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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
"""Arabic Poetry Metric v2 dataset."""
import os
import datasets
_DESCRIPTION = """\
"""
_CITATION = """\
"""
_DOWNLOAD_URL = "https://drive.google.com/uc?export=download&id=11iIHChBR7sVcUfGMnxfEAjbe7sSjzx5M"
class MetRecV2Config(datasets.BuilderConfig):
"""BuilderConfig for MetRecV2."""
def __init__(self, **kwargs):
"""BuilderConfig for MetRecV2.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(MetRecV2Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class MetRecV2(datasets.GeneratorBasedBuilder):
"""Metrec dataset."""
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="train_all", description="Full dataset"),
datasets.BuilderConfig(name="train_50k", description="Subset with 50K max baits per meter"),
]
DEFAULT_CONFIG_NAME = "train_all"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"label": datasets.features.ClassLabel(
names=[
"saree",
"kamel",
"mutakareb",
"mutadarak",
"munsareh",
"madeed",
"mujtath",
"ramal",
"baseet",
"khafeef",
"taweel",
"wafer",
"hazaj",
"rajaz",
"mudhare",
"muqtadheb",
"prose"
]
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
def _vocab_text_gen(self, archive):
for _, ex in self._generate_examples(archive, os.path.join("final_baits", "train.txt")):
yield ex["text"]
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
#data_dir = os.path.join(arch_path, "final_baits")
if self.config.name == "train_all":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "train.txt")}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"directory": os.path.join(data_dir, "test.txt")}
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "train_50k.txt")}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"directory": os.path.join(data_dir, "test.txt")}
),
]
def _generate_examples(self, directory, labeled=True):
"""Generate examples."""
# For labeled examples, extract the label from the path.
with open(directory, encoding="UTF-8") as f:
for id_, record in enumerate(f.read().splitlines()):
label, bait = record.split(" ", 1)
yield str(id_), {"text": bait, "label": int(label)} |