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_2/optimizer.pt from Sag1012/machine-translation: direct link, hf CLI and curl.
- Browser
- Download file 3.02 GB
-
https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_2/optimizer.pt
- Command line
-
hf download hf://Sag1012/machine-translation/EncoderDecoder_2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_2/optimizer.pt
3.02 GB
- Xet hash:
- a660caaf1c291cb9e00a16dca0cbd3c7dfaa942193c7112e8bb791c4c9d7ab3f
- Size of remote file:
- 3.02 GB
- SHA256:
- 41b55c87460f3cb5562ee7274533cc83826cf1cc27a4feba0058e29ac56c8f8f
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