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README.md

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----
-annotations_creators:
-- found
-language_creators:
-- machine-generated
-language:
-- ur
-license:
-- odbl
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- text-classification
-task_ids:
-- sentiment-classification
-paperswithcode_id: null
-pretty_name: ImDB Urdu Reviews
-dataset_info:
-  features:
-  - name: sentence
-    dtype: string
-  - name: sentiment
-    dtype:
-      class_label:
-        names:
-          0: positive
-          1: negative
-  splits:
-  - name: train
-    num_bytes: 114670811
-    num_examples: 50000
-  download_size: 31510992
-  dataset_size: 114670811
----
-
-# Dataset Card for ImDB Urdu Reviews
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#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
-
-- **Homepage:** [Github](https://github.com/mirfan899/Urdu)
-- **Repository:** [Github](https://github.com/mirfan899/Urdu)
-- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
-- **Leaderboard:**
-- **Point of Contact:** [Ikram Ali](https://github.com/akkefa)
-
-### Dataset Summary
-
-[More Information Needed]
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
-
-### Languages
-
-[More Information Needed]
-
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-- sentence: The movie review which was translated into Urdu.
-- sentiment: The sentiment exhibited in the review, either positive or negative.
-
-### Data Splits
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### 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
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset.

dataset_infos.json

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-{"default": {"description": "\nLarge Movie translated Urdu Reviews Dataset.\nThis is a dataset for binary sentiment classification containing substantially more data than previous\nbenchmark datasets. We provide a set of 40,000 highly polar movie reviews for training, and 10,000 for testing.\nTo increase the availability of sentiment analysis dataset for a low recourse language like Urdu,\nwe opted to use the already available IMDB Dataset. we have translated this dataset using google translator.\nThis is a binary classification dataset having two classes as positive and negative.\nThe reason behind using this dataset is high polarity for each class.\nIt contains 50k samples equally divided in two classes.\n", "citation": "\n@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n  author    = {Maas, Andrew L. and Daly,nRaymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y...},\n  title     = {Learning Word Vectors for Sentiment Analysis},\n  month     = {June},\n  year      = {2011},\n  address   = {Portland, Oregon, USA},\n  publisher = {Association for Computational Linguistics},\n  pages     = {142--150},\n  url       = {http://www.aclweb.org/anthology/P11-1015}\n}\n", "homepage": "https://github.com/mirfan899/Urdu", "license": "", "features": {"sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentiment": {"num_classes": 2, "names": ["positive", "negative"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "text-classification", "text_column": "sentence", "label_column": "sentiment", "labels": ["negative", "positive"]}], "builder_name": "imdb_urdu_reviews", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 114670811, "num_examples": 50000, "dataset_name": "imdb_urdu_reviews"}}, "download_checksums": {"https://github.com/mirfan899/Urdu/blob/master/sentiment/imdb_urdu_reviews.csv.tar.gz?raw=true": {"num_bytes": 31510992, "checksum": "f60f7e9972661dc5d8ec1c867972ae35f86dac32de43a274a2a794095dccdf99"}}, "download_size": 31510992, "post_processing_size": null, "dataset_size": 114670811, "size_in_bytes": 146181803}}

default/imdb_urdu_reviews-train.parquet

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imdb_urdu_reviews.py

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-"""IMDB Urdu movie reviews dataset."""
-
-
-import csv
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """
-@InProceedings{maas-EtAl:2011:ACL-HLT2011,
-  author    = {Maas, Andrew L. and Daly,nRaymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y...},
-  title     = {Learning Word Vectors for Sentiment Analysis},
-  month     = {June},
-  year      = {2011},
-  address   = {Portland, Oregon, USA},
-  publisher = {Association for Computational Linguistics},
-  pages     = {142--150},
-  url       = {http://www.aclweb.org/anthology/P11-1015}
-}
-"""
-
-_DESCRIPTION = """
-Large Movie translated Urdu Reviews Dataset.
-This is a dataset for binary sentiment classification containing substantially more data than previous
-benchmark datasets. We provide a set of 40,000 highly polar movie reviews for training, and 10,000 for testing.
-To increase the availability of sentiment analysis dataset for a low recourse language like Urdu,
-we opted to use the already available IMDB Dataset. we have translated this dataset using google translator.
-This is a binary classification dataset having two classes as positive and negative.
-The reason behind using this dataset is high polarity for each class.
-It contains 50k samples equally divided in two classes.
-"""
-
-_URL = "https://github.com/mirfan899/Urdu/blob/master/sentiment/imdb_urdu_reviews.csv.tar.gz?raw=true"
-
-_HOMEPAGE = "https://github.com/mirfan899/Urdu"
-
-
-class ImdbUrduReviews(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "sentiment": datasets.ClassLabel(names=["positive", "negative"]),
-                }
-            ),
-            citation=_CITATION,
-            homepage=_HOMEPAGE,
-            task_templates=[TextClassification(text_column="sentence", label_column="sentiment")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        dl_path = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_path, "imdb_urdu_reviews.csv")}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.reader(f, delimiter=",")
-            for id_, row in enumerate(reader):
-                if id_ == 0:
-                    continue
-                yield id_, {"sentiment": row[1], "sentence": row[0]}