Add dataset loader

names.py

@@ -0,0 +1,81 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""Dataset that includes list of Arabic first names with meaning and origin of most names"""
+
+
+import csv
+import os
+
+import datasets
+
+_CITATION = """\
+@software{Al-Fetyani_Maha_Processing_Library_2021,
+author = {Al-Fetyani, Mohammad},
+month = {11},
+title = {{Maha Processing Library}},
+url = {https://github.com/TRoboto/Maha},
+year = {2021}
+}
+"""
+
+_DESCRIPTION = """\
+List of Arabic first names with meaning and origin of most names
+"""
+
+_TRAIN_FILE_NAME = "train.tsv"
+
+
+class Names(datasets.GeneratorBasedBuilder):
+    """List of Arabic first names with meaning and origin of most names"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "name": datasets.Value("string"),
+                    "description": datasets.Value("string"),
+                    "origin": datasets.Value("string"),
+                }
+            ),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join("data", _TRAIN_FILE_NAME),
+                    "split": datasets.Split.TRAIN,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.reader(f, delimiter="\t")
+            next(data, None)  # skip the headers
+            for row_id, row in enumerate(data):
+                # if any of the fields is missing, skip the row
+                if any(row[field] is None for field in row):
+                    continue
+                yield row_id, {
+                    "name": row["name"],
+                    "description": row["description"],
+                    "origin": row["origin"],
+                }