Convert dataset to Parquet

README.md

@@ -49,10 +49,15 @@ dataset_info:
           '20': Time
   splits:
   - name: train
-    num_bytes: 5095721
+    num_bytes: 5095617
     num_examples: 258240
-  download_size: 17063406
-  dataset_size: 5095721
+  download_size: 1459014
+  dataset_size: 5095617
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for CANER

caner.py

@@ -1,122 +0,0 @@
-# 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.
-"""A new corpus of tagged data that can be useful for handling the issues in recognition of Classical Arabic named entities"""
-
-
-import csv
-import os
-
-import datasets
-
-
-_CITATION = """\
-@article{article,
-author = {Salah, Ramzi and Zakaria, Lailatul},
-year = {2018},
-month = {12},
-pages = {},
-title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
-volume = {96},
-journal = {Journal of Theoretical and Applied Information Technology}
-}
-"""
-
-_DESCRIPTION = """\
-Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
-"""
-
-_HOMEPAGE = "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-_URL = "https://raw.githubusercontent.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/master/CANERCorpus.csv"
-
-
-class Caner(datasets.GeneratorBasedBuilder):
-    """Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "token": datasets.Value("string"),
-                "ner_tag": datasets.ClassLabel(
-                    names=[
-                        "Allah",
-                        "Book",
-                        "Clan",
-                        "Crime",
-                        "Date",
-                        "Day",
-                        "Hell",
-                        "Loc",
-                        "Meas",
-                        "Mon",
-                        "Month",
-                        "NatOb",
-                        "Number",
-                        "O",
-                        "Org",
-                        "Para",
-                        "Pers",
-                        "Prophet",
-                        "Rlig",
-                        "Sect",
-                        "Time",
-                    ]
-                ),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        data_path = dl_manager.download(_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_path,
-                },
-            )
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as csv_file:
-            reader = csv.reader(csv_file, delimiter=",")
-            next(reader, None)
-
-            for id_, row in enumerate(reader):
-
-                yield id_, {
-                    "token": row[0],
-                    "ner_tag": row[1],
-                }

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68d710a709cefb398a3b44ac3d0629975fed8ba489747448c499c65baffc3de2
+size 1459014

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "Classical Arabic Named Entity Recognition corpus as a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.\n", "citation": "@article{article,\nauthor = {Salah, Ramzi and Zakaria, Lailatul},\nyear = {2018},\nmonth = {12},\npages = {},\ntitle = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},\nvolume = {96},\njournal = {Journal of Theoretical and Applied Information Technology}\n}\n", "homepage": "https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus", "license": "", "features": {"token": {"dtype": "string", "id": null, "_type": "Value"}, "ner_tag": {"num_classes": 21, "names": ["Allah", "Book", "Clan", "Crime", "Date", "Day", "Hell", "Loc", "Meas", "Mon", "Month", "NatOb", "Number", "O", "Org", "Para", "Pers", "Prophet", "Rlig", "Sect", "Time"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "caner", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5095721, "num_examples": 258240, "dataset_name": "caner"}}, "download_checksums": {"https://github.com/RamziSalah/Classical-Arabic-Named-Entity-Recognition-Corpus/archive/master.zip": {"num_bytes": 17063406, "checksum": "b4f6bbcc1074dfb9a6cf53fbbd5825a8eafbff842cd89ed20ab33f5b3ef6cddb"}}, "download_size": 17063406, "post_processing_size": null, "dataset_size": 5095721, "size_in_bytes": 22159127}}