Instructions to use Farideh/dataset_model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Farideh/dataset_model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Farideh/dataset_model2") 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_model2") model = AutoModelForImageClassification.from_pretrained("Farideh/dataset_model2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Farideh/dataset_model2: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/Farideh/dataset_model2/resolve/main/training_args.bin
- Command line
-
hf download hf://Farideh/dataset_model2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Farideh/dataset_model2/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 95aba69635ab6d7abf366693fa0a7c4d8a4bd6a8b9b38fcbb8b276aca8d3681e
- Size of remote file:
- 3.39 kB
- SHA256:
- 032ee4a3f588b9581a7e8ac4380a924958cc93b8b44d505ad878bdc7fef779cb
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