Convert dataset to Parquet

README.md

@@ -20,6 +20,7 @@ task_ids:
 paperswithcode_id: hard
 pretty_name: Hotel Arabic-Reviews Dataset
 dataset_info:
+  config_name: plain_text
   features:
   - name: text
     dtype: string
@@ -32,13 +33,18 @@ dataset_info:
           '2': '3'
           '3': '4'
           '4': '5'
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 27507085
+    num_bytes: 27507041
     num_examples: 105698
-  download_size: 8508677
-  dataset_size: 27507085
+  download_size: 13607961
+  dataset_size: 27507041
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  default: true
 ---
 
 # Dataset Card for Hard

hard.py

@@ -1,105 +0,0 @@
-# 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
-"""Hotel Reviews in Arabic language"""
-
-
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-This dataset contains 93700 hotel reviews in Arabic language.\
-The hotel reviews were collected from Booking.com website during June/July 2016.\
-The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.\
-The following table summarize some tatistics on the HARD Dataset.
-"""
-
-_CITATION = """\
-@incollection{elnagar2018hotel,
-  title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},
-  author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
-  booktitle={Intelligent Natural Language Processing: Trends and Applications},
-  pages={35--52},
-  year={2018},
-  publisher={Springer}
-}
-"""
-
-_DOWNLOAD_URL = "https://raw.githubusercontent.com/elnagara/HARD-Arabic-Dataset/master/data/balanced-reviews.zip"
-
-
-class HardConfig(datasets.BuilderConfig):
-    """BuilderConfig for Hard."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for Hard.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(HardConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-
-
-class Hard(datasets.GeneratorBasedBuilder):
-    """Hard dataset."""
-
-    BUILDER_CONFIGS = [
-        HardConfig(
-            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=[
-                            "1",
-                            "2",
-                            "3",
-                            "4",
-                            "5",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/elnagara/HARD-Arabic-Dataset",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"directory": os.path.join(data_dir, "balanced-reviews.txt")}
-            ),
-        ]
-
-    def _generate_examples(self, directory):
-        """Generate examples."""
-        with open(directory, mode="r", encoding="utf-16") as file:
-            for id_, line in enumerate(file.read().splitlines()[1:]):
-                _, _, rating, _, _, _, review_text = line.split("\t")
-                yield str(id_), {"text": review_text, "label": rating}

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc1abe8fadfb59f6dcc2f933565a23f08af89814bf7f7946019ea5cc35245114
+size 13607961