Instructions to use UVA-MSBA/Signals with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UVA-MSBA/Signals with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UVA-MSBA/Signals")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/Signals") model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Signals", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 919078d3cd9e553e0e2ff741ad61bbfaabb77b670ac2366096884fde96402419
- Size of remote file:
- 268 MB
- SHA256:
- c29e0eb0e414110ca12d27c2bee21711cbe88f0193bfc6caf756b3c73d91050c
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