Instructions to use ryanznie/layoutlmv3-lora-invoice-number with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ryanznie/layoutlmv3-lora-invoice-number with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base") model = PeftModel.from_pretrained(base_model, "ryanznie/layoutlmv3-lora-invoice-number") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from ryanznie/layoutlmv3-lora-invoice-number: direct link, hf CLI and curl.
- Browser
- Download file 444 Bytes
-
https://huggingface.co/ryanznie/layoutlmv3-lora-invoice-number/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://ryanznie/layoutlmv3-lora-invoice-number/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/ryanznie/layoutlmv3-lora-invoice-number/resolve/main/preprocessor_config.json
444 Bytes
| { | |
| "apply_ocr": false, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "LayoutLMv3ImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "ocr_lang": null, | |
| "processor_class": "LayoutLMv3Processor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "tesseract_config": "" | |
| } | |