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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets 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
"""CC-NEWS-ES-titles: Title generation from CC-NEWS in Spanish."""
import json
import datasets
from datasets.tasks import Summarization
logger = datasets.logging.get_logger(__name__)
_CITATION = """ """
_DESCRIPTION = ""
_HOMEPAGE = ""
_LICENSE = ""
_URL = "https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES-titles/resolve/main/"
_URLS = {
"train": _URL + "train.json",
"test": _URL + "test.json",
"eval": _URL + "eval.json"
}
class CCNewsESTitlesConfig(datasets.BuilderConfig):
"""BuilderConfig for CCNewsESTitles."""
def __init__(self, **kwargs):
"""BuilderConfig for CCNewsESTitles.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CCNewsESTitlesConfig, self).__init__(**kwargs)
class CCNewsESTitles(datasets.GeneratorBasedBuilder):
"""Title generation dataset in Spanish from CC-NEWS"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
CCNewsESTitlesConfig(
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"output_text": datasets.Value("string")
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train = dl_manager.download_and_extract(_URLS["train"])
eval_ = dl_manager.download_and_extract(_URLS["eval"])
test = dl_manager.download_and_extract(_URLS["test"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": eval_}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test})
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
data = []
with open(filepath) as f:
for line in f:
data.append(json.loads(line))
for idx, obs in enumerate(data):
yield idx, obs |