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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://sumedh/lstm-seq2seq") - Notebooks
- Google Colab
- Kaggle
Download model.png from sumedh/lstm-seq2seq: direct link, hf CLI and curl.
- Browser
- Download file 12 kB
-
https://huggingface.co/sumedh/lstm-seq2seq/resolve/main/model.png
- Command line
-
hf download hf://sumedh/lstm-seq2seq/model.png
-
curl -L -o model.png https://huggingface.co/sumedh/lstm-seq2seq/resolve/main/model.png
12 kB

- Xet hash:
- dc9413aff0ae21368a75c83ba28aba2afa1f3781c22098eb252749079f9717d2
- Size of remote file:
- 12 kB
- SHA256:
- 94c67ae08cd1d852c49011bc6c27f92a2dfa1048f7806bed29a683bac534f1c7
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