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# metrics.py
import torch
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline
# Load Sentence-BERT model for semantic similarity
sentence_bert_model = SentenceTransformer('all-MiniLM-L6-v2')
# Load a pre-trained emotion classifier
emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=None)
def compute_semantic_similarity(original_comment, paraphrased_comment):
"""
Compute the semantic similarity between the original and paraphrased comments using Sentence-BERT.
Returns a score between 0 and 1 (higher is better).
"""
original_embedding = sentence_bert_model.encode(original_comment, convert_to_tensor=True)
paraphrased_embedding = sentence_bert_model.encode(paraphrased_comment, convert_to_tensor=True)
similarity_score = util.cos_sim(original_embedding, paraphrased_embedding)[0][0].item()
return round(similarity_score, 2)
def compute_emotion_shift(original_comment, paraphrased_comment):
"""
Compute the shift in emotional tone between the original and paraphrased comments.
Returns the dominant emotion labels for both comments and a flag indicating if the shift is positive.
"""
# Classify emotions in the original comment
original_emotions = emotion_classifier(original_comment)
# Since pipeline returns a list of lists, take the first (and only) inner list
original_emotions = original_emotions[0] if isinstance(original_emotions, list) and original_emotions else []
original_dominant_emotion = max(original_emotions, key=lambda x: x['score'])['label'] if original_emotions else "unknown"
# Classify emotions in the paraphrased comment
paraphrased_emotions = emotion_classifier(paraphrased_comment)
paraphrased_emotions = paraphrased_emotions[0] if isinstance(paraphrased_emotions, list) and paraphrased_emotions else []
paraphrased_dominant_emotion = max(paraphrased_emotions, key=lambda x: x['score'])['label'] if paraphrased_emotions else "unknown"
# Define negative and positive emotions
negative_emotions = ['anger', 'sadness', 'fear']
positive_emotions = ['joy', 'love']
# Check if the shift is positive (e.g., from a negative emotion to a neutral/positive one)
is_positive_shift = (
original_dominant_emotion in negative_emotions and
(paraphrased_dominant_emotion in positive_emotions or paraphrased_dominant_emotion not in negative_emotions)
)
return original_dominant_emotion, paraphrased_dominant_emotion, is_positive_shift
def compute_empathy_score(paraphrased_comment):
"""
Compute a proxy empathy score based on politeness keywords.
Returns a score between 0 and 1 (higher indicates more empathy).
"""
empathy_keywords = ['please', 'thank you', 'appreciate', 'understand', 'sorry', 'consider', 'kindly', 'help', 'support']
comment_lower = paraphrased_comment.lower()
keyword_count = sum(1 for keyword in empathy_keywords if keyword in comment_lower)
empathy_score = min(keyword_count / 3, 1.0)
return round(empathy_score, 2) |