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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: CC-NEWS in Spanish."""
import json
import os
import datasets
from datasets.tasks import Summarization
logger = datasets.logging.get_logger(__name__)
_CITATION = """ """
_DESCRIPTION = ""
_HOMEPAGE = ""
_LICENSE = ""
class CCNewsESConfig(datasets.BuilderConfig):
"""BuilderConfig for CCNewsES."""
def __init__(self, **kwargs):
"""BuilderConfig for CCNewsES.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(CCNewsESConfig, self).__init__(**kwargs)
class CCNewsES(datasets.GeneratorBasedBuilder):
"""Title generation dataset in Spanish from CC-NEWS"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
CCNewsESConfig(name=domain) for domain in ["ar","bo","br","cl","co","com","cr","es","gt","hn","mx","ni","pa","pe","pr","py","sv","uy","ve"]
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"country": datasets.Value("string"),
"text": datasets.Value("string"),
"id": datasets.Value("int32"),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
name = self.config.name
_URL = f"https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES/resolve/main/{name}.zip"
train = dl_manager.download_and_extract(_URL)
if name in ["com", "es", "mx"]:
files = os.listdir(train)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": [os.path.join(train, f) for f in files]})
]
else:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": [os.path.join(train, f"{name}.json")]})
]
def _generate_examples(self, filepath):
logger.info("generating examples from = %s", filepath)
data = []
for f in filepath:
with open(f, "r") as f:
data = json.load(f)
for idx, obs in enumerate(data):
yield idx, obs |