Instructions to use jwpark84/layoutlm-funsd1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jwpark84/layoutlm-funsd1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jwpark84/layoutlm-funsd1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jwpark84/layoutlm-funsd1") model = AutoModelForTokenClassification.from_pretrained("jwpark84/layoutlm-funsd1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from jwpark84/layoutlm-funsd1: direct link, hf CLI and curl.
- Browser
- Download file 474 Bytes
-
https://huggingface.co/jwpark84/layoutlm-funsd1/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://jwpark84/layoutlm-funsd1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/jwpark84/layoutlm-funsd1/resolve/main/preprocessor_config.json
474 Bytes
| { | |
| "_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": "" | |
| } | |