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:
- f10daff84b7d2c766ba34c8380989a64d2420156bfe0afd955caaa2818b9abba
- Size of remote file:
- 3.04 GB
- SHA256:
- d800632568627ed3eca24fcd1874488f9a90aad251cdea3972aae1ae8c950979
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