Instructions to use nesanchezo/model_prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nesanchezo/model_prueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nesanchezo/model_prueba") 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("nesanchezo/model_prueba") model = AutoModelForImageClassification.from_pretrained("nesanchezo/model_prueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from nesanchezo/model_prueba: direct link, hf CLI and curl.
- Browser
- Download file 201 Bytes
-
https://huggingface.co/nesanchezo/model_prueba/resolve/main/eval_results.json
- Command line
-
hf download hf://nesanchezo/model_prueba/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/nesanchezo/model_prueba/resolve/main/eval_results.json
201 Bytes
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.9605734767025089, | |
| "eval_loss": 0.1888817846775055, | |
| "eval_runtime": 50.8855, | |
| "eval_samples_per_second": 5.483, | |
| "eval_steps_per_second": 0.688 | |
| } |