Language Detection with Machine Learning Language detection is a natural language processing task where we need to identify the language of a text or document. Using machine learning for language identification was a difficult task a few years ago because there was not a lot of data on languages, but with the availability of data with ease, several powerful machine learning models are already available for language identification. So, if you want to learn how to train a machine learning model for language detection, then this article is for you. In this article, I will walk you through the task of language detection with machine learning using Python.
As a human, you can easily detect the languages you know. For example, I can easily identify Hindi and English, but being an Indian, it is also not possible for me to identify all Indian languages. This is where the language identification task can be used. Google Translate is one of the most popular language translators in the world which is used by so many people around the world. It also includes a machine learning model to detect languages that you can use if you don’t know which language you want to translate.
The most important part of training a language detection model is data. The more data you have about every language, the more accurate your model will perform in real-time. The dataset that I am using is collected from Kaggle, which contains data about 22 popular languages and contains 1000 sentences in each of the languages, so it will be an appropriate dataset for training a language detection model with machine learning. So in the section below, I will take you through how you can train a language detection model with machine learning using Python.
link for the dataset used is http://raw.githubusercontent.com/amankharwal/Website-data/master/dataset.csv