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
- Xet hash:
- f967853c172861b7cba5d98182e84ce8ddd41536331a8a45ca07e387e85454d2
- Size of remote file:
- 1.44 MB
- SHA256:
- ebe650bb01f55b74fe32182b5eac56128774f05222267156b42563b51458f151
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