Instructions to use diah/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diah/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="diah/layoutlm-funsd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("diah/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("diah/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 474 Bytes
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"_valid_processor_keys": [
"images",
"do_resize",
"size",
"resample",
"apply_ocr",
"ocr_lang",
"tesseract_config",
"return_tensors",
"data_format",
"input_data_format"
],
"apply_ocr": true,
"do_resize": true,
"image_processor_type": "LayoutLMv2ImageProcessor",
"ocr_lang": null,
"processor_class": "LayoutLMv2Processor",
"resample": 2,
"size": {
"height": 224,
"width": 224
},
"tesseract_config": ""
}
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