| ---
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| license: other
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| license_name: per-domain
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| license_link: LICENSE.md
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| language:
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| - ur
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| task_categories:
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| - text-classification
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| tags:
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| - urdu
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| - roman-urdu
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| - domain-robustness
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| - benchmark
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| pretty_name: Urdu Multi-Domain Benchmark (v1.1)
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| size_categories:
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| - 100K<n<1M
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| configs:
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| - config_name: N-SA_SA-B
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| data_files:
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| - split: train
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| path: "data/Nastaliq/Sentiment Analysis/Domain_B_Movie_Reviews/train.csv"
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| - split: test
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| path: "data/Nastaliq/Sentiment Analysis/Domain_B_Movie_Reviews/test.csv"
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| - config_name: N-SA_SA-E
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| data_files:
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| - split: train
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| path: "data/Nastaliq/Sentiment Analysis/Domain_E_Socioeconomic_Agricultural/train.csv"
|
| - split: test
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| path: "data/Nastaliq/Sentiment Analysis/Domain_E_Socioeconomic_Agricultural/test.csv"
|
| - config_name: N-HS_HS-B
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| data_files:
|
| - split: train
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| path: "data/Nastaliq/Hate Speech/Domain_B_Social_Media_Hate_Speech/train.csv"
|
| - split: test
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| path: "data/Nastaliq/Hate Speech/Domain_B_Social_Media_Hate_Speech/test.csv"
|
| - config_name: N-HS_HS-C
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| data_files:
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| - split: train
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| path: "data/Nastaliq/Hate Speech/Domain_C_Politics_Sports_Health_Family/train.csv"
|
| - split: test
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| path: "data/Nastaliq/Hate Speech/Domain_C_Politics_Sports_Health_Family/test.csv"
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| - config_name: N-HS_HS-D
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| data_files:
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| - split: train
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| path: "data/Nastaliq/Hate Speech/Domain_D_Sports_Labor_Arts_Education/train.csv"
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| - split: test
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| path: "data/Nastaliq/Hate Speech/Domain_D_Sports_Labor_Arts_Education/test.csv"
|
| - config_name: N-QA_QA-A
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| data_files:
|
| - split: train
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| path: "data/Nastaliq/QA/Domain_A_Religious_Hadith/train.csv"
|
| - split: test
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| path: "data/Nastaliq/QA/Domain_A_Religious_Hadith/test.csv"
|
| - config_name: N-QA_QA-B
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| data_files:
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| - split: train
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| path: "data/Nastaliq/QA/Domain_B_General_Knowledge/train.csv"
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| - split: test
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| path: "data/Nastaliq/QA/Domain_B_General_Knowledge/test.csv"
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| - config_name: N-QA_QA-C
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| data_files:
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| - split: train
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| path: "data/Nastaliq/QA/Domain_C_Long_Form_QnA/train.csv"
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| - split: test
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| path: "data/Nastaliq/QA/Domain_C_Long_Form_QnA/test.csv"
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| - config_name: N-QA_QA-D
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| data_files:
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| - split: train
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| path: "data/Nastaliq/QA/Domain_D_Short_Form_QnA/train.csv"
|
| - split: test
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| path: "data/Nastaliq/QA/Domain_D_Short_Form_QnA/test.csv"
|
| - config_name: R-SA_SA-A
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| data_files:
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| - split: train
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| path: "data/Roman/Sentiment Analysis/Domain_A_Mixed_Public_Discourse/train.csv"
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| - split: test
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| path: "data/Roman/Sentiment Analysis/Domain_A_Mixed_Public_Discourse/test.csv"
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| - config_name: R-SA_SA-B
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| data_files:
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| - split: train
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| path: "data/Roman/Sentiment Analysis/Domain_B_Social_Media_Politics_Drama/train.csv"
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| - split: test
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| path: "data/Roman/Sentiment Analysis/Domain_B_Social_Media_Politics_Drama/test.csv"
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| - config_name: R-SA_SA-C
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| data_files:
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| - split: train
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| path: "data/Roman/Sentiment Analysis/Domain_C_YouTube_Entertainment/train.csv"
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| - split: test
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| path: "data/Roman/Sentiment Analysis/Domain_C_YouTube_Entertainment/test.csv"
|
| - config_name: R-CA_CA-A
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| data_files:
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| - split: train
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_A_YouTube_Comments/train.csv"
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| - split: test
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_A_YouTube_Comments/test.csv"
|
| - config_name: R-CA_CA-B
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| data_files:
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| - split: train
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_B_Content_Creator_Comments/train.csv"
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| - split: test
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_B_Content_Creator_Comments/test.csv"
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| - config_name: R-CA_CA-C
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| data_files:
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| - split: train
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_C_Social_Media_Diverse_Vocab/train.csv"
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| - split: test
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_C_Social_Media_Diverse_Vocab/test.csv"
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| - config_name: R-CA_CA-D
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| data_files:
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| - split: train
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_D_General_Online_Video/train.csv"
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| - split: test
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| path: "data/Roman/Cyber Abuse or Abusive Language/Domain_D_General_Online_Video/test.csv"
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| - config_name: R-P3_PR-A
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| data_files:
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| - split: train
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| path: "data/Roman/Product or eCommerce Reviews/Domain_A_Daraz_Ecommerce/train.csv"
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| - split: test
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| path: "data/Roman/Product or eCommerce Reviews/Domain_A_Daraz_Ecommerce/test.csv"
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| - config_name: R-P3_PR-B
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| data_files:
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| - split: train
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| path: "data/Roman/Product or eCommerce Reviews/Domain_B_Restaurant_Food/train.csv"
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| - split: test
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| path: "data/Roman/Product or eCommerce Reviews/Domain_B_Restaurant_Food/test.csv"
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| - config_name: R-P3_PR-D
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| data_files:
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| - split: train
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| path: "data/Roman/Product or eCommerce Reviews/Domain_D_Electronics_Gaming_Delivery/train.csv"
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| - split: test
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| path: "data/Roman/Product or eCommerce Reviews/Domain_D_Electronics_Gaming_Delivery/test.csv"
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| - config_name: R-P3_TW-B
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| data_files:
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| - split: train
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| path: "data/Roman/Twitter or Social Media Opinions/Domain_B_Consumer_Electronics_Gaming/train.csv"
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| - split: test
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| path: "data/Roman/Twitter or Social Media Opinions/Domain_B_Consumer_Electronics_Gaming/test.csv"
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| - config_name: R-P3_TW-C
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| data_files:
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| - split: train
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| path: "data/Roman/Twitter or Social Media Opinions/Domain_C_Public_Civic_Services/train.csv"
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| - split: test
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| path: "data/Roman/Twitter or Social Media Opinions/Domain_C_Public_Civic_Services/test.csv"
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| - config_name: R-P3_TW-D
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| data_files:
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| - split: train
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| path: "data/Roman/Twitter or Social Media Opinions/Domain_D_Mixed_Social_Commentary/train.csv"
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| - split: test
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| path: "data/Roman/Twitter or Social Media Opinions/Domain_D_Mixed_Social_Commentary/test.csv"
|
| ---
|
|
|
| # Urdu Multi-Domain Benchmark (v1.1)
|
|
|
| A benchmark for **domain robustness** of Urdu text classifiers: 26 datasets in two scripts
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| (Nastaliq and Roman Urdu), grouped into 7 transfer matrices over four task types (sentiment and
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| polarity, abusive language, fake news, question-answer pair validation). All domains of a matrix
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| share one label space, so every source-to-target pair is a valid domain shift.
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|
|
| - Paper: *Domain Robustness of Multilingual NLP Models Across Urdu and Roman Urdu Scripts*
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| (Haroon and Bashir, FAST-NUCES).
|
| - Code, audits and model predictions: https://github.com/AbdullahPatti/Urdu-Multi-Domain-Script-Research
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| - DOI: https://doi.org/10.5281/zenodo.23053495
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|
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| > **Content warning.** The abusive-language and hate-speech domains contain offensive, hateful and
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| > sectarian text. Classifiers trained on these data should not be deployed without human review.
|
|
|
| ## What changed from v1.0
|
|
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| v1.1 supersedes v1.0 (DOI 10.5281/zenodo.22195610). Domains were selected by explicit inclusion
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| criteria, and every split is free of exact and near-duplicate train-test overlap:
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|
|
| - **Excluded candidates** (duplicated sources, labels predictable from the origin of an item,
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| fewer than 100 test items):
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| - `Nastaliq/Hate Speech/Domain_E_Inter_Faith_Sectarian_Ethnic`: same tweets and labels as Hate Speech A (ISE Level-1 re-uses the 22K Offensive pool)
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| - `Nastaliq/Sentiment Analysis/Domain_D_Governance_Political`: same texts and labels as Sentiment A
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| - `Roman/Sentiment Analysis/Domain_D_Utilities_Apps_Courses`: same 11,000-review corpus as Roman Sentiment B
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| - `Roman/Twitter or Social Media Opinions/Domain_A_Twitter_Political_Social`: positive/negative items are the RUSAD corpus (= Roman Sentiment B); all neutral items come from a different source, so the label is predictable from the source
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| - `Nastaliq/Fake News Detection/Domain_C_General_Pakistani_News`: fake and real items come from two separate files; fake items are largely fact-check verdicts, so the label is predictable from the source
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| - `Nastaliq/Fake News Detection/Domain_D_Fact_Checking_Platform`: real items carry an outlet dateline ("92 News", 90% of real vs 3.5% of fake items), so the label is predictable from the source
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| - `Nastaliq/Sentiment Analysis/Domain_C_Civic_Social_Topics`: too small to evaluate (162 items, 33 in test; minimum is 100 test items)
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| - **Near-duplicate-aware splits.** LLM-generated domains were re-split so that clusters of
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| template near-duplicates (character 5-gram Jaccard >= 0.8) do not straddle train and test.
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| - **Question-grouped QA splits.** No question (or near-duplicate question) occurs in both splits.
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| - **Cross-domain overlap** removed inside each matrix.
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|
|
| ## Domains
|
|
|
| | Matrix | ID | Domain | Origin | Train | Test | Licence | Distribution |
|
| |---|---|---|---|---:|---:|---|---|
|
| | N-SA | SA-A | Twitter Social Media | organic | 781 | 194 | none stated (citation requested) | rebuild |
|
| | N-SA | SA-B | Movie Reviews | translated | 6,695 | 1,638 | ODbL 1.0 | file |
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| | N-SA | SA-E | Socioeconomic Agricultural | generated | 1,629 | 407 | xAI terms (attribute Grok) | file |
|
| | N-HS | HS-A | Social Media Offensive | organic | 17,152 | 4,133 | none stated | rebuild |
|
| | N-HS | HS-B | Social Media Hate Speech | organic | 18,486 | 3,540 | MIT | file |
|
| | N-HS | HS-C | Politics Sports Health Family | generated | 1,360 | 340 | xAI terms (attribute Grok) | file |
|
| | N-HS | HS-D | Sports Labor Arts Education | generated | 1,356 | 339 | xAI terms (attribute Grok) | file |
|
| | N-FN | FN-A | Multi Topic News | organic | 634 | 260 | none stated | rebuild |
|
| | N-FN | FN-B | Ax to Grind | organic | 7,217 | 1,645 | none stated | rebuild |
|
| | N-QA | QA-A | Religious Hadith | organic | 992 | 250 | MIT | file |
|
| | N-QA | QA-B | General Knowledge | organic | 7,564 | 1,889 | MIT | file |
|
| | N-QA | QA-C | Long Form QnA | generated | 896 | 224 | xAI terms (attribute Grok) | file |
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| | N-QA | QA-D | Short Form QnA | generated | 2,764 | 691 | xAI terms (attribute Grok) | file |
|
| | R-SA | SA-A | Mixed Public Discourse | organic | 19,262 | 2,140 | MIT | file |
|
| | R-SA | SA-B | Social Media Politics Drama | organic | 8,781 | 2,163 | CC BY 4.0 | file |
|
| | R-SA | SA-C | YouTube Entertainment | organic | 15,587 | 3,759 | unknown | file |
|
| | R-CA | CA-A | YouTube Comments | uncertain | 3,469 | 866 | unknown | file |
|
| | R-CA | CA-B | Content Creator Comments | generated | 1,040 | 260 | xAI terms (attribute Grok) | file |
|
| | R-CA | CA-C | Social Media Diverse Vocab | generated | 1,040 | 260 | xAI terms (attribute Grok) | file |
|
| | R-CA | CA-D | General Online Video | generated | 960 | 240 | xAI terms (attribute Grok) | file |
|
| | R-P3 | PR-A | Daraz Ecommerce | organic | 13,489 | 3,294 | CC BY 4.0 | file |
|
| | R-P3 | PR-B | Restaurant Food | organic | 600 | 148 | unknown | file |
|
| | R-P3 | PR-D | General Social Comments | organic | 19,349 | 4,026 | CC BY 4.0 | file |
|
| | R-P3 | TW-B | Consumer Electronics Gaming | generated | 1,160 | 290 | xAI terms (attribute Grok) | file |
|
| | R-P3 | TW-C | Public Civic Services | generated | 1,160 | 290 | xAI terms (attribute Grok) | file |
|
| | R-P3 | TW-D | Mixed Social Commentary | generated | 1,280 | 320 | xAI terms (attribute Grok) | file |
|
|
|
| Origin: *organic* (public corpus of native text), *translated* (machine-translated from English),
|
| *generated* (produced by the first author with xAI Grok and checked by hand), *uncertain*.
|
| Full provenance and URLs: `domains.csv`.
|
|
|
| ## Getting the data
|
|
|
| Domains marked **file** are included directly (`data/`), each as a config of this dataset:
|
|
|
| ```python
|
| from datasets import load_dataset
|
| ds = load_dataset("abdullaharoon/Urdu-Multi-Domain-Benchmark", "R-SA_SA-B")
|
| ```
|
|
|
| Domains marked **rebuild** come from public corpora whose authors state no licence, so their text is
|
| not redistributed here. `manifests/` holds the row order, a SHA-1 of each normalised text and our
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| label; `rebuild.py` downloads the authors' GitHub repositories and writes the train/test files:
|
|
|
| ```bash
|
| pip install pandas openpyxl
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| python rebuild.py # writes data/<domain>/{train,test}.csv for the four rebuild domains
|
| ```
|
|
|
| The rebuild recovers every item except 2 of 21,285 ISE-Hate tweets, which differ in the authors'
|
| current file; `rebuild.py` reports them.
|
|
|
| ## Licences
|
|
|
| Each domain keeps the licence of its source (table above). Traced sources with a permissive
|
| licence are released under MIT, CC BY 4.0 or ODbL 1.0. Four published corpora (Urdu Sentiment
|
| Corpus, ISE-Hate, Bend the Truth, Ax-to-Grind Urdu) state no licence and are distributed through
|
| `rebuild.py` only; please cite their authors. Generated domains were produced with xAI Grok; its
|
| terms ask that such output be attributed to Grok and prohibit using it to develop models that
|
| compete with xAI. 3 organic domains were obtained from public
|
| uploads whose authors we could not identify; they are released
|
| for research use and will be removed on request from a rights holder. Our own contributions
|
| (splits, manifests, audits, generated-data curation) are released under CC BY-NC 4.0.
|
|
|
| ## Citation
|
|
|
| Please cite the paper and the original corpora listed in `domains.csv`.
|
|
|
