Text Classification
Transformers
Safetensors
English
deberta-v2
safety
biosecurity
content-classification
constitutional-classifiers
deberta-v3
dual-use
nsabb
biology
Eval Results (legacy)
text-embeddings-inference
Instructions to use jang1563/constitutional-bioguard-deberta-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jang1563/constitutional-bioguard-deberta-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jang1563/constitutional-bioguard-deberta-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jang1563/constitutional-bioguard-deberta-v1") model = AutoModelForSequenceClassification.from_pretrained("jang1563/constitutional-bioguard-deberta-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1edae890c06bff68fd60dea8617e626cb95e904efac023c58ab9798acf1e9d5
- Size of remote file:
- 738 MB
- SHA256:
- bb13b1c2d70d9f59dfb8d991dba91ff27cc7d0a78c88bfc4104d08f0971ae1e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.