README.md

Language Detection Model

This repository contains a machine learning model for detecting the language of a given text. The model is trained using a dataset of text samples in multiple languages and can accurately classify the language of new text inputs.

Table of Contents

• Introduction
• Features
• Usage
• Importing Libraries
• Model Architecture
• License

Introduction

Language detection is a crucial task in natural language processing (NLP) with applications in text preprocessing, content recommendation, and multilingual information retrieval. This project aims to provide an efficient and accurate language detection model using machine learning techniques.

Features

• Detects languages with high accuracy
• Supports multiple languages (e.g., English, Spanish, French, German, etc.)
• Easy to integrate with other NLP tools and pipelines
• Provides a simple API for language detection

Usage

To use the language detection model, initialize the LanguageDetector class and use the detect_language method to predict the language of a given text. Provide the text input, and the model will return the detected language.

Importing Libraries

Before using the model, ensure that the following libraries are installed:

Importing Libraries

import pandas as pd
import numpy as np
import re
import seaborn as sns
import matplotlib.pyplot as plt
import pickle

import warnings
warnings.simplefilter("ignore")

Install these libraries using pip if you haven't already:

pip install pandas numpy seaborn matplotlib

Model Architecture

The language detection model is built using a machine learning pipeline that includes:

1. Text Preprocessing: Tokenization, normalization, and feature extraction.
2. Feature Engineering: Using TF-IDF vectors to represent the text data.
3. Classifier: A supervised learning algorithm (e.g., Logistic Regression, Random Forest, or a deep learning model) trained on labeled text samples.