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5ead6b5 54fac60 5ead6b5 54fac60 5ead6b5 54fac60 26fd0f9 0a5683a 5ead6b5 26fd0f9 5ead6b5 54fac60 5ead6b5 0a5683a 54fac60 0a5683a 54fac60 5ead6b5 0a5683a 5ead6b5 0a5683a 54fac60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | import numpy as np
from sentence_transformers import SentenceTransformer, util
EPS = 0.001
def clamp_score(score):
if score <= 0.0:
return EPS
if score >= 1.0:
return 1.0 - EPS
return score
_model = None
def _get_model():
global _model
if _model is None:
_model = SentenceTransformer('all-MiniLM-L6-v2')
return _model
def grade_comment(comment: str, expected_keywords: list, expert_comment: str) -> float:
if not comment:
return clamp_score(0.0)
model = _get_model()
emb_comment = model.encode(comment, convert_to_tensor=True)
emb_expert = model.encode(expert_comment, convert_to_tensor=True)
sim = util.pytorch_cos_sim(emb_comment, emb_expert).item()
comment_lower = comment.lower()
matched = sum(1 for kw in expected_keywords if kw in comment_lower)
kw_score = min(1.0, matched / max(1, len(expected_keywords) // 2))
combined = 0.7 * sim + 0.3 * kw_score
words = comment.split()
if len(words) >= 15:
length_bonus = 0.1
elif len(words) < 5:
length_bonus = -0.2
else:
length_bonus = 0.0
final = combined + length_bonus
# Clamp to (0,1) using EPS
return clamp_score(final)
def grade_question(question: str) -> float:
words = question.split()
if len(words) < 3:
return clamp_score(0.0)
if any(q in question.lower() for q in ["what", "how", "why", "where", "when", "does", "is"]):
score = min(1.0, len(words) / 20)
return clamp_score(score)
return clamp_score(0.2)
def grade_fix(proposed_fix: str, expected_fix_keywords: list, hidden_test: callable) -> float:
matched = sum(1 for kw in expected_fix_keywords if kw in proposed_fix.lower())
kw_score = min(1.0, matched / max(1, len(expected_fix_keywords) // 2))
test_score = 0.0
if hidden_test is not None:
try:
test_score = hidden_test(proposed_fix)
except Exception:
test_score = 0.0
score = 0.6 * test_score + 0.4 * kw_score
return clamp_score(score) |