https://github.com/sayamalt/english-to-french-language-translation-using-seq2seq-modeling

Established a deep learning model which can translate English words/sentences into their corresponding French translations.
https://github.com/sayamalt/english-to-french-language-translation-using-seq2seq-modeling

natural-language-processing neural-machine-translation sequence-to-sequence-models

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Established a deep learning model which can translate English words/sentences into their corresponding French translations.

• Host: GitHub
• URL: https://github.com/sayamalt/english-to-french-language-translation-using-seq2seq-modeling
• Owner: SayamAlt
• Created: 2022-10-18T08:48:07.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-13T08:35:01.000Z (about 2 years ago)
• Last Synced: 2024-11-07T12:48:09.519Z (2 months ago)
• Topics: natural-language-processing, neural-machine-translation, sequence-to-sequence-models
• Language: Jupyter Notebook
• Homepage:
• Size: 4.58 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# English-to-French-Language-Translation-using-Seq2Seq-Modeling

A sequence to sequence (Seq2Seq) model which translates English sentences into their corresponding French sentences.

![English to French Translation using Seq2Seq](https://miro.medium.com/max/1400/1*wPqahJXFKt8RPPgZvETUpw.jpeg)

![Neural Language Translation Architecture](https://miro.medium.com/max/1400/1*EPDNg1M45JXkASby_HiVOg.png)

## Overview

Neural Machine Translation, also referred to as Neural MT, NMT, Deep Neural Machine Translation, Deep NMT, or DNMT, is a cutting-edge machine translation method that uses neural network techniques to estimate the likelihood of a sequence of words. This might be a word or sentence in its entirety, or thanks to recent developments, the entire document. Deep neural networks and artificial intelligence are used in NMT to train neural models, which is a fundamentally different approach to the problem of language translation and localization. In just three years, there has been a significant shift from SMT to NMT, making NMT the main machine translation methodology. With superior fluency and adequacy than statistical machine translation methods, neural machine translation often delivers translations of a significantly higher quality. 

Only a small portion of the memory required by conventional Statistical Machine Translation (SMT) models is used by neural machine translation. Because the neural translation model is trained end-to-end to maximise translation performance, this NMT approach differs from traditional translation SMT systems. In contrast to the conventional phrase-based translation system, which consists of numerous small sub-components that are tuned separately, neural machine translation aims to create and train a single, massive neural network that can read a sentence and produce an accurate translation. 

## Dataset Used

Link: https://www.kaggle.com/datasets/devicharith/language-translation-englishfrench

## Content

The dataset comprises 2 columns, one of which includes English words/sentences and the other contains the corresponding French words/sentences i.e. French translations. 

## Python Libraries Used



  
Keras

  
Tensorflow

  
Scikit-learn

  
Numpy

  
Pandas

  
Seaborn

  
Matplotlib



## Acknowledgements

For getting more datasets of distinct languages, refer to the following link: http://www.manythings.org/anki/