# 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 don'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 = { "train": None, # actual value set in the `_split_generators` method "validation": "validation.jsonl", "test": "test.jsonl" } _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 > 95: # No training data matches this quality criterion del _URLs["train"] else: _URLs["train"] = "train_{0}.jsonl.bz2".format(self.config.lang) 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", }, ) ] 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"], data_dir["train"], 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[0], "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: with open(filepath, encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) if data["lang"] == lang: yield id_, { "lang": data["lang"], "sent1": data["sent1"], "sent2": data["sent2"], "annot_score": data["annot_score"], "gem_id": data["gem_id"], "quality": 100, }