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/scheduler.pt from Sag1012/machine-translation: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_3/scheduler.pt
- Command line
-
hf download hf://Sag1012/machine-translation/EncoderDecoder_3/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/Sag1012/machine-translation/resolve/main/EncoderDecoder_3/scheduler.pt
1.06 kB
- Xet hash:
- 72a99a0b59d66bd2cd355d847ccdc122d138a0d49ebe79da51ffe72f8c264e23
- Size of remote file:
- 1.06 kB
- SHA256:
- 85c63a87a59ffa140e5921aa42b7ed28d9b1aebaac99447aa944eeeae12fe1d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.