Instructions to use intexcp/donut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intexcp/donut with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="intexcp/donut")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("intexcp/donut") model = AutoModelForMultimodalLM.from_pretrained("intexcp/donut", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 683 Bytes
f91fe92 | 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 28 29 30 31 32 33 34 35 36 | {
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"disable_grouping": null,
"do_align_long_axis": false,
"do_center_crop": null,
"do_convert_rgb": null,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"do_thumbnail": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "DonutImageProcessorFast",
"image_std": [
0.5,
0.5,
0.5
],
"input_data_format": null,
"processor_class": "DonutProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"height": 1600,
"width": 1200
}
}
|