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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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39
applications/Chat/evaluate/metrics.py
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@@ -21,13 +21,17 @@ def bleu_score(preds: List[str], targets: List[str], language: str) -> Dict[str,
"""
bleu_scores = {"bleu1": 0, "bleu2": 0, "bleu3": 0, "bleu4": 0}
cumulative_bleu = [0] * 4
weights = [(1. / 1., 0., 0., 0.), (1. / 2., 1. / 2., 0., 0.), (1. / 3., 1. / 3., 1. / 3., 0.),
(1. / 4., 1. / 4., 1. / 4., 1. / 4.)]
weights = [
(1.0 / 1.0, 0.0, 0.0, 0.0),
(1.0 / 2.0, 1.0 / 2.0, 0.0, 0.0),
(1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0, 0.0),
(1.0 / 4.0, 1.0 / 4.0, 1.0 / 4.0, 1.0 / 4.0),
]
for pred, target in zip(preds, targets):
if language == "cn":
pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split()
target_list = [(' '.join(jieba.cut(preprocessing_text(target)))).split()]
pred_list = " ".join(jieba.cut(preprocessing_text(pred))).split()
target_list = [(" ".join(jieba.cut(preprocessing_text(target)))).split()]
elif language == "en":
pred_list = preprocessing_text(pred).split()
target_list = [preprocessing_text(target).split()]
@@ -42,15 +46,14 @@ def bleu_score(preds: List[str], targets: List[str], language: str) -> Dict[str,
def chrf_score(preds: List[str], targets: List[str], language: str) -> Dict[str, float]:
"""Calculate CHRF Score Metric in sentence level.
"""
"""Calculate CHRF Score Metric in sentence level."""
chrf_score = {"chrf": 0}
cumulative_chrf = []
for pred, target in zip(preds, targets):
if language == "cn":
pred_list = ' '.join(jieba.cut(preprocessing_text(pred))).split()
target_list = ' '.join(jieba.cut(preprocessing_text(target))).split()
pred_list = " ".join(jieba.cut(preprocessing_text(pred))).split()
target_list = " ".join(jieba.cut(preprocessing_text(target))).split()
elif language == "en":
pred_list = preprocessing_text(pred).split()
target_list = preprocessing_text(target).split()
@@ -75,8 +78,8 @@ def rouge_cn_score(preds: List[str], targets: List[str]) -> Dict[str, float]:
all_targets = []
for pred, target in zip(preds, targets):
pred_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(pred))))
target_list = remove_redundant_space(' '.join(jieba.cut(preprocessing_text(target))))
pred_list = remove_redundant_space(" ".join(jieba.cut(preprocessing_text(pred))))
target_list = remove_redundant_space(" ".join(jieba.cut(preprocessing_text(target))))
all_preds.append(pred_list)
all_targets.append(target_list)
@@ -99,16 +102,14 @@ def rouge_en_score(preds: List[str], targets: List[str]) -> Dict[str, float]:
longest common subsequence (LCS) between preds and targets.
"""
rouge_scores = {"rouge1": 0, "rouge2": 0, "rougeL": 0}
all_preds = []
all_targets = []
rouge_en = Rouge_en.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=False)
for pred, target in zip(preds, targets):
score = rouge_en.score(preprocessing_text(pred), preprocessing_text(target))
rouge_scores["rouge1"] += score['rouge1'].fmeasure
rouge_scores["rouge2"] += score['rouge2'].fmeasure
rouge_scores["rougeL"] += score['rougeL'].fmeasure
rouge_scores["rouge1"] += score["rouge1"].fmeasure
rouge_scores["rouge2"] += score["rouge2"].fmeasure
rouge_scores["rougeL"] += score["rougeL"].fmeasure
rouge_scores["rouge1"] = rouge_scores["rouge1"] / len(preds)
rouge_scores["rouge2"] = rouge_scores["rouge2"] / len(preds)
@@ -137,7 +138,7 @@ def distinct_score(preds: List[str], language: str) -> Dict[str, float]:
for pred in preds:
if language == "cn":
pred_seg_list = ' '.join(jieba.cut(pred)).split()
pred_seg_list = " ".join(jieba.cut(pred)).split()
count_segs = len(pred_seg_list)
unique_segs = set(pred_seg_list)
count_unique_chars = len(unique_segs)
@@ -151,7 +152,7 @@ def distinct_score(preds: List[str], language: str) -> Dict[str, float]:
split_pred = preprocessing_text(pred).split()
for n in range(0, 3):
for i in range(0, len(split_pred) - n):
ngram = ' '.join(split_pred[i:i + n + 1])
ngram = " ".join(split_pred[i : i + n + 1])
unique_ngram[n].add(ngram)
all_ngram_count[n] += 1
@@ -203,8 +204,8 @@ def calculate_precision_recall_f1(preds: List[str], targets: List[str], language
for pred, target in zip(preds, targets):
if language == "cn":
pred_list = [char for char in ' '.join(jieba.cut(preprocessing_text(pred))).split()]
target_list = [char for char in ' '.join(jieba.cut(preprocessing_text(target))).split()]
pred_list = [char for char in " ".join(jieba.cut(preprocessing_text(pred))).split()]
target_list = [char for char in " ".join(jieba.cut(preprocessing_text(target))).split()]
elif language == "en":
pred_list = [char for char in preprocessing_text(pred).split()]
target_list = [char for char in preprocessing_text(target).split()]