Document Question Answering
Transformers
PyTorch
English
layoutlmv3
DocVQA
Document Question Answering
Document Visual Question Answering
Instructions to use rubentito/layoutlmv3-base-mpdocvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubentito/layoutlmv3-base-mpdocvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="rubentito/layoutlmv3-base-mpdocvqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 321 Bytes
803eaf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"apply_ocr": false,
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "LayoutLMv3FeatureExtractor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"ocr_lang": null,
"processor_class": "LayoutLMv3Processor",
"resample": 2,
"size": 224
}
|