Instructions to use hebashakeel/roberta-wellness-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hebashakeel/roberta-wellness-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hebashakeel/roberta-wellness-classifier", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hebashakeel/roberta-wellness-classifier") model = AutoModelForSequenceClassification.from_pretrained("hebashakeel/roberta-wellness-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfadc53bd8c27ff90ba99b8f81fd4fde5b7e95502135f1d2751c1fc19f62092a
- Size of remote file:
- 499 MB
- SHA256:
- dd33f5c9235ec32cc5f1227116a08f41225b37e3ea6e7db12d841a00017836ff
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