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 text_model/special_tokens_map.json from gonzalobosque/multimodal-document-intelligence: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/gonzalobosque/multimodal-document-intelligence/resolve/main/text_model/special_tokens_map.json
- Command line
-
hf download hf://gonzalobosque/multimodal-document-intelligence/text_model/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/gonzalobosque/multimodal-document-intelligence/resolve/main/text_model/special_tokens_map.json
125 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |