Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use vitthalbhandari/deberta-v3-base_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vitthalbhandari/deberta-v3-base_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vitthalbhandari/deberta-v3-base_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vitthalbhandari/deberta-v3-base_base") model = AutoModelForSequenceClassification.from_pretrained("vitthalbhandari/deberta-v3-base_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f6436703038ab5186cffa16962112433bafc638fc0b213111a9334fe92e12fd
- Size of remote file:
- 738 MB
- SHA256:
- 4cda17fedd5114da755b80d693903ee285d4fc0e5b56efec5ff0c7fe1831731b
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