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
| { | |
| "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" | |
| } | |