File size: 530 Bytes
ed66ac7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | from prompt_injection.evaluators.base import PromptEvaluator
from sentence_transformers import SentenceTransformer
import numpy as np
class MiniLMEmbeddingPromptEvaluator(PromptEvaluator):
def __init__(self) -> None:
super().__init__()
self.model=SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')
def eval_sample(self,sample):
try:
return self.model.encode([sample])
except Exception as err:
return np.nan
def get_name(self):
return 'Embedding' |