Instructions to use keras-io/char-lstm-seq2seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/char-lstm-seq2seq with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-io/char-lstm-seq2seq") - Notebooks
- Google Colab
- Kaggle
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
latent_dim = 256
num_encoder_tokens = 71
max_encoder_seq_length = 15
num_decoder_tokens = 92
max_decoder_seq_length = 59
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