Instructions to use KETI-NLP/vit-patch16-384-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KETI-NLP/vit-patch16-384-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KETI-NLP/vit-patch16-384-roberta-base")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("KETI-NLP/vit-patch16-384-roberta-base") model = AutoModel.from_pretrained("KETI-NLP/vit-patch16-384-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from KETI-NLP/vit-patch16-384-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 380 Bytes
-
https://huggingface.co/KETI-NLP/vit-patch16-384-roberta-base/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://KETI-NLP/vit-patch16-384-roberta-base/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/KETI-NLP/vit-patch16-384-roberta-base/resolve/main/preprocessor_config.json
380 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "VisionTextDualEncoderProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| } | |
| } | |