Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use dv347/deberta-v3-base_smcalflow-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-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-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dv347/deberta-v3-base_smcalflow-classifier") model = AutoModelForSequenceClassification.from_pretrained("dv347/deberta-v3-base_smcalflow-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f009b9590f5f52c0e0b87a0037dd5d7a27a622a73bac6428359361ccf7c08e55
- Size of remote file:
- 5.27 kB
- SHA256:
- 6afbc235794e46dca9d6d4d15e95bfc109e700d807ec0df5902cd57b5a684818
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