Update files from the datasets library (from 1.3.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+languages:
+- ar
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other-semeval_2017
+- extended|other-astd
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+- text-classification-other-sarcasm-detection
+---
+
+# Dataset Card for ArSarcasm
+
+## 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-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [GitHub](https://github.com/iabufarha/ArSarcasm)
+- **Paper:** https://www.aclweb.org/anthology/2020.osact-1.5/
+
+### Dataset Summary
+
+ArSarcasm is a new Arabic sarcasm detection dataset.
+The dataset was created using previously available Arabic sentiment analysis
+datasets ([SemEval 2017](https://www.aclweb.org/anthology/S17-2088.pdf)
+and [ASTD](https://www.aclweb.org/anthology/D15-1299.pdf)) and adds sarcasm and
+dialect labels to them.
+
+The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.
+
+For more details, please check the paper
+[From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://www.aclweb.org/anthology/2020.osact-1.5/)
+
+### Supported Tasks and Leaderboards
+
+You can get more information about an Arabic sarcasm tasks and leaderboard
+[here](https://sites.google.com/view/ar-sarcasm-sentiment-detection/).
+
+### Languages
+
+Arabic (multiple dialects)
+
+## Dataset Structure
+
+### Data Instances
+
+```javascript
+{'dialect': 1, 'original_sentiment': 0, 'sarcasm': 0, 'sentiment': 0, 'source': 'semeval', 'tweet': 'نصيحه ما عمرك اتنزل لعبة سوبر ماريو مش زي ما كنّا متوقعين الله يرحم ايامات السيقا والفاميلي #SuperMarioRun'}
+```
+
+### Data Fields
+
+- tweet: the original tweet text
+- sarcasm: 0 for non-sarcastic, 1 for sarcastic
+- sentiment: 0 for negative, 1 for neutral, 2 for positive
+- original_sentiment: 0 for negative, 1 for neutral, 2 for positive
+- source: the original source of tweet: SemEval or ASTD
+- dialect: 0 for Egypt, 1 for Gulf, 2 for Levant, 3 for Magreb, 4 for Modern Standard Arabic (MSA)
+
+### Data Splits
+
+The training set contains 8,437 tweets, while the test set contains 2,110 tweets.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them.
+
+#### Who are the source language producers?
+
+SemEval 2017 and ASTD
+
+### Annotations
+
+#### Annotation process
+
+For the annotation process, we used Figure-Eight
+crowdsourcing platform. Our main objective was to annotate the
+data for sarcasm detection, but due to the challenges imposed by dialectal variations, we decided to add the annotation for dialects. We also include a new annotation for
+sentiment labels in order to have a glimpse of the variability and subjectivity between different annotators. Thus, the
+annotators were asked to provide three labels for each tweet
+as the following:
+
+- Sarcasm: sarcastic or non-sarcastic.
+- Sentiment: positive, negative or neutral.
+- Dialect: Egyptian, Gulf, Levantine, Maghrebi or Modern Standard Arabic (MSA).
+
+#### Who are the annotators?
+
+Figure-Eight crowdsourcing platform
+
+### 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
+
+- Ibrahim Abu-Farha
+- Walid Magdy
+
+### Licensing Information
+
+MIT
+
+### Citation Information
+
+```
+@inproceedings{abu-farha-magdy-2020-arabic,
+    title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
+    author = "Abu Farha, Ibrahim  and Magdy, Walid",
+    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resource Association",
+    url = "https://www.aclweb.org/anthology/2020.osact-1.5",
+    pages = "32--39",
+    language = "English",
+    ISBN = "979-10-95546-51-1",
+}
+```
+
+### Contributions
+
+Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset.

ar_sarcasm.py

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+# 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.
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+# no BibTeX citation
+_CITATION = """@inproceedings{abu-farha-magdy-2020-arabic,
+    title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
+    author = "Abu Farha, Ibrahim  and Magdy, Walid",
+    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resource Association",
+    url = "https://www.aclweb.org/anthology/2020.osact-1.5",
+    pages = "32--39",
+    language = "English",
+    ISBN = "979-10-95546-51-1",
+}"""
+
+_DESCRIPTION = """\
+ArSarcasm is a new Arabic sarcasm detection dataset.
+The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD)
+ and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.
+"""
+
+_LICENSE = "MIT"
+
+_URLs = {
+    "default": "https://github.com/iabufarha/ArSarcasm/archive/master.zip",
+}
+
+
+class ArSarcasm(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "dialect": datasets.ClassLabel(names=["egypt", "gulf", "levant", "magreb", "msa"]),
+                "sarcasm": datasets.ClassLabel(names=["non-sarcastic", "sarcastic"]),
+                "sentiment": datasets.ClassLabel(names=["negative", "neutral", "positive"]),
+                "original_sentiment": datasets.ClassLabel(names=["negative", "neutral", "positive"]),
+                "tweet": datasets.Value("string"),
+                "source": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage="https://github.com/iabufarha/ArSarcasm",
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "ArSarcasm-master", "dataset", "ArSarcasm_train.csv"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "ArSarcasm-master", "dataset", "ArSarcasm_test.csv"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            rdr = csv.reader(f, delimiter=",")
+            next(rdr)
+            for id_, row in enumerate(rdr):
+                if len(row) < 6:
+                    continue
+                if row[4][0] == '"' and row[4][-1] == '"':
+                    row[4] = row[4][1:-1]
+                yield id_, {
+                    "dialect": row[0],
+                    "sarcasm": "sarcastic" if row[1] == "True" else "non-sarcastic",
+                    "sentiment": row[2],
+                    "original_sentiment": row[3],
+                    "tweet": row[4],
+                    "source": row[5],
+                }

dataset_infos.json

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+{"default": {"description": "ArSarcasm is a new Arabic sarcasm detection dataset.\nThe dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD)\n and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.\n", "citation": "@inproceedings{abu-farha-magdy-2020-arabic,\n    title = \"From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset\",\n    author = \"Abu Farha, Ibrahim  and Magdy, Walid\",\n    booktitle = \"Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection\",\n    month = may,\n    year = \"2020\",\n    address = \"Marseille, France\",\n    publisher = \"European Language Resource Association\",\n    url = \"https://www.aclweb.org/anthology/2020.osact-1.5\",\n    pages = \"32--39\",\n    language = \"English\",\n    ISBN = \"979-10-95546-51-1\",\n}", "homepage": "https://github.com/iabufarha/ArSarcasm", "license": "MIT", "features": {"dialect": {"num_classes": 5, "names": ["egypt", "gulf", "levant", "magreb", "msa"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sarcasm": {"num_classes": 2, "names": ["non-sarcastic", "sarcastic"], "names_file": null, "id": null, "_type": "ClassLabel"}, "sentiment": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "original_sentiment": {"num_classes": 3, "names": ["negative", "neutral", "positive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "tweet": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ar_sarcasm", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1829167, "num_examples": 8437, "dataset_name": "ar_sarcasm"}, "test": {"name": "test", "num_bytes": 458218, "num_examples": 2110, "dataset_name": "ar_sarcasm"}}, "download_checksums": {"https://github.com/iabufarha/ArSarcasm/archive/master.zip": {"num_bytes": 750717, "checksum": "a148877c4c933827d83b6e679880eeccf58b751c5ed5785f2f5a93aa950d0d41"}}, "download_size": 750717, "post_processing_size": null, "dataset_size": 2287385, "size_in_bytes": 3038102}}

dummy/1.0.0/dummy_data.zip

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