Instructions to use khalidalt/DeBERTa-v3-large-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khalidalt/DeBERTa-v3-large-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="khalidalt/DeBERTa-v3-large-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("khalidalt/DeBERTa-v3-large-mnli") model = AutoModelForSequenceClassification.from_pretrained("khalidalt/DeBERTa-v3-large-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e90a7ed14ee17636fb0fcfe250fe8f93154af305a275f24aa142bafd965fd6bc
- Size of remote file:
- 1.74 GB
- SHA256:
- 6421f561358fc200624b17c798f3b8f9a9c9185b69867765a66b0499df6be055
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.