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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 | """Fine-tune T5-small as a text humanizer (one language per run).
English uses ``google-t5/t5-small``; Chinese uses a Chinese T5-small
(``uer/t5-small-chinese-cluecorpussmall``). Supervised pairs come from
``scripts/build_dataset.py`` (rule-generated), filtered to one language.
MPS notes: avoid per-step host sync; accumulate the loss tensor and sync at
evaluation. Save a checkpoint at the end of every epoch.
"""
import argparse
import json
import time
from pathlib import Path
import torch
from torch.optim import AdamW
from torch.utils.data import DataLoader, Dataset
from transformers import BertTokenizer, T5ForConditionalGeneration, T5Tokenizer
BASE_MODELS = {
"en": "google-t5/t5-small",
"zh": "uer/t5-small-chinese-cluecorpussmall",
}
class PairsDataset(Dataset):
def __init__(self, path: Path, tokenizer: T5Tokenizer, max_len: int, lang: str):
self.pairs = []
with open(path, encoding="utf-8") as f:
for line in f:
row = json.loads(line)
if row["lang"] != lang:
continue
self.pairs.append((row["input_text"], row["output_text"]))
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.pairs)
def __getitem__(self, idx):
src, tgt = self.pairs[idx]
enc = self.tokenizer(
src, max_length=self.max_len, padding="max_length", truncation=True
)
dec = self.tokenizer(
tgt, max_length=self.max_len, padding="max_length", truncation=True
)
labels = torch.tensor(dec["input_ids"])
labels[labels == self.tokenizer.pad_token_id] = -100
return {
"input_ids": torch.tensor(enc["input_ids"]),
"attention_mask": torch.tensor(enc["attention_mask"]),
"labels": labels,
}
def decode(ids, tokenizer):
ids = torch.where(
(ids == -100) | (ids == tokenizer.pad_token_id),
torch.tensor(tokenizer.pad_token_id),
ids,
)
return tokenizer.decode(ids, skip_special_tokens=True)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--lang", choices=["en", "zh"], default="en")
parser.add_argument("--data", default="data")
parser.add_argument("--out", default=None)
parser.add_argument("--base-model", default=None)
parser.add_argument("--max-len", type=int, default=128)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--epochs", type=int, default=3)
parser.add_argument("--warmup-steps", type=int, default=100)
parser.add_argument("--eval-every", type=int, default=200)
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
base_model = args.base_model or BASE_MODELS[args.lang]
out_dir = Path(args.out or f"checkpoints/humanize-text-model/{args.lang}")
torch.manual_seed(args.seed)
device = "mps" if torch.backends.mps.is_available() else (
"cuda" if torch.cuda.is_available() else "cpu"
)
print(f"lang={args.lang} base={base_model} device={device}", flush=True)
tokenizer = (
BertTokenizer.from_pretrained(base_model)
if args.lang == "zh"
else T5Tokenizer.from_pretrained(base_model)
)
if (out_dir / "pytorch_model.bin").exists() or (out_dir / "model.safetensors").exists():
print(f"resuming from existing checkpoint in {out_dir}", flush=True)
model = T5ForConditionalGeneration.from_pretrained(out_dir)
else:
model = T5ForConditionalGeneration.from_pretrained(base_model)
model.to(device)
train_ds = PairsDataset(Path(args.data) / "train.jsonl", tokenizer, args.max_len, args.lang)
val_ds = PairsDataset(Path(args.data) / "val.jsonl", tokenizer, args.max_len, args.lang)
val_samples = [
json.loads(l) for l in open(Path(args.data) / "val.jsonl", encoding="utf-8")
if json.loads(l)["lang"] == args.lang
][:3]
train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, num_workers=0)
optimizer = AdamW(model.parameters(), lr=args.lr)
total_steps = args.epochs * len(train_loader)
scheduler = torch.optim.lr_scheduler.LinearLR(
optimizer, start_factor=1 / (args.warmup_steps + 1), total_iters=args.warmup_steps
)
out_dir.mkdir(parents=True, exist_ok=True)
step = 0
t0 = time.time()
for epoch in range(1, args.epochs + 1):
model.train()
epoch_loss = torch.zeros(())
for batch in train_loader:
batch = {k: v.to(device) for k, v in batch.items()}
loss = model(**batch).loss
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
epoch_loss = epoch_loss + loss.detach()
step += 1
if step % 100 == 0:
print(f"[hb] step {step}/{total_steps} ({time.time() - t0:.0f}s)", flush=True)
if step % args.eval_every == 0:
model.eval()
vloss = 0.0
with torch.no_grad():
for i in range(16):
vb = val_ds[i]
vb = {k: v.unsqueeze(0).to(device) for k, v in vb.items()}
vloss += model(**vb).loss.item()
avg_train = (epoch_loss / step).item()
model.train()
print(
f"[step {step}] epoch={epoch} train_loss={avg_train:.4f} "
f"val_loss={vloss / 16:.4f} ({time.time() - t0:.0f}s)",
flush=True,
)
sample = val_samples[0]
model.eval()
with torch.no_grad():
ids = model.generate(
input_ids=tokenizer(sample["input_text"], return_tensors="pt").input_ids.to(device),
max_length=args.max_len,
)
out = decode(ids[0], tokenizer)
model.train()
print(" IN :", sample["input_text"][:90])
print(" OUT:", out[:90], flush=True)
avg = (epoch_loss / len(train_loader)).item()
print(f"epoch {epoch} done: avg_loss={avg:.4f}", flush=True)
model.save_pretrained(out_dir)
tokenizer.save_pretrained(out_dir)
print(f"checkpoint saved to {out_dir}", flush=True)
print(f"final model saved to {out_dir}", flush=True)
if __name__ == "__main__":
main()
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