sentence stringclasses 150
values | word stringlengths 1 12 | label stringclasses 2
values |
|---|---|---|
Aaj mera mood nahi hai for anything | Aaj | URD |
Aaj mera mood nahi hai for anything | mera | URD |
Aaj mera mood nahi hai for anything | mood | ENG |
Aaj mera mood nahi hai for anything | nahi | URD |
Aaj mera mood nahi hai for anything | hai | URD |
Aaj mera mood nahi hai for anything | for | ENG |
Aaj mera mood nahi hai for anything | anything | ENG |
Yaar kal presentation hai still not ready | Yaar | URD |
Yaar kal presentation hai still not ready | kal | URD |
Yaar kal presentation hai still not ready | presentation | ENG |
Yaar kal presentation hai still not ready | hai | URD |
Yaar kal presentation hai still not ready | still | ENG |
Yaar kal presentation hai still not ready | not | ENG |
Yaar kal presentation hai still not ready | ready | ENG |
Tum online kyun nahi thay yesterday | Tum | URD |
Tum online kyun nahi thay yesterday | online | ENG |
Tum online kyun nahi thay yesterday | kyun | URD |
Tum online kyun nahi thay yesterday | nahi | URD |
Tum online kyun nahi thay yesterday | thay | URD |
Tum online kyun nahi thay yesterday | yesterday | ENG |
Bhai assignment submit kar di I am relieved | Bhai | URD |
Bhai assignment submit kar di I am relieved | assignment | ENG |
Bhai assignment submit kar di I am relieved | submit | ENG |
Bhai assignment submit kar di I am relieved | kar | URD |
Bhai assignment submit kar di I am relieved | di | URD |
Bhai assignment submit kar di I am relieved | I | ENG |
Bhai assignment submit kar di I am relieved | am | ENG |
Bhai assignment submit kar di I am relieved | relieved | ENG |
Kal office bohot hectic tha honestly | Kal | URD |
Kal office bohot hectic tha honestly | office | ENG |
Kal office bohot hectic tha honestly | bohot | URD |
Kal office bohot hectic tha honestly | hectic | ENG |
Kal office bohot hectic tha honestly | tha | URD |
Kal office bohot hectic tha honestly | honestly | ENG |
Main ghar pohanch gaya finally | Main | URD |
Main ghar pohanch gaya finally | ghar | URD |
Main ghar pohanch gaya finally | pohanch | URD |
Main ghar pohanch gaya finally | gaya | URD |
Main ghar pohanch gaya finally | finally | ENG |
Aaj weather bohot nice hai | Aaj | URD |
Aaj weather bohot nice hai | weather | ENG |
Aaj weather bohot nice hai | bohot | URD |
Aaj weather bohot nice hai | nice | ENG |
Aaj weather bohot nice hai | hai | URD |
Tum class miss kar gaye again | Tum | URD |
Tum class miss kar gaye again | class | ENG |
Tum class miss kar gaye again | miss | ENG |
Tum class miss kar gaye again | kar | URD |
Tum class miss kar gaye again | gaye | URD |
Tum class miss kar gaye again | again | ENG |
Mujhe coffee chahiye right now | Mujhe | URD |
Mujhe coffee chahiye right now | coffee | ENG |
Mujhe coffee chahiye right now | chahiye | URD |
Mujhe coffee chahiye right now | right | ENG |
Mujhe coffee chahiye right now | now | ENG |
Yeh movie bohot interesting thi | Yeh | URD |
Yeh movie bohot interesting thi | movie | ENG |
Yeh movie bohot interesting thi | bohot | URD |
Yeh movie bohot interesting thi | interesting | ENG |
Yeh movie bohot interesting thi | thi | URD |
Kal meeting cancel ho gayi | Kal | URD |
Kal meeting cancel ho gayi | meeting | ENG |
Kal meeting cancel ho gayi | cancel | ENG |
Kal meeting cancel ho gayi | ho | URD |
Kal meeting cancel ho gayi | gayi | URD |
Main abhi busy hoon call later | Main | URD |
Main abhi busy hoon call later | abhi | URD |
Main abhi busy hoon call later | busy | ENG |
Main abhi busy hoon call later | hoon | URD |
Main abhi busy hoon call later | call | ENG |
Main abhi busy hoon call later | later | ENG |
Tumhara idea really amazing hai | Tumhara | URD |
Tumhara idea really amazing hai | idea | ENG |
Tumhara idea really amazing hai | really | ENG |
Tumhara idea really amazing hai | amazing | ENG |
Tumhara idea really amazing hai | hai | URD |
Yaar traffic bohot terrible tha | Yaar | URD |
Yaar traffic bohot terrible tha | traffic | ENG |
Yaar traffic bohot terrible tha | bohot | URD |
Yaar traffic bohot terrible tha | terrible | ENG |
Yaar traffic bohot terrible tha | tha | URD |
Lunch ke baad let's study | Lunch | ENG |
Lunch ke baad let's study | ke | URD |
Lunch ke baad let's study | baad | URD |
Lunch ke baad let's study | let's | ENG |
Lunch ke baad let's study | study | ENG |
Mera phone battery low hai | Mera | URD |
Mera phone battery low hai | phone | ENG |
Mera phone battery low hai | battery | ENG |
Mera phone battery low hai | low | ENG |
Mera phone battery low hai | hai | URD |
Tum seriously bohot funny ho | Tum | URD |
Tum seriously bohot funny ho | seriously | ENG |
Tum seriously bohot funny ho | bohot | URD |
Tum seriously bohot funny ho | funny | ENG |
Tum seriously bohot funny ho | ho | URD |
Main project finish kar raha hoon | Main | URD |
Main project finish kar raha hoon | project | ENG |
Main project finish kar raha hoon | finish | ENG |
Main project finish kar raha hoon | kar | URD |
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Check out the documentation for more information.
Code Switching Dataset Description
This dataset was created as part of the Code Saviours SI-26 Project 2. It contains 150 Roman Urdu–English code-switching sentences. Each sentence is split into individual words, and every word is labeled according to the language it belongs to. The dataset is intended for educational and research purposes in Natural Language Processing (NLP), especially for language identification and code-switching detection.
Data Collection
The sentences were collected from publicly available online sources where Roman Urdu and English are commonly mixed in everyday communication. The dataset was then cleaned, organized, tokenized into words, and labeled manually.
Labels URD – Roman Urdu word ENG – English word MIX – Mixed-language token (used when a single token contains both Roman Urdu and English) Dataset Information Total Sentences: 150 Language: Roman Urdu + English Format: CSV Columns: sentence – Original mixed-language sentence word – Individual word/token label – Language label (URD, ENG, or MIX) Purpose
This dataset can be used for code-switching detection, token-level language identification, and other Natural Language Processing (NLP) tasks involving Roman Urdu and English text.
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