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:
- c585d303c31254ad712bf9dcf1fa5ba7fd81f5bb573557d67b764c27cabde1f1
- Size of remote file:
- 1.74 GB
- SHA256:
- a1c5214ef4e0ca5764cb63eaa454b213ac39978d89458d46bd0aa2ae94b988f7
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