AmnaNoor123/code-switching-codesaviours-si26-amna

Code Switching Dataset — Code Saviours SI-26 (Amna) Dataset Description Roman Urdu mixed with English — e.g. "Aaj mera mood nahi hai for anything" — is how a huge share of Pakistanis actually write online, but almost no existing NLP resource labels this kind of code-switching at the word level. This dataset provides 160 real, naturally occurring mixed-language sentences, tokenised and labelled word-by-word as Roman Urdu, English, or a genuine hybrid blend.

How It Was Collected Sentences were gathered from:

Pakistani Twitter/X (searching common Roman Urdu function words such as kya, hai, nahi to surface naturally mixed tweets) r/pakistan comment threads on Reddit Comment sections on Pakistani YouTube videos Everyday WhatsApp-style phrasing (personal/identifying information removed — only the language pattern of the message was kept) Pakistani Facebook public pages Each sentence was manually reviewed to confirm it reflects natural, informal code-switching rather than translated or scripted text.

Labels Label Meaning URD The word is Roman Urdu ENG The word is English MIX The word is a genuine hybrid token — Urdu echo-reduplication or morphology applied to an English root (e.g. tension-wention, phone-shone, load-shedding) Dataset Structure Flat CSV with one row per word:

Column Description sentence The full original sentence the word came from word An individual word/token from that sentence label URD, ENG, or MIX Size: 160 unique sentences, 1,677 labelled word entries (URD: 1019, ENG: 647, MIX: 11)

Intended Use Training/evaluating token-level language-identification models for Roman Urdu-English code-switched text — useful for building NLP tools (spell-checkers, sentiment models, chatbots) that actually work on how Pakistanis write online, rather than only on formal Urdu or English.

Limitations Single-annotator labelling (not cross-validated by multiple annotators) Sentences skew toward casual, everyday topics (university, work, chit-chat) — may not generalise to formal or domain-specific text (e.g. news, legal) MIX category is intentionally narrow (true hybrid tokens are rare), so it is under-represented relative to URD/ENG Citation If you use this dataset, please credit: Code Saviours SI-26 Internship — Amna, 2026.