Instructions to use diseases-risk-factors/binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diseases-risk-factors/binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="diseases-risk-factors/binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("diseases-risk-factors/binary-classification") model = AutoModelForSequenceClassification.from_pretrained("diseases-risk-factors/binary-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb17595ef938c3e2daff3821042ba49bbbe3c8cbf60792662a914a97bfd9b49d
- Size of remote file:
- 4.98 kB
- SHA256:
- d65a919ed31d0870e4564d74008195aa3fd56d2603aa298cff0a62354579ab07
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