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corrector.py
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import pandas as pd
from transformers import pipeline
from tqdm.auto import tqdm
from typing import List
import fire
from torch.utils.data import Dataset
# Use Bert's fill-mask model to fix typos
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipe = pipeline("fill-mask", model="hon9kon9ize/bert-base-cantonese", device=device)
class ListDataset(Dataset):
def __init__(self, original_list):
self.original_list = original_list
def __len__(self):
return len(self.original_list)
def __getitem__(self, i):
return self.original_list[i]
class Typo:
def __init__(self, typo, correct):
self.typo = typo
self.correct = correct
def fix(self, lines: pd.Series) -> pd.Series:
raise NotImplementedError
class RegularTypo(Typo):
def __init__(self, typo, correct):
self.typo = typo
self.correct = correct
def fix(self, lines: pd.Series) -> pd.Series:
return lines.str.replace(self.typo, self.correct)
class MaskTypo(Typo):
def __init__(
self,
typo,
correct,
length_limit=460,
batch_size=64,
):
self.typo = typo
self.correct = correct
self.length_limit = length_limit
self.delimiters = ["。", "!", "?", ";", ",", "\n", "⋯", "…", "?", "!"]
self.batch_size = batch_size
# return the score of the typo and the correct word
def _get_typo_score(self, outputs) -> List[float]:
if isinstance(outputs[0], list):
my_scores = []
# multiple target words
for j in range(len(outputs)):
scores = self._get_typo_score(outputs[j])
my_scores += scores
return my_scores
src_outputs = list(filter(lambda x: x["token_str"] == self.typo, outputs))
tgt_outputs = list(filter(lambda x: x["token_str"] == self.correct, outputs))
if len(src_outputs) != 1 or len(tgt_outputs) != 1:
return [1]
src_output = src_outputs[0] if src_outputs else None
tgt_output = tgt_outputs[0] if tgt_outputs else None
src_score = src_output["score"]
tgt_score = tgt_output["score"]
return [tgt_score - src_score]
# fix the typo in the Panda Series
def fix(self, series: pd.Series) -> pd.Series:
lines = series.copy()
return pd.Series(self._fix(lines.to_list()), index=lines.index)
def _fix(self, lines: List[str]):
fixed_lines = lines.copy()
items = []
for line_index, line in enumerate(lines):
occurrence = [
i for i, c in enumerate(line) if c.lower() == self.typo.lower()
]
# iterate the line from middle of the index to left and right every time
for idx in occurrence:
left = new_left = idx
right = new_right = idx
while (
(right - left) < self.length_limit
and left - 1 > 0
and right + 1 < len(line)
):
if line[left - 1] not in self.delimiters and left - 1 > 0:
new_left = left - 1
if line[right + 1] not in self.delimiters and right < len(line):
new_right = right + 1
if new_left == left and new_right == right:
break
left = new_left
right = new_right
sentence = line[left:right]
if len(sentence) + 1 > self.length_limit:
# print(sentence, len(sentence))
# raise ValueError("Sentence too long")
continue
if len(sentence) > 0:
# replace the index of the typo char to [MASK]
sentence = (
sentence[: idx - left] + "[MASK]" + sentence[idx - left + 1 :]
)
# replace the other occurence of the typo char to [UNK], so that the model can predict the correct word without bias
sentence = sentence.replace(self.typo, "[UNK]").replace(
self.typo, "[UNK]"
)
sentence = (
";" + sentence
) # add a delimiter to the beginning of the sentence to prevent prediction bias of the first word
items.append((line_index, idx, (left, right), sentence))
if len(items) == 0:
return fixed_lines
# get the score of the typo and the correct word
ds = ListDataset([sentence[3] for sentence in items])
outputs = []
for out in tqdm(
pipe(ds, targets=[self.typo, self.correct], batch_size=self.batch_size),
total=len(ds),
desc=f"{self.typo} -> {self.correct}",
leave=False,
):
outputs.append(out)
scores = self._get_typo_score(outputs)
for item, score in zip(items, scores):
if score > 0:
item_index, typo_idx, (left, right), _ = item
corrected_sentence = fixed_lines[item_index]
corrected_sentence = (
corrected_sentence[:typo_idx]
+ self.correct
+ corrected_sentence[typo_idx + 1 :]
)
fixed_lines[item_index] = corrected_sentence
return fixed_lines
regular_typo_items = []
mask_typo_items = []
def register_regular_typo(typo, correct):
regular_typo_items.append(RegularTypo(typo, correct))
def register_mask_typo(typo, correct):
mask_typo_items.append(MaskTypo(typo, correct))
register_regular_typo("傾計", "傾偈")
register_regular_typo("訓覺", "瞓覺")
register_regular_typo("戇鳩", "戇𨳊")
register_regular_typo("on9", "戇鳩")
register_regular_typo("on居", "戇居")
register_regular_typo("潤9[我佢你]", r"潤鳩\1")
register_regular_typo("鐘意", "鍾意")
register_regular_typo("揾", "搵")
register_regular_typo("哩個", "呢個")
register_regular_typo("丫([喇嗱嘛麻?!~?!,])", "吖\1")
register_regular_typo("([啦吖㗎])麻", "\1嘛")
register_regular_typo("([咪哇哎])丫", "\1吖")
register_regular_typo("D", "啲")
register_regular_typo(
r"([多咗呢嗰邊佢我你畀借還嚟出入緊鬆實較大細深淺晏常差依哋返面講係夜乜先過己晒用下耐頭靜間])d([^\\w\\d\\s])",
r"\1啲\2",
)
register_regular_typo(r"([^\w\d\s])D([咩嘢])", r"\1啲\2")
register_regular_typo("[果嗰][d啲]", "嗰啲")
register_regular_typo(
r"([你佢我俾畀嗰有某衰好細勁易大高一返番真少])D([^\\w])", r"\1啲\2"
)
register_regular_typo("([^a-z])o敢", "\1噉")
register_regular_typo("([^a-z])o左", "\1咗")
register_regular_typo("([^a-z])o甘", "\1咁")
register_regular_typo("([^a-z])o既", "\1嘅")
register_regular_typo("([^a-z])o地", "\1哋")
register_regular_typo("([^a-z])o啱", "\1啱")
register_regular_typo("([^a-z])o刺", "\1喇")
register_regular_typo("([^a-z])o個", "\1嗰")
register_regular_typo("([^a-z])o拉", "\1啦")
register_regular_typo("([^a-z])o拿", "\1嗱")
register_regular_typo("([^a-z])o野", "\1嘢")
register_regular_typo("([^a-z])o架", "\1㗎")
register_regular_typo("([^a-z])o黎", "\1嚟")
register_regular_typo("([^a-z])o吾", "\1唔")
register_regular_typo("拿拿([淋林聲臨])", "嗱嗱$1")
register_regular_typo("嗱嗱[淋林]", "嗱嗱臨")
register_regular_typo("大嗱嗱", "大拿拿")
register_regular_typo("嚟拿[??]", "嚟嗱?")
register_regular_typo("裡", "裏")
register_regular_typo("o丫", "吖")
register_regular_typo("㞗", "𨳊")
register_regular_typo("嗮", "晒")
register_regular_typo("尐", "啲")
register_regular_typo("揾", "搵")
register_regular_typo("噖晚", "琴晚")
register_regular_typo("噚晚", "尋晚")
register_regular_typo("噖日", "琴日")
register_regular_typo("岩岩", "啱啱")
register_regular_typo("噚日", "尋日")
register_regular_typo("[撲扑]街", "仆街")
register_regular_typo("[痴癡黐][綫線]", "黐線")
register_regular_typo("[痴癡黐][撚𠹌能][綫線]", "黐撚線")
register_mask_typo("番", "返")
register_mask_typo("翻", "返")
register_mask_typo("黎", "嚟")
register_mask_typo("左", "咗")
register_mask_typo("佐", "咗")
register_mask_typo("遮", "即")
register_mask_typo("哩", "呢")
register_mask_typo("姐", "即")
register_mask_typo("姐", "啫")
register_mask_typo("姐", "啫")
register_mask_typo("吓", "下")
register_mask_typo("岩", "啱")
register_mask_typo("果", "嗰")
register_mask_typo("攪", "搞")
register_mask_typo("既", "嘅")
register_mask_typo("比", "俾")
register_mask_typo("奶", "舐")
register_mask_typo("丫", "啊")
register_mask_typo("丫", "吖")
register_mask_typo("著", "着")
register_mask_typo("訓", "瞓")
register_mask_typo("嫁", "㗎")
register_mask_typo("曬", "晒")
register_mask_typo("甘", "噉")
register_mask_typo("噤", "噉")
register_mask_typo("哩", "匿")
register_mask_typo("到", "度")
register_mask_typo("地", "哋")
register_mask_typo("野", "嘢")
register_mask_typo("駛", "使")
register_mask_typo("洗", "使")
register_mask_typo("俾", "畀")
register_mask_typo("畀", "俾")
register_mask_typo("咁", "噉")
register_mask_typo("架", "㗎")
register_mask_typo("拿", "嗱")
# register_mask_typo('d', '啲', [r'[^\w]d[^\w]']) # Bert model has bias towards Chinese characters
# register_mask_typo('D', '啲') # D is OOV
def fix_typo(df: pd.DataFrame, column_name: str):
for regular_typo in tqdm(regular_typo_items, desc="Regular typo correction"):
df[column_name] = regular_typo.fix(df[column_name])
for mask_typo in tqdm(mask_typo_items, desc="Bert typo correction"):
df[column_name] = mask_typo.fix(df[column_name])
# post process
df[column_name] = df[column_name].str.replace(r"噉(cheap)", r"咁\1", regex=True)
return df
def main(
file_path: str,
output_path: str,
column_name="text",
):
ext = file_path.split(".")[-1]
# read
if "csv" in ext or ext == "tsv" or ext == "txt":
df = pd.read_csv(file_path)
elif ext == "json":
df = pd.read_json(file_path)
else:
raise ValueError("file format not supported")
if column_name not in df.columns:
raise ValueError("text column not found in json file")
df = df.dropna(subset=[column_name])
fix_typo(df, column_name)
df.to_csv(output_path, index=False)
if __name__ == "__main__":
# test_df = pd.DataFrame(
# [
# {
# "text": "岩岩「仲有隻雞……有……」預先一日準備好既雞,D香料既味已經入晒隻雞到。一番賞"
# }
# ]
# )
# fixed_df = fix_typo(test_df, "text")
# print(fixed_df["text"].values[0])
fire.Fire(main)