File size: 9,601 Bytes
586e978 391d4a9 586e978 3e9ef65 586e978 7bf90d1 586e978 3e9ef65 586e978 7bdaba4 b9ec98e 5e177db b9ec98e 786e261 6bb0ab7 b9ec98e d98fbce b9ec98e 3bd8cee 80f7843 3bd8cee 586e978 b9ec98e 586e978 b9ec98e e0a73f6 b9ec98e d98fbce 586e978 786e261 a40dcc3 cc80665 0a8b2de 586e978 e7be9f6 3e9ef65 586e978 070b7d8 4840694 7bf90d1 586e978 fe67c05 4840694 7bf90d1 586e978 070b7d8 3e9ef65 070b7d8 22ad057 586e978 8ed48f8 586e978 391d4a9 3e9ef65 391d4a9 e5233d0 391d4a9 965002e 391d4a9 4840694 391d4a9 4840694 965002e 4840694 586e978 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
# 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"
# 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",
},
)
]
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:
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,
}
|