Instructions to use microsoft/deberta-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-base-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-base-mnli") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb432d312b8fb87d4bafe65557803c37427f03067cb44af0457ffd33b5a52f90
- Size of remote file:
- 557 MB
- SHA256:
- d3075b64720952dbb968398741968d4f8ce1d803b6ba2e7cf9a880c69d324b99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.