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:
- ceaaafcabdfd2eea9ab2cbe23cdfc21933d2a15cec3ba45b571297f94be4607c
- Size of remote file:
- 595 MB
- SHA256:
- 37a5b3bd27d2b12f6125ac17b5516f3a84c3bc26e25fb3741712df74cd6cbe1f
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