Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use dv347/deberta-v3-base_smcalflow-classifier_50 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_50 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_50")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dv347/deberta-v3-base_smcalflow-classifier_50") model = AutoModelForSequenceClassification.from_pretrained("dv347/deberta-v3-base_smcalflow-classifier_50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a460549d69635c800fcce7d4b06f34e0555d3e79f6ee3900f39b28417ce5ad9
- Size of remote file:
- 5.33 kB
- SHA256:
- 2790f809181ccc01bcddf3bed058f5b52f3351d9fdae6f497d322f24b2a0f1f4
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