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_5/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_5/optimizer.pt
- Command line
-
hf download hf://Sag1012/machine-translation/EncoderDecoder_5/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_5/optimizer.pt
3.02 GB
- Xet hash:
- 28a72190aa9c4d782a365020ccc079dfffa387383876eebe98d44427a1fa5b7c
- Size of remote file:
- 3.02 GB
- SHA256:
- c40794d4e2bc8bd762b7c9731a6ffbf75e250eabc0df0681fb37835524e1ee86
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