README.md

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title: README
emoji: ❤️
colorFrom: red
colorTo: red
sdk: streamlit
app_file: app.py
pinned: false

Welcome to the Keras Working Group 🤗 Please join us on keras-working-group channel on Discord 👾!

Hugging Face makes it easy to collaboratively build and showcase your Keras models!
You can collaborate with your organization, upload and showcase your own models in your profile, or join us in this organization to demo Keras examples! ❤️

Keras.io

Push your Keras models to Hub ❤️

Find all Keras models on the 🤗 Hub

To upload your Keras models to the Hub, you can use the push_to_hub_keras function.

!pip install huggingface-hub
!huggingface-cli login
from huggingface_hub.keras_mixin import push_to_hub_keras
push_to_hub_keras(model = model, repo_url = "https://huggingface.co/your-username/your-awesome-model")
    

To load Keras models from the 🤗Hub, use from_pretrained_keras function.

!pip install huggingface-hub
!huggingface-cli login
from huggingface_hub.keras_mixin import from_pretrained_keras
from_pretrained_keras("your-username/your-awesome-model)
    

If you'd like to upload 🤗Transformers based Keras checkpoints and let us host your metrics interactively in the repo in with TensorBoard, use PushToHubCallback like follows:

!pip install huggingface-hub
!huggingface-cli login

from transformers.keras_callbacks import PushToHubCallback
from tensorflow.keras.callbacks import TensorBoard
tensorboard_callback = TensorBoard(log_dir = "./logs/tensorboard)
push_to_hub_callback = PushToHubCallback(output_dir="./logs", 
                                        tokenizer=tokenizer,
                                        hub_model_id=model_id)
callbacks = [tensorboard_callback, push_to_hub_callback]
model.fit(..., callbacks=callbacks, ...)