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:
- 38c72a2f32c0e09e0de2050d3fa992148e0e033235cb6b9cc147cbe977cfdf68
- Size of remote file:
- 855 Bytes
- SHA256:
- 1efa6f355c2ce164b191774871c70aa24310b96799eb0b4ba0dea96e89f06d9d
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