Instructions to use hf-internal-testing/tiny-random-LayoutLMv3Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv3Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LayoutLMv3Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv3Model") - Notebooks
- Google Colab
- Kaggle
File size: 457 Bytes
69725e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"apply_ocr": true,
"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,
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
},
"tesseract_config": ""
}
|