Document Question Answering
Transformers
PyTorch
vision-encoder-decoder
image-text-to-text
donut
image-to-text
vision
Instructions to use nafiz09/docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nafiz09/docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="nafiz09/docvqa")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nafiz09/docvqa") model = AutoModelForMultimodalLM.from_pretrained("nafiz09/docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 363 Bytes
8a87927 | 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 | {
"do_align_long_axis": false,
"do_normalize": true,
"do_pad": true,
"do_resize": true,
"do_thumbnail": true,
"feature_extractor_type": "DonutFeatureExtractor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"processor_class": "DonutProcessor",
"resample": 2,
"size": [
1920,
2560
]
}
|