Instructions to use hf-internal-testing/tiny-random-UdopModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UdopModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UdopModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-UdopModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UdopModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_valid_processor_keys": [ | |
| "images", | |
| "do_resize", | |
| "size", | |
| "resample", | |
| "do_rescale", | |
| "rescale_factor", | |
| "do_normalize", | |
| "image_mean", | |
| "image_std", | |
| "apply_ocr", | |
| "ocr_lang", | |
| "tesseract_config", | |
| "return_tensors", | |
| "data_format", | |
| "input_data_format" | |
| ], | |
| "apply_ocr": true, | |
| "crop_size": 224, | |
| "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, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "tesseract_config": "" | |
| } | |