Instructions to use BadreddineHug/LayoutLMv3_funds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_funds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_funds")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_funds") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_funds", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from BadreddineHug/LayoutLMv3_funds: direct link, hf CLI and curl.
- Browser
- Download file 502 Bytes
-
https://huggingface.co/BadreddineHug/LayoutLMv3_funds/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://BadreddineHug/LayoutLMv3_funds@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/BadreddineHug/LayoutLMv3_funds/resolve/refs%2Fpr%2F1/preprocessor_config.json
502 Bytes
| { | |
| "apply_ocr": false, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv3FeatureExtractor", | |
| "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": "" | |
| } | |