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metrics.py
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# Code for metrics comes from beam_retriever/blob/main/gpt_turbo_exp.py
import re
import string
import collections
def normalize_answer(s):
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s:
return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_subem(a_gold, a_pred):
return int(normalize_answer(a_gold) in normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
# metrics for attribution
def compute_attr_metrics(response, ground_truth):
attr_matches = re.findall(r'\[(\d+)\]', response)
attr_predicted = [int(match) for match in attr_matches]
predicted_set = set(attr_predicted)
ground_truth_set = set(ground_truth)
true_positives = len(predicted_set & ground_truth_set)
precision = true_positives / len(predicted_set) if predicted_set else 0
recall = true_positives / len(ground_truth_set) if ground_truth_set else 0
f1 = (2 * precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
return precision, recall, f1