Delete loading script

metrec.py

@@ -1,124 +0,0 @@
-# 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 dataset."""
-
-
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Arabic Poetry Metric Classification.
-The dataset contains the verses and their corresponding meter classes.\
-Meter classes are represented as numbers from 0 to 13. \
-The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.\
-The train dataset contains 47,124 records and the test dataset contains 8316 records.
-"""
-
-_CITATION = """\
-@article{metrec2020,
-  title={MetRec: A dataset for meter classification of arabic poetry},
-  author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan},
-  journal={Data in Brief},
-  year={2020},
-  publisher={Elsevier}
-}
-"""
-
-_DOWNLOAD_URL = "https://raw.githubusercontent.com/zaidalyafeai/MetRec/master/baits.zip"
-
-
-class MetRecConfig(datasets.BuilderConfig):
-    """BuilderConfig for MetRec."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for MetRec.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MetRecConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class Metrec(datasets.GeneratorBasedBuilder):
-    """Metrec dataset."""
-
-    BUILDER_CONFIGS = [
-        MetRecConfig(
-            name="plain_text",
-            description="Plain text",
-        )
-    ]
-
-    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",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/zaidalyafeai/MetRec",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    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):
-        arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
-        data_dir = os.path.join(arch_path, "final_baits")
-        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")}
-            ),
-        ]
-
-    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)}