Instructions to use wltjr1007/LEAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wltjr1007/LEAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wltjr1007/LEAR", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wltjr1007/LEAR", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 565 Bytes
dd983c1 | 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 | {
"architectures": [
"ConditionalUNet"
],
"auto_map": {
"AutoConfig": "configuration_conditional_unet.ConditionalUNetConfig",
"AutoModel": "modeling_conditional_unet.ConditionalUNet"
},
"encoder_rep": "evanrsl/resnet-Alzheimer",
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2,
"LABEL_3": 3
},
"model_type": "conditional-unet",
"num_channels": 3,
"torch_dtype": "float32",
"transformers_version": "4.46.2"
}
|