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import os
import re
import unicodedata
from typing import Dict
import kenlm
import sentencepiece
from huggingface_hub import cached_download, hf_hub_url
class SentencePiece:
def __init__(
self,
model: str,
):
super().__init__()
self.sp = sentencepiece.SentencePieceProcessor()
self.sp.load(str(model))
def do(self, text: dict) -> dict:
tokenized = self.sp.encode_as_pieces(text)
return " ".join(tokenized)
class KenlmModel:
digit_re: re.Pattern = re.compile(r"\d")
unicode_punct: Dict[str, str] = {
"，": ",",
"。": ".",
"、": ",",
"„": '"',
"”": '"',
"“": '"',
"«": '"',
"»": '"',
"１": '"',
"」": '"',
"「": '"',
"《": '"',
"》": '"',
"´": "'",
"∶": ":",
"：": ":",
"？": "?",
"！": "!",
"（": "(",
"）": ")",
"；": ";",
"–": "-",
"—": " - ",
"．": ". ",
"～": "~",
"’": "'",
"…": "...",
"━": "-",
"〈": "<",
"〉": ">",
"【": "[",
"】": "]",
"％": "%",
"►": "-",
}
unicode_punct_re = re.compile(f"[{''.join(unicode_punct.keys())}]")
non_printing_chars_re = re.compile(
f"[{''.join(map(chr, list(range(0,32)) + list(range(127,160))))}]"
)
kenlm_model_dir = None
sentence_piece_model_dir = None
def __init__(
self,
model_dataset: str,
language: str,
lower_case: bool = False,
remove_accents: bool = False,
normalize_numbers: bool = True,
punctuation: int = 1,
):
self.model = kenlm.Model(os.path.join(model_dataset, f"{language}.arpa.bin"))
self.tokenizer = SentencePiece(os.path.join(model_dataset, f"{language}.sp.model"))
self.accent = remove_accents
self.case = lower_case
self.numbers = normalize_numbers
self.punct = punctuation
@classmethod
def from_pretrained(
cls,
model_dataset: str,
language: str,
):
return cls(
model_dataset,
language,
False,
False,
True,
1,
)
def pp(self, log_score, length):
return 10.0 ** (-log_score / length)
def get_perplexity(self, doc: str, normalize_cc_net: bool = True):
if normalize_cc_net:
doc = self.normalize(
doc,
accent=self.accent,
case=self.case,
numbers=self.numbers,
punct=self.punct,
)
# Tokenize (after normalizing): See https://github.com/facebookresearch/cc_net/blob/bda555bd1cf1ee2e0b925363e62a61cd46c8b60d/cc_net/mine.py#L352 for full pipeline
doc = self.tokenizer.do(doc)
doc_log_score, doc_length = 0, 0
for line in doc.split("\n"):
log_score = self.model.score(line)
length = len(line.split()) + 1
doc_log_score += log_score
doc_length += length
return round(self.pp(doc_log_score, doc_length), 1)
def normalize(
self,
line: str,
accent: bool = True,
case: bool = True,
numbers: bool = True,
punct: int = 1,
) -> str:
line = line.strip()
if not line:
return line
if case:
line = line.lower()
if accent:
line = self.strip_accents(line)
if numbers:
line = self.digit_re.sub("0", line)
if punct == 1:
line = self.replace_unicode_punct(line)
elif punct == 2:
line = self.remove_unicode_punct(line)
line = self.remove_non_printing_char(line)
return line
def strip_accents(self, line: str) -> str:
"""Strips accents from a piece of text."""
nfd = unicodedata.normalize("NFD", line)
output = [c for c in nfd if unicodedata.category(c) != "Mn"]
if len(output) == line:
return line
return "".join(output)
def replace_unicode_punct(self, text: str) -> str:
return "".join(self.unicode_punct.get(c, c) for c in text)
def remove_unicode_punct(self, text: str) -> str:
"""More aggressive version of replace_unicode_punct but also faster."""
return self.unicode_punct_re.sub("", text)
def remove_non_printing_char(self, text: str) -> str:
return self.non_printing_chars_re.sub("", text)
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