Text Classification
Transformers
Safetensors
roberta
safety
judge
probguard
calibeval
text-embeddings-inference
Instructions to use hxz-sec/CalibEval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hxz-sec/CalibEval with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hxz-sec/CalibEval")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hxz-sec/CalibEval") model = AutoModelForSequenceClassification.from_pretrained("hxz-sec/CalibEval", device_map="auto") - Notebooks
- Google Colab
- Kaggle
CalibEval
CalibEval is a RoBERTa-large sequence-classification safety judge used by ProbGuard to label sampled continuations during calibration and evaluation.
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
repo_id = "hxz-sec/CalibEval"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
The model outputs two labels for safety classification. In the EarlyGuard evaluation scripts, the unsafe probability is read from the positive class and thresholded at 0.5 by default.
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Model tree for hxz-sec/CalibEval
Base model
FacebookAI/roberta-large