https://github.com/mohamed-devp/language-identification-with-cnns
Train and evaluate a deep learning model capable of identifying six distinc languages with 99% accuracy.
https://github.com/mohamed-devp/language-identification-with-cnns
convolutional-neural-networks deep-learning language-identification natural-language-processing
Last synced: 11 days ago
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Train and evaluate a deep learning model capable of identifying six distinc languages with 99% accuracy.
- Host: GitHub
- URL: https://github.com/mohamed-devp/language-identification-with-cnns
- Owner: Mohamed-Devp
- Created: 2025-04-23T13:48:16.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-23T14:11:10.000Z (over 1 year ago)
- Last Synced: 2025-10-25T13:26:12.932Z (9 months ago)
- Topics: convolutional-neural-networks, deep-learning, language-identification, natural-language-processing
- Language: Jupyter Notebook
- Homepage:
- Size: 22.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Language identification with CNNs
## Table of Contents
- [Overview](#overview)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
## Overview
This project leverages NLP techniques and CNNs to train a model capable of identifying six distinct languages with high accuracy.
## Dataset
The dataset used in this project is a subset of the [Wili-2018](https://www.google.com/url?q=https%3A%2F%2Fwww.kaggle.com%2Fdatasets%2Fmexwell%2Fwili-2018) dataset from kaggle.
## Installation
To run this project, you need to have Python installed along with the required libraries. You can install the necessary dependencies using the following command:
```bash
pip install -r requirements.txt
```
## Usage
To train and test the model, run the ```main.ipynb``` notebook. This notebook contains all the steps from data preprocessing, model training, and evaluation.
## Results
The performance of the model can be evaluated using metrics such as accuracy, precision, recall, and F1-score that are computed for every distinct language.These metrics help in understanding the effectiveness of the language identifcation model.
## Contributing
Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.