Instructions to use Saul98lm/prueba2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saul98lm/prueba2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Saul98lm/prueba2") 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("Saul98lm/prueba2") model = AutoModelForImageClassification.from_pretrained("Saul98lm/prueba2") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 500
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
config.json
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"problem_type": "single_label_classification",
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"transformers_version": "4.33.
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"transformers_version": "4.33.2"
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pytorch_model.bin
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training_args.bin
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