Instructions to use npnam693/layoutlm-sroie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npnam693/layoutlm-sroie with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="npnam693/layoutlm-sroie")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("npnam693/layoutlm-sroie") model = AutoModelForTokenClassification.from_pretrained("npnam693/layoutlm-sroie", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from npnam693/layoutlm-sroie: direct link, hf CLI and curl.
- Browser
- Download file 317 Bytes
-
https://huggingface.co/npnam693/layoutlm-sroie/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://npnam693/layoutlm-sroie/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/npnam693/layoutlm-sroie/resolve/main/preprocessor_config.json
317 Bytes
| { | |
| "apply_ocr": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv2FeatureExtractor", | |
| "image_processor_type": "LayoutLMv2ImageProcessor", | |
| "ocr_lang": null, | |
| "processor_class": "LayoutLMv2Processor", | |
| "resample": 2, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "tesseract_config": "" | |
| } | |