Instructions to use Cournane/roberta-base-reduced-Upper_fabric with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cournane/roberta-base-reduced-Upper_fabric with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cournane/roberta-base-reduced-Upper_fabric")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cournane/roberta-base-reduced-Upper_fabric") model = AutoModelForSequenceClassification.from_pretrained("Cournane/roberta-base-reduced-Upper_fabric") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 933370c92e978cfa0f5fed41408bc028e11262a758f3629e102387ff7c1538b7
- Size of remote file:
- 3.9 kB
- SHA256:
- 64a4cfc11f32806ca97b2b9311a2547111e6aba2ab4146d91c4ed2892deefe30
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.