Image-to-Text
Transformers
PyTorch
English
Korean
multilingual
veld
feature-extraction
vision, language
pretrained model
custom_code
Instructions to use KETI-NLP/veld-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KETI-NLP/veld-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="KETI-NLP/veld-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KETI-NLP/veld-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 437 Bytes
12fa22c f68803e 12fa22c f68803e 12fa22c f68803e 12fa22c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"auto_map": {
"AutoProcessor": "processing_veld.VELDProcessor"
},
"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": "VELDProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 384,
"width": 384
}
}
|