Instructions to use researchaccount/sa_sub2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use researchaccount/sa_sub2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="researchaccount/sa_sub2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("researchaccount/sa_sub2") model = AutoModelForSequenceClassification.from_pretrained("researchaccount/sa_sub2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d031f27c1d99ac8030ec231880f6f974deaa58f03291e2fcf06d88618d424162
- Size of remote file:
- 651 MB
- SHA256:
- b0ec094da63e44e40748113673e0bd2ff0b396dabfdb57954aacec191cf8ee35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.