Noisy77/code-switching-codesaviours-si26-bilal

Roman Urdu-English Code Switching Dataset

Dataset Description

A manually labeled dataset of 160+ code-switching sentences where Roman Urdu and English are naturally mixed — reflecting how 230 million Pakistanis actually communicate online.

Each word in every sentence is tagged with a language label, making this dataset suitable for token-level language identification and code-switching NLP research.

Label Meanings

Label	Description	Examples
URD	Roman Urdu (Urdu written in Latin script)	Aaj, bohot, hai, mujhe, karo
ENG	English	meeting, please, seriously, office
MIX	Hybrid or brand names used across both languages	Netflix, Instagram, ChatGPT, Foodpanda

Dataset Format

The dataset is in CSV format with three columns:

Column	Type	Description
sentence	string	The full code-switched sentence
word	string	Individual word from the sentence
label	string	Language label (URD, ENG, or MIX)

Example Entries

sentence	word	label
Aaj ka din bohot busy tha had 3 meetings back to back	Aaj	URD
Aaj ka din bohot busy tha had 3 meetings back to back	ka	URD
Aaj ka din bohot busy tha had 3 meetings back to back	busy	ENG
Aaj ka din bohot busy tha had 3 meetings back to back	meetings	ENG

Collection Methodology

Sentences were crafted to mirror real-world code-switching patterns observed across:

• 🐦 Pakistani Twitter/X — searched for common Urdu markers like "kya", "hai", "nahi" mixed with English
• 🗨️ Reddit (r/pakistan) — comment sections where bilingual users interact
• 📺 YouTube comments — on Pakistani content creators' videos
• 💬 WhatsApp-style messaging — casual conversational patterns (anonymized)

Categories Covered

The dataset spans 8 thematic categories to ensure diversity:

1. Daily life & casual conversation (~30 sentences)
2. Work, office & professional (~25 sentences)
3. Food, restaurants & cooking (~20 sentences)
4. Technology & social media (~20 sentences)
5. Education, studies & exams (~20 sentences)
6. Sports, cricket & entertainment (~15 sentences)
7. Shopping, fashion & clothes (~15 sentences)
8. Travel, weather & transport (~15 sentences)

Intended Uses

• Token-level language identification — Train classifiers to detect language switches at the word level
• Code-switching research — Study patterns of how Urdu-English bilingual speakers mix languages
• NER for mixed-language text — Foundation for named entity recognition in code-switched text
• Sentiment analysis — Pre-processing step for sentiment models handling mixed-language input
• Language model fine-tuning — Adapt models to understand and generate code-switched text

How to Load

import pandas as pd

# From local file
df = pd.read_csv('dataset.csv')

# Or from HuggingFace
from datasets import load_dataset
dataset = load_dataset('Noisy77/code-switching-codesaviours-si26-bilal')

Limitations

• The dataset focuses on Roman Urdu + English only (not Urdu script)
• Sentences are modeled after real patterns but are not direct copies from social media
• The MIX label is used sparingly and primarily for brand/platform names
• Dataset size is relatively small (160+ sentences) — best used for prototyping or as a seed dataset

Citation

If you use this dataset, please cite:

@dataset{bilal_code_switching_2026,
  title={Roman Urdu-English Code Switching Dataset},
  author={Bilal},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/Noisy77/code-switching-codesaviours-si26-bilal}
}

Credit

Built by Bilal during the Code Saviours ML/AI Internship — Batch SI-26.