Instructions to use ongp/70btclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongp/70btclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ongp/70btclassification") 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("ongp/70btclassification") model = AutoModelForImageClassification.from_pretrained("ongp/70btclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ongp/70btclassification: direct link, hf CLI and curl.
- Browser
- Download file 873 Bytes
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https://huggingface.co/ongp/70btclassification/resolve/main/README.md
- Command line
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hf download hf://ongp/70btclassification/README.md
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curl -L -o README.md https://huggingface.co/ongp/70btclassification/resolve/main/README.md
873 Bytes
| tags: | |
| - autotrain | |
| - vision | |
| - image-classification | |
| datasets: | |
| - ongp/autotrain-data-312312 | |
| widget: | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg | |
| example_title: Tiger | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg | |
| example_title: Teapot | |
| - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg | |
| example_title: Palace | |
| co2_eq_emissions: | |
| emissions: 0.011291420892523918 | |
| # Model Trained Using AutoTrain | |
| - Problem type: Multi-class Classification | |
| - Model ID: 2723080856 | |
| - CO2 Emissions (in grams): 0.0113 | |
| ## Validation Metrics | |
| - Loss: 0.877 | |
| - Accuracy: 0.708 | |
| - Macro F1: 0.695 | |
| - Micro F1: 0.708 | |
| - Weighted F1: 0.704 | |
| - Macro Precision: 0.703 | |
| - Micro Precision: 0.708 | |
| - Weighted Precision: 0.711 | |
| - Macro Recall: 0.699 | |
| - Micro Recall: 0.708 | |
| - Weighted Recall: 0.708 |