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import collections
import re
import string
import datasets
import evaluate
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r"\b(un|une|des|le|la|les)\b", re.UNICODE)
return re.sub(regex, " ", 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()
# Exact match (the normalized answer exactly match the gold answer)
def exact(predictions, references):
return int(normalize_answer(references[0]) == normalize_answer(predictions[0]))
# The F-score of predicted tokens versus the gold answer
def f1(predictions, references):
gold_toks = get_tokens(references[0])
pred_toks = get_tokens(predictions[0])
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
def rouge1(items):
"""
# passthrough for efficiency
"""
return items
def rouge1_agg(items):
"""
Higher is better
"""
refs = list(zip(*items))[0]
preds = list(zip(*items))[1]
rouge_scorer = evaluate.load("rouge")
return rouge_scorer.compute(predictions=preds, references=refs)["rouge1"]
def is_included(items):
"""
# passthrough for efficiency
"""
if items[0] in items[1]:
return True
return False
def preprocess(text):
text = text.strip()
# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
text = text.replace(" [title]", ". ")
text = re.sub("\\[.*?\\]", "", text)
text = text.replace(" ", " ")
return text
def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
def _process_doc(doc):
ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
out_doc = {
"query": preprocess(doc["activity_label"] + ": " + ctx),
"choices": [preprocess(ending) for ending in doc["endings"]],
"gold": int(doc["label"]),
}
return out_doc
return dataset.map(_process_doc)
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