Instructions to use UVA-MSBA/Mod4_T7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UVA-MSBA/Mod4_T7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UVA-MSBA/Mod4_T7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/Mod4_T7") model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Mod4_T7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9467d7b0f3c32b98c46af128dead572b3b3b5d00738f1a70cd0b267e05bb431
- Size of remote file:
- 3.44 kB
- SHA256:
- 4e01b5d8e679a9d125ce721dc930671c2a334c4f77eff5ec406bbd50f1850c68
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