Instructions to use Farideh/dataset_model4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Farideh/dataset_model4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Farideh/dataset_model4") 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("Farideh/dataset_model4") model = AutoModelForImageClassification.from_pretrained("Farideh/dataset_model4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Farideh/dataset_model4: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/Farideh/dataset_model4/resolve/main/training_args.bin
- Command line
-
hf download hf://Farideh/dataset_model4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Farideh/dataset_model4/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 5561b7123c5739478a06743049f82497a91bfbd25ad5d15c8148a21ae70dd60c
- Size of remote file:
- 3.39 kB
- SHA256:
- 1ceee5b0fc9c7abed13ec81451481fb983ca0463f78d525787d122f9ccc46f66
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