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--- |
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library_name: keras |
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license: apache-2.0 |
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tags: |
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- seq2seq |
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- translation |
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language: |
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- en |
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- fr |
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--- |
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## Keras Implementation of Character-level recurrent sequence-to-sequence model |
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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/). |
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Full credits to : [fchollet](https://twitter.com/fchollet) |
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Model reproduced by : [Sumedh](https://huggingface.co/sumedh) |
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## Intended uses & limitations |
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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. |
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## Training and evaluation data |
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English to French translation data from |
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https://www.manythings.org/anki/ |
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## Training procedure |
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- We start with input sequences from a domain (e.g. English sentences) and corresponding target sequences from another domain (e.g. French sentences). |
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- An encoder LSTM turns input sequences to 2 state vectors (we keep the last LSTM state and discard the outputs). |
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- 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. |
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- 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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| name | learning_rate | decay | rho | momentum | epsilon | centered | training_precision | |
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|----|-------------|-----|---|--------|-------|--------|------------------| |
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|RMSprop|0.0010000000474974513|0.0|0.8999999761581421|0.0|1e-07|False|float32| |
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```python |
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batch_size = 64 # Batch size for training. |
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epochs = 100 # Number of epochs to train for. |
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latent_dim = 256 # Latent dimensionality of the encoding space. |
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num_samples = 10000 # Number of samples to train on. |
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``` |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
