Instructions to use Farideh/dataset_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Farideh/dataset_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Farideh/dataset_model") 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_model") model = AutoModelForImageClassification.from_pretrained("Farideh/dataset_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Farideh/dataset_model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/Farideh/dataset_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Farideh/dataset_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Farideh/dataset_model/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- f72096fe11c85a8b02d4f76aa7f25e02e422a108df5d013c398d7473b09d4f80
- Size of remote file:
- 3.39 kB
- SHA256:
- 37298d238300243cce71da5ef4091ca72446f9de3426ef092b88fa25b093036b
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