Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use bdpc/DeBERT_50K_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bdpc/DeBERT_50K_steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bdpc/DeBERT_50K_steps")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bdpc/DeBERT_50K_steps") model = AutoModelForSequenceClassification.from_pretrained("bdpc/DeBERT_50K_steps") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 113f6f880cbb8039d028b39516d32307ac4bd36db7a3e4433c53f57fd1a6a595
- Size of remote file:
- 4.09 kB
- SHA256:
- 097f22deb737cb0ef2d1d8a83ef78af6e067bb18f68cf867f30cb5c51f2007e6
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