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:
- 32511719f34fa0f20f34c0920c776a7f0bfe36f85fa28b6739dd652a72a2b2f9
- Size of remote file:
- 15.7 kB
- SHA256:
- e553cec509e2aa131ad343e15ac1f26baf8ac2aec645bfa7007d38c9faf775c2
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