Machine-Translation-NLP

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Machine-Translation-NLP

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A Machine Translation project that translates text from English to French

Machine-Translation-NLP 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# Machine Translation
This project is a part of an end-to-end machine translation pipeline that will accept English text as input and return the French translation.

![Machine Translation](https://www.dynamiclanguage.com/wp-content/uploads/2019/03/blog-heading-1.png)

## Content 
- [Machine Translation](#machine-translation)
  - [Content](#content)
  - [Description](#description)
  - [Dataset](#dataset)
  - [Install Prerequisites](#install-prerequisites)
  - [Network Architecture](#network-architecture)
  - [Final Model Code](#final-model-code)
  - [Authors](#authors)
  - [Contributing](#contributing)

## Description
This project takes any english text and converts it to sequences of integers based on a big enough french and english vocabularies and pass it to a model that returns a probability distribution over possible translations with accuracy > 97%.

## Dataset
In this project we will be using [WMT](http://www.statmt.org/), The most common datasets used for machine translation.

## Install Prerequisites
This project requires **Python 3** and the following Python libraries installed:

- [NumPy](http://www.numpy.org/)
- [TensorFlow](https://www.tensorflow.org) 1.x
- [Keras](https://keras.io) 2.x

## Network Architecture 
After tokenizing the text and make all pre-processes to it we pass it to a ```Word Embedding``` layer then to 2 ```Bidirectional LSTM``` with 256 units then ```TimeDistributed``` layer with a ```softmax``` activation function to produce probability distribution.
![](https://i.ibb.co/0sKYNHt/Screen-Shot-2020-06-20-at-5-48-59-PM.png)

## Final Model Code

After training this model for 10 epochs we were able to get accuracy of 98% over both the training and validation sets.

```python

def model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size):
    """
    Build and train a model that incorporates embedding, encoder-decoder, and bidirectional RNN on x and y
    :param input_shape: Tuple of input shape
    :param output_sequence_length: Length of output sequence
    :param english_vocab_size: Number of unique English words in the dataset
    :param french_vocab_size: Number of unique French words in the dataset
    :return: Keras model built, but not trained
    """
   
    learning_rate=5e-3
    
    model=Sequential()
    model.add(Embedding(english_vocab_size,256,
                      input_length=input_shape[1]))

model.add(Bidirectional(LSTM(256),))

model.add(RepeatVector(output_sequence_length))

model.add(Bidirectional(LSTM(256,return_sequences=True)))

model.add(TimeDistributed(Dense(french_vocab_size,
                              activation='softmax')))
    
    model.compile(loss=sparse_categorical_crossentropy,
                  optimizer=Adam(learning_rate),
                  metrics=['accuracy'])
    
    print(model.summary())
    return model

```
> you can find all model trials in the notebook

## Authors

- **Ahmed Abd-Elbakey Ghonem** - [**Github**](https://github.com/3ba2ii)

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

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