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")# pip install -U transformers accelerate # 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
Download preprocessor_config.json from nafiz09/docvqa: direct link, hf CLI and curl.
- Browser
- Download file 363 Bytes
-
https://huggingface.co/nafiz09/docvqa/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://nafiz09/docvqa@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/nafiz09/docvqa/resolve/refs%2Fpr%2F1/preprocessor_config.json
363 Bytes
| { | |
| "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 | |
| ] | |
| } | |