Convert dataset to Parquet (#3)

- Convert dataset to Parquet (06233c757e70673cf71249f47eea5166eb8d6288)
- Delete loading script (c5784feca89a65fd8742c09b860c04e8d51dc0c4)

README.md

@@ -32,10 +32,15 @@ dataset_info:
           '2': Neutral
   splits:
   - name: train
-    num_bytes: 1633423
+    num_bytes: 1633411
     num_examples: 20229
-  download_size: 1628349
-  dataset_size: 1633423
+  download_size: 1060033
+  dataset_size: 1633411
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for Roman Urdu Dataset

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:435cd809536b800ee0b92c0c0db344adc35fd0cf7916e9d058ae07880836bb1e
+size 1060033

roman_urdu.py

@@ -1,94 +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.
-"""Roman Urdu data corpus with 20,000 polarity labeled records"""
-
-
-import csv
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@InProceedings{Sharf:2018,
-title = "Performing Natural Language Processing on Roman Urdu Datasets",
-authors = "Zareen Sharf and Saif Ur Rahman",
-booktitle = "International Journal of Computer Science and Network Security",
-volume = "18",
-number = "1",
-pages = "141-148",
-year = "2018"
-}
-
-@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"
-}
-"""
-
-_DESCRIPTION = """\
-This is an extensive compilation of Roman Urdu Dataset (Urdu written in Latin/Roman script) tagged for sentiment analysis.
-"""
-
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set"
-
-_URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00458/Roman%20Urdu%20DataSet.csv"
-
-
-class RomanUrdu(datasets.GeneratorBasedBuilder):
-    """Roman Urdu sentences gathered from reviews of various e-commerce websites, comments on public Facebook pages, and twitter accounts, with positive, neutral, and negative polarity labels per each row."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "sentiment": datasets.features.ClassLabel(names=["Positive", "Negative", "Neutral"]),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="sentence", label_column="sentiment")],
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.reader(f, delimiter=",")
-            for id_, row in enumerate(reader):
-                yield id_, {
-                    "sentence": row[0],
-                    # 'Neative' typo in original dataset
-                    "sentiment": "Negative" if row[1] == "Neative" else row[1],
-                }