Instructions to use hf-internal-testing/tiny-random-LayoutLMv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LayoutLMv2Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-LayoutLMv2Model: direct link, hf CLI and curl.
- Browser
- Download file 273 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-LayoutLMv2Model/resolve/6a1b510769b344979a910a7d0bade613a9ec2dfc/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-LayoutLMv2Model@6a1b510769b344979a910a7d0bade613a9ec2dfc/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-LayoutLMv2Model/resolve/6a1b510769b344979a910a7d0bade613a9ec2dfc/preprocessor_config.json
273 Bytes
| { | |
| "apply_ocr": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv2FeatureExtractor", | |
| "image_processor_type": "LayoutLMv2ImageProcessor", | |
| "ocr_lang": null, | |
| "resample": 2, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "tesseract_config": "" | |
| } | |