
## Keras Implementation of Character-level recurrent sequence-to-sequence model

This repo contains the model and the notebook [to this Keras example on Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/).

Full credits to: [fchollet](https://twitter.com/fchollet)

## Background Information 
This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain.

## Limitations
It works on text of length <= 15 characters

## Parameters needed for using the model
```python
latent_dim = 256
num_encoder_tokens = 71
max_encoder_seq_length = 15
num_decoder_tokens = 92
max_decoder_seq_length = 59
```
{"language": ["en", "fr"], "license": ["cc0-1.0"], "tags": ["seq2seq", "translation"]}
keras seq2seq translation en fr license:cc0-1.0 has_space region:us
TAGS
#keras #seq2seq #translation #en #fr #license-cc0-1.0 #has_space #region-us 


## Keras Implementation of Character-level recurrent sequence-to-sequence model

This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.

Full credits to: fchollet

## Background Information 
This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain.

## Limitations
It works on text of length <= 15 characters

## Parameters needed for using the model

## Keras Implementation of Character-level recurrent sequence-to-sequence model

This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.

Full credits to: fchollet ## Background Information 
This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain. ## Limitations
It works on text of length <= 15 characters ## Parameters needed for using the model
TAGS
#keras #seq2seq #translation #en #fr #license-cc0-1.0 #has_space #region-us 
 ## Keras Implementation of Character-level recurrent sequence-to-sequence model

This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.

Full credits to: fchollet ## Background Information 
This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain. ## Limitations
It works on text of length <= 15 characters ## Parameters needed for using the model
passage: TAGS
#keras #seq2seq #translation #en #fr #license-cc0-1.0 #has_space #region-us 
## Keras Implementation of Character-level recurrent sequence-to-sequence model

This repo contains the model and the notebook to this Keras example on Character-level recurrent sequence-to-sequence model.

Full credits to: fchollet## Background Information 
This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain.## Limitations
It works on text of length <= 15 characters## Parameters needed for using the model