readme: ---
  library_name: keras
  license: apache-2.0
  tags:
  - seq2seq
  - translation
  language:
  - en
  - fr
  ---
  
  ## 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)
  
  Model reproduced by : [Sumedh](https://huggingface.co/sumedh)
  
  ## Intended uses & limitations
  
  This model implements a basic character-level recurrent sequence-to-sequence network for 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. It works best on text of length <= 15 characters.
  
  ## Training and evaluation data
  English to French translation data from
  https://www.manythings.org/anki/
  
  ## Training procedure
  - We start with input sequences from a domain (e.g. English sentences) and corresponding target sequences from another domain (e.g. French sentences).
  - An encoder LSTM turns input sequences to 2 state vectors (we keep the last LSTM state and discard the outputs).
  - A decoder LSTM is trained to turn the target sequences into the same sequence but offset by one timestep in the future, a training process called "teacher forcing" in this context. It uses as initial state the state vectors from the encoder. Effectively, the decoder learns to generate targets[t+1...] given targets[...t], conditioned on the input sequence.
  - In inference mode, when we want to decode unknown input sequences, we: - Encode the input sequence into state vectors - Start with a target sequence of size 1 (just the start-of-sequence character) - Feed the state vectors and 1-char target sequence to the decoder to produce predictions for the next character - Sample the next character using these predictions (we simply use argmax). - Append the sampled character to the target sequence - Repeat until we generate the end-of-sequence character or we hit the character limit.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  | name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision |
  |----|-------------|-----|---|--------|-------|--------|------------------|
  |RMSprop|0.0010000000474974513|0.0|0.8999999761581421|0.0|1e-07|False|float32|
  
  ```python
  batch_size = 64  # Batch size for training.
  epochs = 100  # Number of epochs to train for.
  latent_dim = 256  # Latent dimensionality of the encoding space.
  num_samples = 10000  # Number of samples to train on.
  ```
  
   ## Model Plot
  
  <details>
  <summary>View Model Plot</summary>
  
  ![Model Image](./model.png)
  
  </details>