Instructions to use sumedh/lstm-seq2seq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sumedh/lstm-seq2seq with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://sumedh/lstm-seq2seq") - Notebooks
- Google Colab
- Kaggle
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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 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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