Text Classification
Transformers
Safetensors
multi-head-deberta-for-sequence-classification
custom_code
Instructions to use wandb/celadon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wandb/celadon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wandb/celadon", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("wandb/celadon", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download configuration_deberta_multi.py from wandb/celadon: direct link, hf CLI and curl.
- Browser
- Download file 273 Bytes
-
https://huggingface.co/wandb/celadon/resolve/main/configuration_deberta_multi.py
- Command line
-
hf download hf://wandb/celadon/configuration_deberta_multi.py
-
curl -L -o configuration_deberta_multi.py https://huggingface.co/wandb/celadon/resolve/main/configuration_deberta_multi.py
273 Bytes
| from transformers import DebertaV2Config | |
| class MultiHeadDebertaV2Config(DebertaV2Config): | |
| model_type = "multi-head-deberta-for-sequence-classification" | |
| def __init__(self, num_heads=5, **kwargs): | |
| self.num_heads = num_heads | |
| super().__init__(**kwargs) |