Text Classification
Transformers
Safetensors
deberta-v2
multi-label-classification
compliance
prism
text-embeddings-inference
Instructions to use Austin-Groundsetter/deberta-prism-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Austin-Groundsetter/deberta-prism-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Austin-Groundsetter/deberta-prism-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Austin-Groundsetter/deberta-prism-v2") model = AutoModelForSequenceClassification.from_pretrained("Austin-Groundsetter/deberta-prism-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17a1b733b45f724337c0a063465422eed78c3de9b6b5f17a95476c441ee97db6
- Size of remote file:
- 4.92 kB
- SHA256:
- 25107a6741ba46ecd2e6943e94dcbeac730cff054ecd5c2e3620278a64f4ffce
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