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") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wesleymorris/SummaryContent")
model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/SummaryContent")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/SummaryContent")