Instructions to use lucrbrtv/idm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lucrbrtv/idm with Transformers:
# Load model directly from transformers import IDM model = IDM.from_pretrained("lucrbrtv/idm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -13,16 +13,16 @@ I've calculated the balanced F-score (F1) for each label:
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| Action | F1 Score | Note |
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| ATTACK | 0.564 | |
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| FWD | 0.809 ||
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| LEFT | 0.586 | |
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| RIGHT | 0.607 | |
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| SPEED | 0.000 | Not used in training |
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| TURN_L | 0.541 | |
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| TURN_R | 0.513 | |
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| USE | 0.453 | Lowest score |
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| Accuracy | 0.834| 83.35% |
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| Loss (eval) | 0.375 | |
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Training:
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| Action | F1 Score | Note |
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| F1/ATTACK | 0.564 | |
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| F1/CK | 0.581 | |
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| F1/FWD | 0.809 ||
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| F1/LEFT | 0.586 | |
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| F1/RIGHT | 0.607 | |
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| F1/SPEED | 0.000 | Not used in training |
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| F1/TURN_L | 0.541 | |
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| F1/TURN_R | 0.513 | |
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| F1/USE | 0.453 | Lowest score |
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| Accuracy (eval) | 0.834| 83.35% |
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| Loss (eval) | 0.375 | |
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Training:
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