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:
- dc9413aff0ae21368a75c83ba28aba2afa1f3781c22098eb252749079f9717d2
- Size of remote file:
- 12 kB
- SHA256:
- 94c67ae08cd1d852c49011bc6c27f92a2dfa1048f7806bed29a683bac534f1c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.