Instructions to use FrameNetBrasil/reinventa-vit-entity-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrameNetBrasil/reinventa-vit-entity-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="FrameNetBrasil/reinventa-vit-entity-classifier") 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("FrameNetBrasil/reinventa-vit-entity-classifier") model = AutoModelForImageClassification.from_pretrained("FrameNetBrasil/reinventa-vit-entity-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13640bbc4c92453e6ad88149f0fcd84c50b37b1fea44a8bad6f4c8070682c053
- Size of remote file:
- 345 MB
- SHA256:
- 0b8a42dc46a384f67926636f2c6cc4214969a36ecf4f95de68c3f24e8b31551d
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