Instructions to use wesleymorris/SummaryContent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleymorris/SummaryContent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/SummaryContent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleymorris/SummaryContent") model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/SummaryContent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a1fa5f6912c6536b3326fbe03dcf239d83fedcfb03841dcd7d4f85884111a46
- Size of remote file:
- 3.31 kB
- SHA256:
- c66b5399d0b5c3076e055121b9840ce89de8bd9c4b38b28f48a53d8984637a24
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