Instructions to use Sag1012/machine-translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Sag1012/machine-translation 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://Sag1012/machine-translation") - Notebooks
- Google Colab
- Kaggle
Download EncoderDecoder_3/rng_state.pth from Sag1012/machine-translation: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_3/rng_state.pth
- Command line
-
hf download hf://Sag1012/machine-translation/EncoderDecoder_3/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_3/rng_state.pth
14.2 kB
- Xet hash:
- 0dc96d7248002b8438d7edf7db0a74d3e92b022dba2ec9adc34aef97f35a9e51
- Size of remote file:
- 14.2 kB
- SHA256:
- 851107b0b3e7e5e8ba0aa4fc490d8e041d95a3549b556aad7472eed32f04527d
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