Convert dataset to Parquet (#2)

- Convert dataset to Parquet (e84b36c517c037ec6ac254aeebeaf4fba911c093)
- Delete loading script (22d221533ba783824e8b492b16eb0422b3480d0d)

README.md

@@ -19,7 +19,6 @@ task_ids:
 - text-scoring
 - sentiment-classification
 - sentiment-scoring
-paperswithcode_id: null
 pretty_name: OCLAR
 dataset_info:
   features:
@@ -31,10 +30,15 @@ dataset_info:
     dtype: int8
   splits:
   - name: train
-    num_bytes: 398204
+    num_bytes: 398196
     num_examples: 3916
-  download_size: 382976
-  dataset_size: 398204
+  download_size: 180384
+  dataset_size: 398196
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for OCLAR

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f32bb34a4de69303c6410e9484dd5122b91e2a271d92d44061860f7e9802ce7
+size 180384

oclar.py

@@ -1,98 +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.
-"""Opinion Corpus for Lebanese Arabic Reviews (OCLAR) Data Set"""
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """
-@misc{Dua:2019 ,
-author = "Dua, Dheeru and Graff, Casey",
-year = "2017",
-title = "{UCI} Machine Learning Repository",
-url = "http://archive.ics.uci.edu/ml",
-institution = "University of California, Irvine, School of Information and Computer Sciences" }
-
-@InProceedings{AlOmari2019oclar,
-title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},
-authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},
-year={2019}
-}
-"""
-
-_DESCRIPTION = """\
-The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato
-website (https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops,
-etc.The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers
-rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about
-451 texts.
-"""
-
-_HOMEPAGE = "http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00499/OCLAR%20-%20Opinion%20Corpus%20for%20Lebanese%20Arabic%20Reviews.csv"
-
-
-class Oclar(datasets.GeneratorBasedBuilder):
-    """TOpinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on
-    services reviews, including hotels, restaurants, shops, and others.
-    """
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "pagename": datasets.Value("string"),
-                    "review": datasets.Value("string"),
-                    "rating": datasets.Value("int8"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_path = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_path,
-                    "split": "train",
-                },
-            )
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
-            next(csv_reader, None)  # skipping headers
-            for id_, row in enumerate(csv_reader):
-                pagename, review, rating = row
-                rating = int(rating)
-                yield id_, {"pagename": pagename, "review": review, "rating": rating}