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Authorship verification module.
Uses a fine-tuned model to verify whether the corrected output
could plausibly have been written by the same author as the input.
Target: > 0.80 same-author probability.
"""
from typing import Tuple
from loguru import logger
import torch
import torch.nn.functional as F
class AuthorshipVerifier:
"""Verifies authorship consistency between input and output text."""
def __init__(self, model_name: str = "roberta-base"):
try:
from sentence_transformers import SentenceTransformer
self.model = SentenceTransformer(model_name, device="cpu")
logger.info(f"AuthorshipVerifier loaded with {model_name}")
except Exception as e:
logger.warning(f"Failed to load authorship model: {e}")
self.model = None
def verify(self, text_a: str, text_b: str) -> float:
"""Return probability that both texts were written by the same author.
Uses sentence embedding similarity as a proxy for authorship.
Higher cosine similarity suggests same author.
"""
if self.model is None:
return 0.5 # Neutral score if model unavailable
if not text_a or not text_b:
return 0.5
try:
embeddings = self.model.encode([text_a, text_b], convert_to_tensor=True)
sim = F.cosine_similarity(
embeddings[0].unsqueeze(0),
embeddings[1].unsqueeze(0),
)
# Scale similarity to [0, 1] probability
# Cosine similarity is already in [-1, 1], shift to [0, 1]
prob = (sim.item() + 1.0) / 2.0
return prob
except Exception as e:
logger.warning(f"Authorship verification failed: {e}")
return 0.5
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