Instructions to use aymanashour/summ2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aymanashour/summ2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aymanashour/summ2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aymanashour/summ2") model = AutoModelForSequenceClassification.from_pretrained("aymanashour/summ2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f50908308791aaa9e49b0448dc487b6b3226c6910297f648d9d05729a20e4d2b
- Size of remote file:
- 3.31 kB
- SHA256:
- d0c43171afd2f0a5e56cc53b9e1be66fa8d603f0eeeed78b67fe95453270487d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.