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 saved_model.pb from sumedh/lstm-seq2seq: direct link, hf CLI and curl.
- Browser
- Download file 1.44 MB
-
https://huggingface.co/sumedh/lstm-seq2seq/resolve/db44e0a871118e29fe4e5c5d67e8afbb39f49bdb/saved_model.pb
- Command line
-
hf download hf://sumedh/lstm-seq2seq@db44e0a871118e29fe4e5c5d67e8afbb39f49bdb/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/sumedh/lstm-seq2seq/resolve/db44e0a871118e29fe4e5c5d67e8afbb39f49bdb/saved_model.pb
1.44 MB
- Xet hash:
- f967853c172861b7cba5d98182e84ce8ddd41536331a8a45ca07e387e85454d2
- Size of remote file:
- 1.44 MB
- SHA256:
- ebe650bb01f55b74fe32182b5eac56128774f05222267156b42563b51458f151
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