Keras
Joblib
Safetensors
PyTorch
English
multimodal
document-classification
distilbert
efficientnet
late-fusion
rvl-cdip
tensorflow
scikit-learn
Instructions to use gonzalobosque/multimodal-document-intelligence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use gonzalobosque/multimodal-document-intelligence 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://gonzalobosque/multimodal-document-intelligence") - Notebooks
- Google Colab
- Kaggle
Download image_model/model.keras from gonzalobosque/multimodal-document-intelligence: direct link, hf CLI and curl.
- Browser
- Download file 54.1 MB
-
https://huggingface.co/gonzalobosque/multimodal-document-intelligence/resolve/main/image_model/model.keras
- Command line
-
hf download hf://gonzalobosque/multimodal-document-intelligence/image_model/model.keras
-
curl -L -o model.keras https://huggingface.co/gonzalobosque/multimodal-document-intelligence/resolve/main/image_model/model.keras
54.1 MB
- Xet hash:
- 166c4247099d6d0851d08dee75cab4847cf465dfa679ad0a54cc3a1b531eea1b
- Size of remote file:
- 54.1 MB
- SHA256:
- f46c30bedc0761f270af41a3e260352787ac5327d0e3abd971c24416e5e98eb5
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