Instructions to use UVA-MSBA/Mod4Team12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UVA-MSBA/Mod4Team12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UVA-MSBA/Mod4Team12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/Mod4Team12") model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Mod4Team12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e2a82f11fe6aaee25d0b15a1d52d76f2d181ccc49bddb10f256db60472fb12c
- Size of remote file:
- 557 MB
- SHA256:
- bc4a0d830871e8c2dda6ec9afd207ef8e2235632f4b454eeca86350d556421dd
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