Instructions to use TalentoTechIA/juanprueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TalentoTechIA/juanprueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TalentoTechIA/juanprueba") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("TalentoTechIA/juanprueba") model = AutoModelForImageClassification.from_pretrained("TalentoTechIA/juanprueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from TalentoTechIA/juanprueba: direct link, hf CLI and curl.
- Browser
- Download file 386 Bytes
-
https://huggingface.co/TalentoTechIA/juanprueba/resolve/main/all_results.json
- Command line
-
hf download hf://TalentoTechIA/juanprueba/all_results.json
-
curl -L -o all_results.json https://huggingface.co/TalentoTechIA/juanprueba/resolve/main/all_results.json
386 Bytes
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.9699248120300752, | |
| "eval_loss": 0.137566477060318, | |
| "eval_runtime": 2.377, | |
| "eval_samples_per_second": 55.954, | |
| "eval_steps_per_second": 7.152, | |
| "total_flos": 3.205097416476426e+17, | |
| "train_loss": 0.1284546733332368, | |
| "train_runtime": 303.0865, | |
| "train_samples_per_second": 13.646, | |
| "train_steps_per_second": 1.716 | |
| } |