Kennymdo/South-African-Language-Identification-Hacakthon-2023-

# South-African-Language-Identification-Hacakthon-2023- With such a multilingual population, it is only obvious that our systems and devices also communicate in multi-languages. In this challenge, you will take text which is in any of South Africa's 11 Official languages and identify which language the text is in. This is an example of NLP's Language Identification, the task of determining the natural language that a piece of text is written in. ## Problem Statement you will take text which is in any of South Africa's 11 Official languages and identify which language the text is in. This is an example of NLP's Language Identification, the task of determining the natural language that a piece of text is written in using various classification models. ## Data overview The dataset used for this challenge is the NCHLT Text Corpora collected by the South African Department of Arts and Culture & Centre for Text Technology (CTexT, North-West University, South Africa). The training set was improved through additional cleaning done by Praekelt. The data is in the form Language ID, Text. The text is in various states of cleanliness. Some NLP techniques will be necessary to clean up the data. File descriptions train_set.csv - the training set test_set.csv - the test set sample_submission.csv - a sample submission file in the correct format ## Language IDs * afr - Afrikaans * eng - English * nbl - isiNdebele * nso - Sepedi * sot - Sesotho * ssw - siSwati * tsn - Setswana * tso - Xitsonga * ven - Tshivenda * xho - isiXhosa * zul - isiZulu