qadeesanoor/code-switching-codesaviours-si26-qadeesanoor

Dataset Summary

This dataset contains Roman Urdu–English code-switched sentences, the way mixed-language text actually gets written in everyday Pakistani texting, tweeting, and casual conversation (e.g. "Aaj ka din bohot busy tha, had 3 meetings back to back"). Each sentence is broken down word-by-word, and every word is tagged with a language label. No existing Roman Urdu NLP resource handles this kind of within-sentence language mixing well — this dataset is a step toward building tools (taggers, classifiers, OCR post-processors) that can.

Supported Task

Word-level language identification / token classification for code-switched Roman Urdu–English text.

Languages Roman Urdu (Urdu written in Latin script) English Code-mixed English–Urdu (see label scheme below) Dataset Structure

Fields (flat CSV, one row per word):

Column Description sentence The full original sentence word A single word/token from that sentence label Language tag for that word: URD, ENG, or MIX

Label scheme:

URD — Roman Urdu word (e.g. bohot, kal, karna) ENG — English word used as-is (e.g. busy, meeting, honestly) MIX — English-origin word fully nativized into everyday Roman Urdu speech, used the way a native word would be rather than as a code-switch (e.g. phone, office, traffic, assignment)

Size: 151 sentences / 1,489 word-level entries Label distribution: URD 901, ENG 488, MIX 100

Data Collection

Sentences were collected from [DESCRIBE YOUR ACTUAL SOURCES HERE — e.g. "Pakistani Twitter/X, r/pakistan comments, YouTube comments on Pakistani videos, and anonymized WhatsApp messages"]. Personal identifying information was removed before inclusion. Word-level labels were assigned using a Roman Urdu dictionary lookup and then manually spot-checked and corrected.

Intended Use

For training/evaluating word-level language identification models on Roman Urdu–English code-switched text — useful for NLP pipelines (OCR post-processing, chatbots, sentiment analysis) that need to handle mixed Pakistani text rather than assuming pure English or pure Urdu input.

Limitations Small dataset (151 sentences) — suited for prototyping/fine-tuning, not training a model from scratch. Labels were rule/dictionary-assisted, not annotated by multiple independent native speakers, so some labeling disagreements are possible, especially around the URD/MIX boundary for nativized loanwords. Topic coverage is informal/everyday speech (university, work, family, food, health) — not domain-specific text like news or legal documents.