Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use dv347/deberta-v3-base_smcalflow-classifier_20 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_20 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_20")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dv347/deberta-v3-base_smcalflow-classifier_20") model = AutoModelForSequenceClassification.from_pretrained("dv347/deberta-v3-base_smcalflow-classifier_20", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 871de79f17528bb59372b7376d21751a09946f67e995ff67c30f1d94d0f2ff1f
- Size of remote file:
- 5.27 kB
- SHA256:
- dc86cb4ec6ec5b6a25053885b4284a837d8987e469690c0974825b5bb15d0ca3
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