Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use domenicrosati/deberta-v3-large-model-edit-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use domenicrosati/deberta-v3-large-model-edit-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="domenicrosati/deberta-v3-large-model-edit-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("domenicrosati/deberta-v3-large-model-edit-classifier") model = AutoModelForSequenceClassification.from_pretrained("domenicrosati/deberta-v3-large-model-edit-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d2e9acaef41170df7da4fb3f1a9e1dd06258174e8e61727e6ac0d943803029e
- Size of remote file:
- 3.58 kB
- SHA256:
- 52a39fd58247b8d665943321708f5ca710b95cb2ff401d38e7e007f88cd831c4
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