LinguaNet is a language identification model built on Deep Neural Networks (DNNs) using Python and TensorFlow. It employs character n-grams to classify the language of any given text input. The system is capable of ingesting user inputs and efficiently outputting the identified language.
This project includes the visualization of learned features using techniques such as t-SNE and PCA to give insights into how the network differentiates between languages. The model has demonstrated a final accuracy of 89.2%, illustrating its effectiveness in language identification.
Python (>=3.7)
TensorFlow (>=2.4.0)
Scikit-learn (>=0.24.0)
Numpy (>=1.19.5)
Matplotlib (>=3.3.2)
Clone the repo
git clone https://github.com/mohamzamir/LinguaNet-A-Neural-Network-Based-Language-Identification-SystemThe model is a Deep Neural Network (DNN) implemented using Python and TensorFlow. It utilizes character n-grams to create distinct feature sets for different languages, and these features are used to classify the language of a given text input.
To gain insights into the feature representation, we visualized the learned features of the model using techniques such as t-SNE and PCA.
The LinguaNet model achieved a final accuracy of 89.2%, highlighting its effectiveness in identifying and differentiating languages based on text input.
We plan to refine the model further, aiming to increase its accuracy and expand its scope to include more languages. Contributions and feedback are welcome.
Copyright [2023] [Amir Hamza]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at:
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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