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
File size: 1,375 Bytes
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"_name_or_path": "distilbert-base-uncased",
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "letter",
"1": "form",
"2": "email",
"3": "handwritten",
"4": "advertisement",
"5": "scientific report",
"6": "scientific publication",
"7": "specification",
"8": "file folder",
"9": "news article",
"10": "budget",
"11": "invoice",
"12": "presentation",
"13": "questionnaire",
"14": "resume",
"15": "memo"
},
"initializer_range": 0.02,
"label2id": {
"advertisement": 4,
"budget": 10,
"email": 2,
"file folder": 8,
"form": 1,
"handwritten": 3,
"invoice": 11,
"letter": 0,
"memo": 15,
"news article": 9,
"presentation": 12,
"questionnaire": 13,
"resume": 14,
"scientific publication": 6,
"scientific report": 5,
"specification": 7
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "float32",
"transformers_version": "4.49.0",
"vocab_size": 30522
}
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