README.md

---

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.

- Paper: *Domain Robustness of Multilingual NLP Models Across Urdu and Roman Urdu Scripts*
  (Haroon and Bashir, FAST-NUCES).
- Code, audits and model predictions: https://github.com/AbdullahPatti/Urdu-Multi-Domain-Script-Research
- DOI: https://doi.org/10.5281/zenodo.23053495

> **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:

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

```bash

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`.