Instructions to use Gaurav20/Encoder_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Gaurav20/Encoder_Model 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://Gaurav20/Encoder_Model") - Notebooks
- Google Colab
- Kaggle
Download encoder_model.h5 from Gaurav20/Encoder_Model: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/Gaurav20/Encoder_Model/resolve/4cd4826f6d0bd1b4bbc0f8a5eb5340082e818da3/encoder_model.h5
- Command line
-
hf download hf://Gaurav20/Encoder_Model@4cd4826f6d0bd1b4bbc0f8a5eb5340082e818da3/encoder_model.h5
-
curl -L -o encoder_model.h5 https://huggingface.co/Gaurav20/Encoder_Model/resolve/4cd4826f6d0bd1b4bbc0f8a5eb5340082e818da3/encoder_model.h5
1.27 GB
- Xet hash:
- a5eddc83cebe1cbf5b0a88a7bdc97a4528c8682c5917faef277709fad64c7127
- Size of remote file:
- 1.27 GB
- SHA256:
- 7e91faeb9bde4ba4faf966d281c6473f286d2cc293312712ba88b4702b60b377
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