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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace NLP Authors.
#
# 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.
# Lint as: python3
"""tweetyface dataset."""
import json
import datasets
_DESCRIPTION = """\
DEBUG DATASET
"""
_HOMEPAGE = "https://github.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers"
URL = "https://raw.githubusercontent.com/ml-projects-kiel/OpenCampus-ApplicationofTransformers/develop/data/"
_URLs = {
"english": {
"train": URL + "tweetyface_en/train.json",
"validation": URL + "tweetyface_en/validation.json",
},
"german": {
"train": URL + "tweetyface_de/train.json",
"validation": URL + "tweetyface_de/validation.json",
},
}
_VERSION = "0.4.0"
_LICENSE = """
Apache License Version 2.0
"""
class TweetyFaceConfig(datasets.BuilderConfig):
"""BuilderConfig for TweetyFace."""
def __init__(self, **kwargs):
"""BuilderConfig for TweetyFace.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TweetyFaceConfig, self).__init__(**kwargs)
class TweetyFace(datasets.GeneratorBasedBuilder):
"""tweetyface"""
BUILDER_CONFIGS = [
TweetyFaceConfig(
name=lang,
description=f"{lang.capitalize()} Twitter Users",
version=datasets.Version(_VERSION),
)
for lang in _URLs.keys()
]
def _info(self):
if self.config.name == "english":
names = [
"MKBHD",
"elonmusk",
"alyankovic",
"Cristiano",
"katyperry",
"neiltyson",
"BillGates",
"BillNye",
"GretaThunberg",
"BarackObama",
"Trevornoah",
]
else:
names = [
"OlafScholz",
"Karl_Lauterbach",
"janboehm",
"Markus_Soeder",
]
return datasets.DatasetInfo(
description=_DESCRIPTION + self.config.description,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=names),
"idx": datasets.Value("string"),
"ref_tweet": datasets.Value("bool"),
"ref_type": datasets.Value("string"),
"reply_to": datasets.Value("string"),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": data_dir["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": data_dir["validation"]},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
with open(filepath, encoding="utf-8") as f:
for row in f:
data = json.loads(row)
idx = data["idx"]
yield idx, data
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