Instructions to use deetsml/HIMsetfitMultiLabelModel2epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deetsml/HIMsetfitMultiLabelModel2epochs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="deetsml/HIMsetfitMultiLabelModel2epochs")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deetsml/HIMsetfitMultiLabelModel2epochs") model = AutoModel.from_pretrained("deetsml/HIMsetfitMultiLabelModel2epochs") - Notebooks
- Google Colab
- Kaggle
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README.md
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`torch.utils.data.dataloader.DataLoader` of length 14756 with parameters:
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{
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```
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**Loss**:
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`torch.utils.data.dataloader.DataLoader` of length 14756 with parameters:
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```
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{"multi_target_strategy": "multi-output",
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"candidate_labels": ['atmosphere', 'beer list', 'bottomless brunch', 'breakfast', 'brunch', 'business', 'byob', 'casual', 'date night', 'delivery', 'fine dining', 'gluten free', 'good value', 'great cocktails', 'great service', 'happy hour', 'healthy', 'instagrammable', 'kid friendly', 'large groups', 'late night', 'live music', 'lunch', 'night out', 'outdoor seating', 'quick', 'romantic', 'rooftop', 'small plates', 'special event', 'sports/tvs', 'takeout', 'tasting menu', 'vegetarian', 'walk-in', 'wine list'],
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'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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