Instructions to use paru4ik/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paru4ik/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="paru4ik/content")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("paru4ik/content") model = AutoModelForDocumentQuestionAnswering.from_pretrained("paru4ik/content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from paru4ik/content: direct link, hf CLI and curl.
- Browser
- Download file 317 Bytes
-
https://huggingface.co/paru4ik/content/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://paru4ik/content@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/paru4ik/content/resolve/refs%2Fpr%2F1/preprocessor_config.json
317 Bytes
| { | |
| "apply_ocr": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv2FeatureExtractor", | |
| "image_processor_type": "LayoutLMv2ImageProcessor", | |
| "ocr_lang": null, | |
| "processor_class": "LayoutLMv2Processor", | |
| "resample": 2, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "tesseract_config": "" | |
| } | |