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:
- 0c97a08e97ac82b0d215dc8d693d09b39a8eda8a3785b8d884c1ba68d8c4d6b0
- Size of remote file:
- 3.44 kB
- SHA256:
- 8183d170fbe0028ef306a44f2cec189822cbed364f79cac76877a8ac5bdd6095
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