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Datasets:
abdullaharoon
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Urdu-Multi-Domain-Benchmark

Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Urdu
Size:
100K - 1M
Tags:
urdu
roman-urdu
domain-robustness
benchmark
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Urdu-Multi-Domain-Benchmark / README.md
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abdullaharoon
Provenance: trace Nastaliq HS-B (MIT) and SA-B (ODbL) to their public sources
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metadata 
license: other
license_name: per-domain
license_link: LICENSE.md
language:
  - ur
task_categories:
  - text-classification
tags:
  - urdu
  - roman-urdu
  - domain-robustness
  - benchmark
pretty_name: Urdu Multi-Domain Benchmark (v1.1)
size_categories:
  - 100K<n<1M
configs:
  - config_name: N-SA_SA-B
    data_files:
      - split: train
        path: data/Nastaliq/Sentiment Analysis/Domain_B_Movie_Reviews/train.csv
      - split: test
        path: data/Nastaliq/Sentiment Analysis/Domain_B_Movie_Reviews/test.csv
  - config_name: N-SA_SA-E
    data_files:
      - split: train
        path: >-
          data/Nastaliq/Sentiment
          Analysis/Domain_E_Socioeconomic_Agricultural/train.csv
      - split: test
        path: >-
          data/Nastaliq/Sentiment
          Analysis/Domain_E_Socioeconomic_Agricultural/test.csv
  - config_name: N-HS_HS-B
    data_files:
      - split: train
        path: data/Nastaliq/Hate Speech/Domain_B_Social_Media_Hate_Speech/train.csv
      - split: test
        path: data/Nastaliq/Hate Speech/Domain_B_Social_Media_Hate_Speech/test.csv
  - config_name: N-HS_HS-C
    data_files:
      - split: train
        path: >-
          data/Nastaliq/Hate
          Speech/Domain_C_Politics_Sports_Health_Family/train.csv
      - split: test
        path: >-
          data/Nastaliq/Hate
          Speech/Domain_C_Politics_Sports_Health_Family/test.csv
  - config_name: N-HS_HS-D
    data_files:
      - split: train
        path: >-
          data/Nastaliq/Hate
          Speech/Domain_D_Sports_Labor_Arts_Education/train.csv
      - split: test
        path: >-
          data/Nastaliq/Hate
          Speech/Domain_D_Sports_Labor_Arts_Education/test.csv
  - config_name: N-QA_QA-A
    data_files:
      - split: train
        path: data/Nastaliq/QA/Domain_A_Religious_Hadith/train.csv
      - split: test
        path: data/Nastaliq/QA/Domain_A_Religious_Hadith/test.csv
  - config_name: N-QA_QA-B
    data_files:
      - split: train
        path: data/Nastaliq/QA/Domain_B_General_Knowledge/train.csv
      - split: test
        path: data/Nastaliq/QA/Domain_B_General_Knowledge/test.csv
  - config_name: N-QA_QA-C
    data_files:
      - split: train
        path: data/Nastaliq/QA/Domain_C_Long_Form_QnA/train.csv
      - split: test
        path: data/Nastaliq/QA/Domain_C_Long_Form_QnA/test.csv
  - config_name: N-QA_QA-D
    data_files:
      - split: train
        path: data/Nastaliq/QA/Domain_D_Short_Form_QnA/train.csv
      - split: test
        path: data/Nastaliq/QA/Domain_D_Short_Form_QnA/test.csv
  - config_name: R-SA_SA-A
    data_files:
      - split: train
        path: >-
          data/Roman/Sentiment
          Analysis/Domain_A_Mixed_Public_Discourse/train.csv
      - split: test
        path: data/Roman/Sentiment Analysis/Domain_A_Mixed_Public_Discourse/test.csv
  - config_name: R-SA_SA-B
    data_files:
      - split: train
        path: >-
          data/Roman/Sentiment
          Analysis/Domain_B_Social_Media_Politics_Drama/train.csv
      - split: test
        path: >-
          data/Roman/Sentiment
          Analysis/Domain_B_Social_Media_Politics_Drama/test.csv
  - config_name: R-SA_SA-C
    data_files:
      - split: train
        path: data/Roman/Sentiment Analysis/Domain_C_YouTube_Entertainment/train.csv
      - split: test
        path: data/Roman/Sentiment Analysis/Domain_C_YouTube_Entertainment/test.csv
  - config_name: R-CA_CA-A
    data_files:
      - split: train
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_A_YouTube_Comments/train.csv
      - split: test
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_A_YouTube_Comments/test.csv
  - config_name: R-CA_CA-B
    data_files:
      - split: train
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_B_Content_Creator_Comments/train.csv
      - split: test
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_B_Content_Creator_Comments/test.csv
  - config_name: R-CA_CA-C
    data_files:
      - split: train
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_C_Social_Media_Diverse_Vocab/train.csv
      - split: test
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_C_Social_Media_Diverse_Vocab/test.csv
  - config_name: R-CA_CA-D
    data_files:
      - split: train
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_D_General_Online_Video/train.csv
      - split: test
        path: >-
          data/Roman/Cyber Abuse or Abusive
          Language/Domain_D_General_Online_Video/test.csv
  - config_name: R-P3_PR-A
    data_files:
      - split: train
        path: >-
          data/Roman/Product or eCommerce
          Reviews/Domain_A_Daraz_Ecommerce/train.csv
      - split: test
        path: >-
          data/Roman/Product or eCommerce
          Reviews/Domain_A_Daraz_Ecommerce/test.csv
  - config_name: R-P3_PR-B
    data_files:
      - split: train
        path: >-
          data/Roman/Product or eCommerce
          Reviews/Domain_B_Restaurant_Food/train.csv
      - split: test
        path: >-
          data/Roman/Product or eCommerce
          Reviews/Domain_B_Restaurant_Food/test.csv
  - config_name: R-P3_PR-D
    data_files:
      - split: train
        path: >-
          data/Roman/Product or eCommerce
          Reviews/Domain_D_Electronics_Gaming_Delivery/train.csv
      - split: test
        path: >-
          data/Roman/Product or eCommerce
          Reviews/Domain_D_Electronics_Gaming_Delivery/test.csv
  - config_name: R-P3_TW-B
    data_files:
      - split: train
        path: >-
          data/Roman/Twitter or Social Media
          Opinions/Domain_B_Consumer_Electronics_Gaming/train.csv
      - split: test
        path: >-
          data/Roman/Twitter or Social Media
          Opinions/Domain_B_Consumer_Electronics_Gaming/test.csv
  - config_name: R-P3_TW-C
    data_files:
      - split: train
        path: >-
          data/Roman/Twitter or Social Media
          Opinions/Domain_C_Public_Civic_Services/train.csv
      - split: test
        path: >-
          data/Roman/Twitter or Social Media
          Opinions/Domain_C_Public_Civic_Services/test.csv
  - config_name: R-P3_TW-D
    data_files:
      - split: train
        path: >-
          data/Roman/Twitter or Social Media
          Opinions/Domain_D_Mixed_Social_Commentary/train.csv
      - split: test
        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 (Nastaliq and Roman Urdu), grouped into 7 transfer matrices over four task types (sentiment and polarity, abusive language, fake news, question-answer pair validation). All domains of a matrix share one label space, so every source-to-target pair is a valid domain shift.

Content warning. The abusive-language and hate-speech domains contain offensive, hateful and sectarian text. Classifiers trained on these data should not be deployed without human review.

What changed from v1.0

v1.1 supersedes v1.0 (DOI 10.5281/zenodo.22195610). Domains were selected by explicit inclusion criteria, and every split is free of exact and near-duplicate train-test overlap:

  • Excluded candidates (duplicated sources, labels predictable from the origin of an item, fewer than 100 test items):
  • 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)
  • Nastaliq/Sentiment Analysis/Domain_D_Governance_Political: same texts and labels as Sentiment A
  • Roman/Sentiment Analysis/Domain_D_Utilities_Apps_Courses: same 11,000-review corpus as Roman Sentiment B
  • 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
  • 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
  • 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
  • Nastaliq/Sentiment Analysis/Domain_C_Civic_Social_Topics: too small to evaluate (162 items, 33 in test; minimum is 100 test items)
  • Near-duplicate-aware splits. LLM-generated domains were re-split so that clusters of template near-duplicates (character 5-gram Jaccard >= 0.8) do not straddle train and test.
  • Question-grouped QA splits. No question (or near-duplicate question) occurs in both splits.
  • Cross-domain overlap removed inside each matrix.

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
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
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:

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 label; rebuild.py downloads the authors' GitHub repositories and writes the train/test files:

pip install pandas openpyxl
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.