# 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 ] # There is no default configuration. User always needs to specify one: #DEFAULT_CONFIG_NAME = None def _info(self): # This method specifies the datasets.DatasetInfo object which # contains informations and typings for the dataset 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"), } ) 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, # 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=("sent1", "sent2"), # is this correct? # 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.""" # This method is tasked with downloading/extracting the data # and defining the splits depending on the configuration. # Several configurations are possible (listed in # BUILDER_CONFIGS), and the configuration selected by the user # is in self.config.name, which consists of two fields # separated by a period, containing the values of # self.config.lang and self.config.quality. if lang is None: # This is an error, nothing to do here return [] # Select which file of the training data contains the matching data: 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. # The validation and test sets are so small that we do not perform # any filtering or optimization at this stage. # dl_manager is a datasets.download.DownloadManager, which # downloads and extracts the 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.) 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 the desired quality value is 100, no subset of the # training set is good enough, and we only produce validation # and test sets, in order to save space and time. if self.config.quality <= 95: # In this case there is matching training data, so we produce # a train split. 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: # Training sets are in compressed bz2 files. # They contain a field "quality" missing from the validation and test sets. # We also 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. with bz2.open(filepath, "rt", encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) if data["quality"] < quality: # The rest of this file contains too low quality data, # because the data is sorted best first break yield id_, { "lang": data["lang"], "sent1": data["sent1"], "sent2": data["sent2"], "annot_score": 0.0, # means there is no annotation "gem_id": data["gem_id"], } else: # The validation and test sets are in jsonl files. # They contain the fields "lang" and "annot_score" that we filter on. # If we ask for the full sets, we will keep all data entries, also # the sentence pairs that were not considered paraphrases by the # annotators: 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: # only keep desired language if keep_all or data["annot_score"] >= 3.0: # for full sets keep all; # for standard test and validation sets, keep only # the paraphrases (annot_score >= 3.0 means "good # or mostly good example of paraphrases") yield id_, { "lang": data["lang"], "sent1": data["sent1"], "sent2": data["sent2"], "annot_score": data["annot_score"], "gem_id": data["gem_id"], }