https://github.com/sayamalt/english-to-german-translation-using-seq2seq

Successfully established a neural machine translation model using sequence to sequence modeling which can successfully translate English sentences to their corresponding German translations.
https://github.com/sayamalt/english-to-german-translation-using-seq2seq

natural-language-processing neural-language-translation sequence-to-sequence-models text-generation-using-lstm text-preprocessing

Last synced: 3 months ago
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Successfully established a neural machine translation model using sequence to sequence modeling which can successfully translate English sentences to their corresponding German translations.

• Host: GitHub
• URL: https://github.com/sayamalt/english-to-german-translation-using-seq2seq
• Owner: SayamAlt
• Created: 2022-11-13T10:32:38.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-13T10:48:32.000Z (over 2 years ago)
• Last Synced: 2024-12-28T08:09:55.283Z (5 months ago)
• Topics: natural-language-processing, neural-language-translation, sequence-to-sequence-models, text-generation-using-lstm, text-preprocessing
• Language: Jupyter Notebook
• Homepage:
• Size: 626 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# English-to-German-Translation-using-Seq2Seq

## Overview

Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems.

The global dissemination of new ideas, expertise, and information requires translation. To establish successful cross-cultural communication, it is vitally required. Translation plays a role in the dissemination of new knowledge and has the power to alter history. 

Machine translation technology can help law firms and corporate legal departments understand and process large quantities of legal documents quickly. Machine translation can handle most of the volume, but there is no margin for error in legal translations.

![English to German](https://fiverr-res.cloudinary.com/images/q_auto,f_auto/gigs/102851079/original/5d1b01ab0a2b63394c26382f9cf73b964ed724c1/translate-500-words-of-english-into-german.jpg)

![English to German Translation using Seq2Seq](https://nextjournal.com/data/QmSAGUejc3K95ZiLErFRG8h18epbxmEs4crg97Jx7GtbC3?content-type=image%2Fpng&filename=Encoder-Decoder%20Model.png)

![English to German Translation](https://www.nbshare.io/static/snapshots/translation_german_1.png)

![English to German](https://fiverr-res.cloudinary.com/images/q_auto,f_auto/gigs/158952193/original/c905b235e04d27685b0c4d9794cc7b0e151154ae/translate-english-to-german-or-german-to-english.jpg)

## Dataset Used

Link: https://www.kaggle.com/datasets/kaushal2896/english-to-german

## Technologies Used



  
Numpy

  
Pandas

  
Seaborn

  
Matplotlib

  
Keras

  
Tensorflow

  
Scikit-learn



## Info

Check for newest version here: http://www.manythings.org/anki/

This data is from the sentences_detailed.csv file from tatoeba.org.

Tatoeba Link: http://tatoeba.org/files/downloads/sentences_detailed.csv

## Terms of Use

See the terms of use. 

These files have been released under the same license as the source.

http://tatoeba.org/eng/terms_of_use

http://creativecommons.org/licenses/by/2.0

Attribution: www.manythings.org/anki and tatoeba.org