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:
+- found
+languages:
+- ur
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- sentiment-classification
+---
+
+# Dataset Card for Roman Urdu Dataset
+
+## 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
+
+- **Repository:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set)
+- **Point of Contact:** [Zareen Sharf](mailto:zareensharf76@gmail.com)
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Urdu
+
+## Dataset Structure
+
+[More Information Needed]
+
+### Data Instances
+
+```
+Wah je wah,Positive,
+```
+
+### Data Fields
+
+Each row consists of a short Urdu text, followed by a sentiment label. The labels are one of `Positive`, `Negative`, and `Neutral`. Note that the original source file is a comma-separated values file.
+
+* `sentence`: A short Urdu text
+* `label`: One of `Positive`, `Negative`, and `Neutral`, indicating the polarity of the sentiment expressed in the sentence
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### 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
+
+```
+@InProceedings{Sharf:2018,
+  title     = "Performing Natural Language Processing on Roman Urdu Datasets",
+  authors   = "Zareen Sharf and Saif Ur Rahman",
+  booktitle = "International Journal of Computer Science and Network Security",
+  volume    = "18",
+  number    = "1",
+  pages     = "141-148",
+  year      = "2018"
+}
+
+@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"
+}
+```

dataset_infos.json

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+{"default": {"description": "This is an extensive compilation of Roman Urdu Dataset (Urdu written in Latin/Roman script) tagged for sentiment analysis.\n", "citation": "@InProceedings{Sharf:2018,\ntitle = \"Performing Natural Language Processing on Roman Urdu Datasets\",\nauthors = \"Zareen Sharf and Saif Ur Rahman\",\nbooktitle = \"International Journal of Computer Science and Network Security\",\nvolume = \"18\",\nnumber = \"1\",\npages = \"141-148\",\nyear = \"2018\"\n}\n\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", "homepage": "https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentiment": {"num_classes": 3, "names": ["Positive", "Negative", "Neutral"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "roman_urdu_dataset", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1633423, "num_examples": 20229, "dataset_name": "roman_urdu_dataset"}}, "download_checksums": {"https://archive.ics.uci.edu/ml/machine-learning-databases/00458/Roman%20Urdu%20DataSet.csv": {"num_bytes": 1628349, "checksum": "d9b54d3685b6ef691cb4a2cc0783fc189dcfa0560cca9afec1099b015c798c7c"}}, "download_size": 1628349, "post_processing_size": null, "dataset_size": 1633423, "size_in_bytes": 3261772}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:def524b4fb553a8c1f835ac784007990c00b16a5221042b790c12e42d5b2447f
+size 404

roman_urdu.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.
+"""Roman Urdu data corpus with 20,000 polarity labeled records"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@InProceedings{Sharf:2018,
+title = "Performing Natural Language Processing on Roman Urdu Datasets",
+authors = "Zareen Sharf and Saif Ur Rahman",
+booktitle = "International Journal of Computer Science and Network Security",
+volume = "18",
+number = "1",
+pages = "141-148",
+year = "2018"
+}
+
+@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"
+}
+"""
+
+_DESCRIPTION = """\
+This is an extensive compilation of Roman Urdu Dataset (Urdu written in Latin/Roman script) tagged for sentiment analysis.
+"""
+
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set"
+
+_URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00458/Roman%20Urdu%20DataSet.csv"
+
+
+class RomanUrdu(datasets.GeneratorBasedBuilder):
+    """Roman Urdu sentences gathered from reviews of various e-commerce websites, comments on public Facebook pages, and twitter accounts, with positive, neutral, and negative polarity labels per each row."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "sentiment": datasets.features.ClassLabel(names=["Positive", "Negative", "Neutral"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.reader(f, delimiter=",")
+            for id_, row in enumerate(reader):
+                yield id_, {
+                    "sentence": row[0],
+                    # 'Neative' typo in original dataset
+                    "sentiment": "Negative" if row[1] == "Neative" else row[1],
+                }