File size: 8,903 Bytes
7cb8aac 7f7899a 7cb8aac 7f7899a 7cb8aac 54e3906 7cb8aac 54e3906 7cb8aac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | """humanize-text-model inference wrapper (bilingual: English + Chinese).
Loads the Lynote bilingual humanizer — two small seq2seq checkpoints in one
repo (``en/`` T5-small, ``zh/`` Chinese T5-small) — and routes each input to
the right language model. URLs, numbers, paths, code and quoted strings are
protected with tokenizer-friendly placeholders before generation and restored
afterwards.
Usage:
from humanize import Humanizer
h = Humanizer() # loads "Lynote/humanize-text-model" from the Hub
print(h("It is important to note that this robust solution serves as a testament to our commitment."))
"""
import argparse
import os
import re
import sys
from typing import List, Optional
import torch
from transformers import BertTokenizer, T5ForConditionalGeneration, T5Tokenizer
DEFAULT_MODEL = "Lynote/humanize-text-model"
MAX_CHARS = 4_000
MAX_TOKENS = 256
DEVICE = "mps" if torch.backends.mps.is_available() else (
"cuda" if torch.cuda.is_available() else "cpu"
)
PROTECTED_PATTERN = re.compile(
r"(`[^`]+`|https?://[^\s\u4e00-\u9fff，。！？；：、]+|(?:[A-Za-z]:)?(?:[/\\][\w.\-]+)+|"
r"\b\d+(?:\.\d+)?(?:%|[A-Za-z]+)?\b|\"[^\"]*\"|'[^']*')"
)
CJK_RATIO = re.compile(r"[\u4e00-\u9fff]")
def protect(text: str, language: Optional[str] = None):
"""Replace protected spans with language-aware placeholders:
EN ``PROTECTED_n``, ZH ``【保护n】`` (both tokenizer-friendly)."""
protected = []
zh = language == "zh" or (language is None and is_chinese(text))
def replace(match):
token = f"【保护{len(protected)}】" if zh else f"PROTECTED_{len(protected)}"
protected.append(match.group(0))
return token
return PROTECTED_PATTERN.sub(replace, text), protected
def restore(text: str, protected: List[str]) -> str:
"""Restore placeholders (either format, tolerant of inserted spaces)."""
for index, value in enumerate(protected):
text = re.sub(rf"PROTECTED_{index}\b", lambda m: value, text)
text = re.sub(
rf"【\s*保\s*护\s*{index}\s*】", lambda m: value, text
)
return text
def is_chinese(text: str, threshold: float = 0.05) -> bool:
total = len(re.sub(r"\s+", "", text))
if total == 0:
return False
return len(CJK_RATIO.findall(text)) / total > threshold
class Humanizer:
def __init__(
self,
model_id: str = DEFAULT_MODEL,
device: Optional[str] = None,
cache_dir: Optional[str] = None,
local_files_only: bool = False,
hf_token: Optional[str] = None,
lazy: bool = True,
):
"""model_id is a repo (or local dir) containing ``en/`` and ``zh/``
subdirectories with separate checkpoints. With ``lazy=True`` (default)
a language model is only loaded when first needed."""
self.model_id = model_id
self.device = torch.device(device or DEVICE)
self._models = {}
self._tokenizers = {}
self._cache_dir = cache_dir
self._local_only = local_files_only
self._token = hf_token
if not lazy:
self._get("en")
self._get("zh")
def _load(self, language: str, model: T5ForConditionalGeneration, tokenizer: T5Tokenizer):
self._models[language] = model
self._tokenizers[language] = tokenizer
def _get(self, language: str):
if language not in self._models:
model = T5ForConditionalGeneration.from_pretrained(
self.model_id, subfolder=language,
cache_dir=self._cache_dir,
local_files_only=self._local_only, token=self._token,
)
tokenizer_cls = BertTokenizer if language == "zh" else T5Tokenizer
tokenizer = tokenizer_cls.from_pretrained(
self.model_id, subfolder=language,
cache_dir=self._cache_dir,
local_files_only=self._local_only, token=self._token,
)
model.to(self.device).eval()
self._load(language, model, tokenizer)
return self._models[language], self._tokenizers[language]
def humanize(self, text: str, num_beams: int = 3, do_sample: bool = False) -> str:
text = (text or "").strip()
if not text:
raise ValueError("Input text is empty.")
if len(text) > MAX_CHARS:
raise ValueError(f"Input exceeds {MAX_CHARS:,} characters.")
return self.humanize_batch([text], num_beams=num_beams, do_sample=do_sample)[0]
def humanize_batch(
self,
texts: List[str],
num_beams: int = 3,
do_sample: bool = False,
batch_size: int = 8,
) -> List[str]:
"""Humanize a list of texts. URLs, numbers, paths, code and quotes are
protected per-text with placeholders and restored after generation."""
masked = []
protected_all = []
for t in texts:
masked_text, protected = protect(t)
masked.append(masked_text)
protected_all.append(protected)
outputs: List[str] = []
grouped: dict = {}
for i, t in enumerate(masked):
grouped.setdefault("zh" if is_chinese(t) else "en", []).append(i)
for language, indices in grouped.items():
model, tokenizer = self._get(language)
for start in range(0, len(indices), batch_size):
chunk_idx = indices[start : start + batch_size]
enc = tokenizer(
[masked[i] for i in chunk_idx],
max_length=MAX_TOKENS,
truncation=True,
padding=True,
return_tensors="pt",
)
if "token_type_ids" in enc:
enc.pop("token_type_ids")
enc = enc.to(self.device)
with torch.no_grad():
gen_kwargs = dict(
num_beams=num_beams,
do_sample=do_sample,
early_stopping=True,
)
if language == "zh":
# Small Chinese T5 degenerates on long sequences: cap
# length and block exact 4-gram repetition (EN does not
# need these).
gen_kwargs["max_length"] = 40
gen_kwargs["no_repeat_ngram_size"] = 4
else:
gen_kwargs["max_length"] = MAX_TOKENS
gen = model.generate(**enc, **gen_kwargs)
decoded = tokenizer.batch_decode(gen, skip_special_tokens=True)
for j, text in zip(chunk_idx, decoded):
outputs.append((j, text))
outputs.sort(key=lambda x: x[0])
return [
restore(self._polish(out), protected)
for out, protected in zip([o[1] for o in outputs], protected_all)
]
@staticmethod
def _polish(text: str) -> str:
text = re.sub(r"^\s*(?:extra\d+\s*[,，。\s]*)+", "", text)
text = re.sub(r"(?<=[\u4e00-\u9fff])\s+(?=[\u4e00-\u9fff，。；：！？])", "", text)
text = re.sub(r"(?<=[，。；：！？])\s+(?=[\u4e00-\u9fff])", "", text)
text = re.sub(r"[ \t]{2,}", " ", text)
text = re.sub(r"\s+([,.;:!?，。；：！？])", r"\1", text)
text = re.sub(r"[,;]\s*\.", ".", text)
text = re.sub(r"\.\s*[,;]", ".", text)
text = re.sub(r"[，,]\s*(?=[。！？.!?])", "", text)
text = re.sub(r"\.\s+([a-z])", lambda m: ". " + m.group(1).upper(), text)
return text.strip(" ，")
def main():
parser = argparse.ArgumentParser(description="Bilingual text humanizer (T5-small EN + Chinese T5-small)")
parser.add_argument("--text", help="text to humanize")
parser.add_argument("--input", help="path to a text file")
parser.add_argument("--output", help="write result to a file")
parser.add_argument("--model", default=DEFAULT_MODEL, help="model id or local dir")
parser.add_argument("--beams", type=int, default=3)
parser.add_argument("--list-models", action="store_true", help="print model ids and exit")
args = parser.parse_args()
if args.list_models:
print(DEFAULT_MODEL)
return
if args.text and args.input:
parser.error("provide only one of --text / --input")
source = args.text or (open(args.input, encoding="utf-8").read() if args.input else None)
if not source:
parser.error("provide --text or --input")
humanizer = Humanizer(model_id=args.model)
result = humanizer.humanize(source, num_beams=args.beams)
if args.output:
with open(args.output, "w", encoding="utf-8") as f:
f.write(result)
print(f"written to {args.output}")
else:
print(result)
if __name__ == "__main__":
main()
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