |
| #if self.config.name == "test": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features( | |
| { | |
| "lang": datasets.Value("string"), | |
| "sent1": datasets.Value("string"), | |
| "sent2": datasets.Value("string"), | |
| "annot_score": datasets.Value("float"), | |
| "gem_id": datasets.Value("string"), | |
| "quality": datasets.Value("uint8") | |
| } | |
| ) | |
| 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, | |
| # specify them here. They'll be used if as_supervised=True in | |
| # builder.as_dataset. | |
| supervised_keys=None, | |
| # 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): | |
| """Returns SplitGenerators.""" | |
| # 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 | |
| if self.config.quality < 70: | |
| # We need to retrieve the largest training set file | |
| # containing the full training set for the desired language | |
| _URLs["train"] = "train_{0}.60.jsonl.bz2".format(self.config.lang) | |
| elif self.config.quality <= 95: | |
| # We can do with a smaller version of the training set | |
| # for the desired language | |
| _URLs["train"] = "train_{0}.70.jsonl.bz2".format(self.config.lang) | |
| # Otherwise, if the desired quality is above 95, we do not | |
| # download any training data, because there is no matching data | |
| data_dir = dl_manager.download_and_extract(_URLs) | |
| splits = [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "lang": self.config.lang, | |
| "quality": 100, | |
| "filepath": data_dir["test"], | |
| "split": "test" | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "lang": self.config.lang, | |
| "quality": 100, | |
| "filepath": data_dir["validation"], | |
| "split": "validation", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="test.full", | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "lang": self.config.lang, | |
| "quality": 100, | |
| "filepath": data_dir["test.full"], | |
| "split": "test.full" | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name="validation.full", | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "lang": self.config.lang, | |
| "quality": 100, | |
| "filepath": data_dir["validation.full"], | |
| "split": "validation.full", | |
| }, | |
| ), | |
| ] | |
| if self.config.quality <= 95: | |
| # We do have training data as well | |
| splits.append( | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "lang": self.config.lang, | |
| "quality": self.config.quality, | |
| "filepath": data_dir["train"], | |
| "split": "train", | |
| }, | |
| ) | |
| ) | |
| return splits | |
| def _generate_examples( | |
| self, lang, quality, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| ): | |
| """ Yields examples as (key, example) tuples. """ | |
| # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is here for legacy reason (tfds) and is not important in itself. | |
| if split == datasets.Split.TRAIN: | |
| with bz2.open(filepath, "rt", encoding="utf-8") as f: | |
| # We know that this file only contains the desired language, | |
| # because for the training sets the languages are in separate | |
| # files, and only the desired language has been downloaded | |
| for id_, row in enumerate(f): | |
| data = json.loads(row) | |
| if data["quality"] < quality: | |
| # The rest of this file contains too low quality data | |
| break | |
| yield id_, { | |
| "lang": data["lang"], | |
| "sent1": data["sent1"], | |
| "sent2": data["sent2"], | |
| "annot_score": 0.0, | |
| "gem_id": data["gem_id"], | |
| "quality": data["quality"], | |
| } | |
| else: | |
| keep_all = (split == "validation.full" or split == "test.full") | |
| with open(filepath, encoding="utf-8") as f: | |
| for id_, row in enumerate(f): | |
| data = json.loads(row) | |
| if data["lang"] == lang: | |
| if keep_all or data["annot_score"] >= 3.0: | |
| yield id_, { | |
| "lang": data["lang"], | |
| "sent1": data["sent1"], | |
| "sent2": data["sent2"], | |
| "annot_score": data["annot_score"], | |
| "gem_id": data["gem_id"], | |
| "quality": 100, | |
| } | |