Create metrecv2.py

metrecv2.py

@@ -0,0 +1,114 @@
+# 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
+from datasets.tasks import TextClassification
+
+
+_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 = [
+        MetRecV2Config(
+            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",
+                            "mudhare",
+                            "muqtadheb",
+                            "prose"
+                        ]
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            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):
+        data_dir = 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)}