Instructions to use J-RUM/professions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use J-RUM/professions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="J-RUM/professions") 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("J-RUM/professions") model = AutoModelForImageClassification.from_pretrained("J-RUM/professions") - Notebooks
- Google Colab
- Kaggle
commit files to HF hub
Browse files- README.md +1 -1
- images/engineer.jpg +2 -2
- images/nurse.jpg +2 -2
- pytorch_model.bin +1 -1
- runs/events.out.tfevents.1694171903.a9e904e0d472.7654.0 +3 -0
README.md
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metrics:
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---
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# professions
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metrics:
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type: accuracy
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value: 0.8482142686843872
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---
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# professions
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images/engineer.jpg
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