Instructions to use Cournane/roberta-base-reduced-Outer_pattern with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-reduced-Outer_pattern with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-reduced-Outer_pattern")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-reduced-Outer_pattern") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-reduced-Outer_pattern") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72dc5e5125fd95f86a48d4a0b7247eed259d8be4729bd4b8b5a3270b57813529
- Size of remote file:
- 3.96 kB
- SHA256:
- 8ae19fe325419aa9ecbb575c47d44593c864cd60428164e162543aafd86cb92d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.