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 class_order.json from gonzalobosque/multimodal-document-intelligence: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/gonzalobosque/multimodal-document-intelligence/resolve/main/class_order.json
- Command line
-
hf download hf://gonzalobosque/multimodal-document-intelligence/class_order.json
-
curl -L -o class_order.json https://huggingface.co/gonzalobosque/multimodal-document-intelligence/resolve/main/class_order.json
261 Bytes
| [ | |
| "letter", | |
| "form", | |
| "email", | |
| "handwritten", | |
| "advertisement", | |
| "scientific report", | |
| "scientific publication", | |
| "specification", | |
| "file folder", | |
| "news article", | |
| "budget", | |
| "invoice", | |
| "presentation", | |
| "questionnaire", | |
| "resume", | |
| "memo" | |
| ] | |