Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- crowdsourced
+languages:
+- ar
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+- text-scoring
+task_ids:
+- sentiment-classification
+- sentiment-scoring
+---
+
+# Dataset Card for OCLAR
+
+## 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:** [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#)
+- **Paper:** [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0)
+- **Point of Contact:** Marwan Al Omari <marwanalomari '@' yahoo.com>
+
+### Dataset Summary
+
+The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon)
+on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc.
+The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers
+rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451
+texts.
+
+### Supported Tasks and Leaderboards
+
+Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services
+reviews, including hotels, restaurants, shops, and others.
+
+### Languages
+
+The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is
+the review left by the user / client , and a `rating` which is a score between 1 and 5.
+
+The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative.
+
+An example from the OCLAR data set looks as follows:
+
+```
+  "pagename": 'Ramlet Al Baida Beirut Lebanon',
+  "review": 'مكان يطير العقل ويساعد على الاسترخاء',
+  "rating": 5,
+```
+
+### Data Fields
+
+- `pagename`: string name of the service / location being reviewed
+- `review`: string review left by the user / costumer
+- `rating`: number of stars left by the reviewer. It ranges from 1 to 5.
+
+### Data Splits
+
+The data set comes in a single csv file of a total `3916` reviews :
+- `3465` are considered positive (a rating of 3 to 5)
+- `451` are considered negative (a rating of 1 or 2)
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country. 
+Reviews are about public services, including hotels, restaurants, shops, and others.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon)
+
+#### Who are the source language producers?
+
+The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon).
+They're mainly Arabic speaking Lebanese people.
+
+### Annotations
+
+#### Annotation process
+
+The dataset does not contain any additional annotations
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese
+context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings: 
+1) The classifier is confident when used to predict positive reviews,
+2) while it is biased on predicting reviews with negative sentiment, and finally 
+3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication,
+Lebanese University, Beirut, Lebanon;  Nacereddine Hammami from college of Computer and Information Sciences, 
+Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University,
+Beirut, Lebanon.
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+- Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com
+- Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb
+- Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa
+- Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb
+```
+@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" }
+
+@InProceedings{AlOmari2019oclar,
+title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},
+authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},
+year={2019}
+}
+```

dataset_infos.json

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+{"default": {"description": "The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato website \n(https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc.\nThe corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers\nrating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts.\n", "citation": "\n@misc{Dua:2019 ,\nauthor = \"Dua, Dheeru and Graff, Casey\",\nyear = \"2017\",\ntitle = \"{UCI} Machine Learning Repository\",\nurl = \"http://archive.ics.uci.edu/ml\",\ninstitution = \"University of California, Irvine, School of Information and Computer Sciences\" }\n\n@InProceedings{AlOmari2019oclar,\ntitle = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},\nauthors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},\nyear={2019}\n}\n", "homepage": "http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#", "license": "", "features": {"pagename": {"dtype": "string", "id": null, "_type": "Value"}, "review": {"dtype": "string", "id": null, "_type": "Value"}, "rating": {"dtype": "int8", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "oclar", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 398204, "num_examples": 3916, "dataset_name": "oclar"}}, "download_checksums": {"http://archive.ics.uci.edu/ml/machine-learning-databases/00499/OCLAR%20-%20Opinion%20Corpus%20for%20Lebanese%20Arabic%20Reviews.csv": {"num_bytes": 382976, "checksum": "78e0bc3d5accd737d3df45297f3f384a0238e2aa81006d546fa2cda92de12197"}}, "download_size": 382976, "post_processing_size": null, "dataset_size": 398204, "size_in_bytes": 781180}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c37690e3614177b10ab5b45712de3066ee6c01a31b7997e365216aa539fd55eb
+size 807

oclar.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.
+"""Opinion Corpus for Lebanese Arabic Reviews (OCLAR) Data Set"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import datasets
+
+
+_CITATION = """
+@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" }
+
+@InProceedings{AlOmari2019oclar,
+title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},
+authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},
+year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato
+website (https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops,
+etc.The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers
+rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about
+451 texts.
+"""
+
+_HOMEPAGE = "http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00499/OCLAR%20-%20Opinion%20Corpus%20for%20Lebanese%20Arabic%20Reviews.csv"
+
+
+class Oclar(datasets.GeneratorBasedBuilder):
+    """TOpinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on
+    services reviews, including hotels, restaurants, shops, and others.
+    """
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "pagename": datasets.Value("string"),
+                    "review": datasets.Value("string"),
+                    "rating": datasets.Value("int8"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_path,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """ Yields examples. """
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
+            next(csv_reader, None)  # skipping headers
+            for id_, row in enumerate(csv_reader):
+                pagename, review, rating = row
+                rating = int(rating)
+                yield id_, {"pagename": pagename, "review": review, "rating": rating}