Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- other
+languages:
+- ar
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1k<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- text-classification-other-question-identification
+---
+
+# Dataset Card for journalists_questions
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** http://qufaculty.qu.edu.qa/telsayed/datasets/
+- **Repository:** [Needs More Information]
+- **Paper:** https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Maram Hasanain]
+maram.hasanain@qu.edu.qa
+
+### Dataset Summary
+
+The journalists_questions dataset supports question identification over Arabic tweets of journalists.
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Arabic
+
+## Dataset Structure
+
+### Data Instances
+
+Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not.  A question tweet is a tweet that has at least one interrogative question.  Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label.
+Below is an example:
+{
+ 'tweet_id': '493235142128074753',
+ 'label': 'yes',
+ 'label_confidence':0.6359
+}
+
+
+### Data Fields
+
+tweet_id: the Twitter assigned ID for the tweet object.
+label: annotation of the tweet by whether it is a question or not
+label_confidence: confidence score for the label given annotations of multiple annotators per tweet
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"journalists_questions": {"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n", "citation": "@inproceedings{hasanain2016questions,\n  title={What Questions Do Journalists Ask on Twitter?},\n  author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},\n  booktitle={Tenth International AAAI Conference on Web and Social Media},\n  year={2016}\n}\n", "homepage": "http://qufaculty.qu.edu.qa/telsayed/datasets/", "license": "", "features": {"tweet_id": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 2, "names": ["no", "yes"], "names_file": null, "id": null, "_type": "ClassLabel"}, "label_confidence": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "journalists_questions", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 342296, "num_examples": 10077, "dataset_name": "journalists_questions"}}, "download_checksums": {"https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U": {"num_bytes": 271039, "checksum": "341b1fc8c9fc09458d1f2f24285c2492ca0a9c97302ff80381a4481c7a34b9bb"}}, "download_size": 271039, "post_processing_size": null, "dataset_size": 342296, "size_in_bytes": 613335}}

dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8261b30edf4a2172bc279f3f983f6abaa31b2ae73be9631a5d373a4a6d68fa1f
+size 689

journalists_questions.py

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+# 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.
+
+
+from __future__ import absolute_import, division, print_function
+
+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"]),
+                }