Instructions to use SummerSigh/Safety-Policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerSigh/Safety-Policy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SummerSigh/Safety-Policy")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SummerSigh/Safety-Policy") model = AutoModelForSequenceClassification.from_pretrained("SummerSigh/Safety-Policy") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This is a finetuned DeBERTav3 model from https://huggingface.co/sileod/deberta-v3-base-tasksource-nli.
Model Details
This model was finetuned on policy data related to the rules laid out in the Sparrow paper by Deepmind.
Model Description
- Developed by: SummerSigh
- Model type: DeBERTav3
- Language(s) (NLP): English
- License: apache-2.0
- Finetuned from model: https://huggingface.co/sileod/deberta-v3-base-tasksource-nli
Uses
Given an input text, this model will output "KEPT" (0) or "BROKE" (1). KEPT indicates that the text keeps the policies finetuned in mind, while BROKE means that it broke one or more of the policies.
- Downloads last month
- 2