Instructions to use JW17/L32-3B-UC-BatchSum-seed78 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JW17/L32-3B-UC-BatchSum-seed78 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JW17/L32-3B-UC-BatchSum-seed78")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JW17/L32-3B-UC-BatchSum-seed78") model = AutoModelForSequenceClassification.from_pretrained("JW17/L32-3B-UC-BatchSum-seed78") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3a5375f0554bf429c1330a2de75ace318f4d673ef36e7b3afd7f63369ab577c
- Size of remote file:
- 5.69 kB
- SHA256:
- 323b6e960d40f8ca716bee91ffcc45b5f64906b3d0a3893256e590857038c11e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.