Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use dv347/deberta-v3-base_smcalflow_balanced-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dv347/deberta-v3-base_smcalflow_balanced-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dv347/deberta-v3-base_smcalflow_balanced-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dv347/deberta-v3-base_smcalflow_balanced-classifier") model = AutoModelForSequenceClassification.from_pretrained("dv347/deberta-v3-base_smcalflow_balanced-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c309cefbf462823b56f4d82b65d640a95a374668d774068cbc3142c65a393acb
- Size of remote file:
- 5.33 kB
- SHA256:
- 72203c91cc3f7663a9d27860d5776e22b3d622f69755401139cbb6723041e40b
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