Instructions to use aymanashour/summ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aymanashour/summ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aymanashour/summ")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aymanashour/summ") model = AutoModelForSequenceClassification.from_pretrained("aymanashour/summ") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49bd9c9c041c4207a1931bed38bfc4a22ca1c1e806076e9f16a314f58bd4dc89
- Size of remote file:
- 2.84 GB
- SHA256:
- 99dc61efb97c4e3b1c61b578c282dd806d8b5b8ea5cf1a17fbfddd5f838954e3
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