# 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"
"full-validation": "validation.jsonl",
"full-test": "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="full-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="full-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"],
"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 == "full-validation" || split == "full-test")
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,
}