metrecv2.py

# 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)}