Instructions to use MatthewFrank/w266FinalProject with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatthewFrank/w266FinalProject with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MatthewFrank/w266FinalProject")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MatthewFrank/w266FinalProject") model = AutoModelForSequenceClassification.from_pretrained("MatthewFrank/w266FinalProject", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc9bc3aac4f03389983b1e8317c9129b45561c3fd60c024899824aeae448ee68
- Size of remote file:
- 438 MB
- SHA256:
- f9ca5b2a417474556206f7df661544ccc9cabc14955040a23c7c7925c7d9e6cb
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