Convert dataset to Parquet (#3)

- Convert dataset to Parquet (de3609910e3ab5ae89dbafb419ba3b4c7f02b454)
- Delete loading script (ddb72186073289e346a7abf15307ac278cd31d6b)

README.md

@@ -20,6 +20,7 @@ pretty_name: JournalistsQuestions
 tags:
 - question-identification
 dataset_info:
+  config_name: plain_text
   features:
   - name: tweet_id
     dtype: string
@@ -31,13 +32,18 @@ dataset_info:
           '1': 'yes'
   - name: label_confidence
     dtype: float32
-  config_name: plain_text
   splits:
   - name: train
-    num_bytes: 342296
+    num_bytes: 342288
     num_examples: 10077
-  download_size: 271039
-  dataset_size: 342296
+  download_size: 195694
+  dataset_size: 342288
+configs:
+- config_name: plain_text
+  data_files:
+  - split: train
+    path: plain_text/train-*
+  default: true
 ---
 
 # Dataset Card for journalists_questions

journalists_questions.py

@@ -1,76 +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.
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{hasanain2016questions,
-  title={What Questions Do Journalists Ask on Twitter?},
-  author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
-  booktitle={Tenth International AAAI Conference on Web and Social Media},
-  year={2016}
-}
-"""
-
-_DESCRIPTION = """\
-The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
-tweets manually labeled for question identification over Arabic tweets posted by journalists.
-"""
-_DATA_URL = "https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U"
-
-
-class JournalistsQuestions(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Journalists tweet IDs and annotation by whether the tweet has a question",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "tweet_id": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["no", "yes"]),
-                    "label_confidence": datasets.Value("float"),
-                }
-            ),
-            homepage="http://qufaculty.qu.edu.qa/telsayed/datasets/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", fieldnames=["tweet_id", "label", "label_confidence"])
-            for idx, row in enumerate(reader):
-                yield idx, {
-                    "tweet_id": row["tweet_id"],
-                    "label": row["label"],
-                    "label_confidence": float(row["label_confidence"]),
-                }

plain_text/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b54a9ab8032c05555c284618b747e01692835b867aa0c717e60d2834847edaf
+size 195694