Instructions to use JLB-JLB/Model_folder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLB-JLB/Model_folder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JLB-JLB/Model_folder") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("JLB-JLB/Model_folder") model = AutoModelForImageClassification.from_pretrained("JLB-JLB/Model_folder") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 4.0, | |
| "eval_loss": 0.046887390315532684, | |
| "eval_matthews_correlation": 0.9888040854737966, | |
| "eval_runtime": 3.2987, | |
| "eval_samples_per_second": 40.319, | |
| "eval_steps_per_second": 5.154, | |
| "total_flos": 3.254692734332928e+17, | |
| "train_loss": 0.01579295479071637, | |
| "train_runtime": 122.9749, | |
| "train_samples_per_second": 33.633, | |
| "train_steps_per_second": 1.073 | |
| } |