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485127c | 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 | #!/usr/bin/env python3
"""Fine-tune a small transformer for binary human vs AI on JSONL (id, label, text)."""
import argparse
import json
import os
import sys
import numpy as np
import torch
from torch.utils.data import Dataset
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
Trainer,
TrainingArguments,
)
class JsonlDataset(Dataset):
def __init__(self, path: str, tokenizer, max_length: int):
self.rows = []
with open(path, encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
r = json.loads(line)
self.rows.append(r)
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.rows)
def __getitem__(self, i):
r = self.rows[i]
text = r.get("text", "")
enc = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
padding="max_length",
return_tensors="pt",
)
item = {k: v.squeeze(0) for k, v in enc.items()}
item["labels"] = torch.tensor(int(r["label"]), dtype=torch.long)
return item
def load_jsonl_rows(path: str):
rows = []
with open(path, encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
rows.append(json.loads(line))
return rows
def main():
p = argparse.ArgumentParser(
description="Train a small transformer on JSONL, or run inference only with --predict_jsonl.",
)
p.add_argument("--train_jsonl", help="Training JSONL (required unless predict-only)")
p.add_argument("--val_jsonl", help="Optional validation for Trainer eval")
p.add_argument("--model_name", default="roberta-base")
p.add_argument("--max_length", type=int, default=256)
p.add_argument("--output_dir", required=True, help="Save dir after training, or load dir in predict-only")
p.add_argument("--epochs", type=float, default=2.0)
p.add_argument("--batch_size", type=int, default=8)
p.add_argument("--lr", type=float, default=2e-5)
p.add_argument("--seed", type=int, default=42)
p.add_argument(
"--predict_jsonl",
help="Inference-only: input JSONL (no --train_jsonl needed). Requires --predict_output.",
)
p.add_argument("--predict_output", help="Inference-only: output JSONL with sup_score")
p.add_argument("--device", default=None, help="cuda:0 or cpu (default: auto)")
args = p.parse_args()
predict_only = bool(args.predict_jsonl)
if predict_only:
if not args.predict_output:
raise SystemExit("Predict-only mode requires --predict_output.")
elif not args.train_jsonl:
raise SystemExit("Training requires --train_jsonl, or use predict-only: --predict_jsonl and --predict_output.")
torch.manual_seed(args.seed)
np.random.seed(args.seed)
device = args.device or ("cuda" if torch.cuda.is_available() else "cpu")
if predict_only:
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
model = AutoModelForSequenceClassification.from_pretrained(args.output_dir)
model.to(device)
model.eval()
rows = load_jsonl_rows(args.predict_jsonl)
out_path = args.predict_output
out_d = os.path.dirname(os.path.abspath(out_path))
if out_d:
os.makedirs(out_d, exist_ok=True)
with open(out_path, "w", encoding="utf-8") as fout:
for r in rows:
text = r.get("text", "")
enc = tokenizer(
text,
truncation=True,
max_length=args.max_length,
padding="max_length",
return_tensors="pt",
).to(device)
with torch.no_grad():
logits = model(**enc).logits
prob = torch.softmax(logits, dim=-1)[0, 1].item()
o = dict(r)
o["sup_score"] = float(prob)
fout.write(json.dumps(o, ensure_ascii=False) + "\n")
print(f"Wrote {out_path}")
return
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
model = AutoModelForSequenceClassification.from_pretrained(args.model_name, num_labels=2)
train_ds = JsonlDataset(args.train_jsonl, tokenizer, args.max_length)
eval_ds = JsonlDataset(args.val_jsonl, tokenizer, args.max_length) if args.val_jsonl else None
os.makedirs(args.output_dir, exist_ok=True)
targs = TrainingArguments(
output_dir=args.output_dir,
learning_rate=args.lr,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
num_train_epochs=args.epochs,
weight_decay=0.01,
logging_steps=50,
save_strategy="epoch",
evaluation_strategy="no",
seed=args.seed,
)
trainer = Trainer(
model=model,
args=targs,
train_dataset=train_ds,
eval_dataset=eval_ds,
tokenizer=tokenizer,
)
trainer.train()
trainer.save_model(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
print(f"Saved model to {args.output_dir}")
if __name__ == "__main__":
main()
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