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8a3099e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """Train CodeBERT cross-encoder for SQL error classification with HF Trainer."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
EarlyStoppingCallback,
Trainer,
TrainingArguments,
)
from src.device_utils import get_device
from src.codebert_dataset import (
SQLCodeBERTDataCollator,
prepare_datasets,
)
from src.codebert_labels import load_codebert_labels
from src.hf_metrics import build_compute_metrics, compute_multilabel_metrics
PROJECT_ROOT = Path(__file__).resolve().parent.parent
DEFAULT_DATA = PROJECT_ROOT / "data" / "sql_errors_1m.parquet"
DEFAULT_OUTPUT = PROJECT_ROOT / "models" / "codebert-cross-encoder"
DEFAULT_MODEL = "microsoft/codebert-base"
def train(
data_path: Path | None = DEFAULT_DATA,
dataframe: pd.DataFrame | None = None,
output_dir: Path = DEFAULT_OUTPUT,
model_name: str = DEFAULT_MODEL,
epochs: float = 3.0,
batch_size: int = 16,
eval_batch_size: int = 32,
learning_rate: float = 2e-5,
weight_decay: float = 0.01,
warmup_ratio: float = 0.06,
max_length: int = 512,
max_samples: int | None = None,
test_size: float = 0.1,
val_size: float = 0.1,
threshold: float = 0.5,
seed: int = 42,
push_to_hub: bool = False,
hub_model_id: str | None = None,
fp16: bool = False,
save_strategy: str = "no",
hub_token: str | None = None,
) -> dict:
if dataframe is not None:
df = dataframe.copy()
print(f"Loaded dataframe with {len(df):,} rows")
elif data_path is not None:
print(f"Loading dataset from {data_path}...")
df = pd.read_parquet(data_path)
else:
raise ValueError("Either data_path or dataframe must be provided")
if max_samples and len(df) > max_samples:
df = df.sample(n=max_samples, random_state=seed)
label_list = load_codebert_labels()
num_labels = len(label_list)
print(f"Labels ({num_labels}): {label_list}")
print(f"Samples: {len(df):,}")
device = get_device()
use_fp16 = fp16 and device == "cuda"
print(f"Device: {device} | fp16: {use_fp16}")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
num_labels=num_labels,
problem_type="multi_label_classification",
id2label={i: name for i, name in enumerate(label_list)},
label2id={name: i for i, name in enumerate(label_list)},
)
train_ds, val_ds, test_ds = prepare_datasets(
df,
tokenizer,
test_size=test_size,
val_size=val_size,
max_length=max_length,
seed=seed,
)
print(f"Train: {len(train_ds):,} | Val: {len(val_ds):,} | Test: {len(test_ds):,}")
output_dir.mkdir(parents=True, exist_ok=True)
label_info = {
"labels": label_list,
"model_name": model_name,
"architecture": "codebert-cross-encoder",
"input_format": "QUESTION + SCHEMA + STUDENT_SQL + CORRECT_SQL",
"max_length": max_length,
"threshold": threshold,
}
with open(output_dir / "label_config.json", "w") as f:
json.dump(label_info, f, indent=2)
training_args = TrainingArguments(
output_dir=str(output_dir),
num_train_epochs=epochs,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=eval_batch_size,
learning_rate=learning_rate,
weight_decay=weight_decay,
warmup_ratio=warmup_ratio,
eval_strategy="epoch",
save_strategy=save_strategy,
logging_strategy="steps",
logging_steps=50,
load_best_model_at_end=save_strategy == "epoch",
metric_for_best_model="f1_macro",
greater_is_better=True,
save_total_limit=1,
seed=seed,
report_to="none",
fp16=use_fp16,
use_mps_device=(device == "mps"),
push_to_hub=push_to_hub,
hub_model_id=hub_model_id,
hub_token=hub_token,
)
callbacks = []
if save_strategy == "epoch":
callbacks.append(EarlyStoppingCallback(early_stopping_patience=2))
trainer_kwargs = dict(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=val_ds,
data_collator=SQLCodeBERTDataCollator(tokenizer),
compute_metrics=build_compute_metrics(threshold=threshold),
callbacks=callbacks,
)
try:
trainer = Trainer(processing_class=tokenizer, **trainer_kwargs)
except TypeError:
trainer = Trainer(tokenizer=tokenizer, **trainer_kwargs)
print("Starting CodeBERT cross-encoder training...")
train_result = trainer.train()
print("Evaluating on validation set...")
val_metrics = trainer.evaluate()
print("Evaluating on held-out test set...")
test_output = trainer.predict(test_ds)
test_metrics = compute_multilabel_metrics(
test_output.predictions,
test_output.label_ids,
threshold=threshold,
)
trainer.save_model(str(output_dir))
tokenizer.save_pretrained(str(output_dir))
metrics = {
"train_samples": len(train_ds),
"val_samples": len(val_ds),
"test_samples": len(test_ds),
"train_runtime": train_result.metrics.get("train_runtime"),
"validation": val_metrics,
"test": test_metrics,
}
with open(output_dir / "metrics.json", "w") as f:
json.dump(metrics, f, indent=2, default=float)
print(f"\nValidation F1 (macro): {val_metrics.get('eval_f1_macro', 0):.4f}")
print(f"Test F1 (macro): {test_metrics['f1_macro']:.4f}")
print(f"Test subset accuracy: {test_metrics['subset_accuracy']:.4f}")
print(f"Model saved to {output_dir}")
return metrics
def main() -> None:
parser = argparse.ArgumentParser(
description="Train CodeBERT cross-encoder with Hugging Face Trainer"
)
parser.add_argument("--data", type=Path, default=DEFAULT_DATA)
parser.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT)
parser.add_argument("--model-name", type=str, default=DEFAULT_MODEL)
parser.add_argument("--epochs", type=float, default=3.0)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--eval-batch-size", type=int, default=32)
parser.add_argument("--learning-rate", type=float, default=2e-5)
parser.add_argument("--max-length", type=int, default=512)
parser.add_argument("--max-samples", type=int, default=None)
parser.add_argument("--threshold", type=float, default=0.5)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--push-to-hub", action="store_true")
parser.add_argument("--hub-model-id", type=str, default=None)
parser.add_argument("--fp16", action="store_true")
parser.add_argument(
"--save-strategy",
choices=["no", "epoch"],
default="no",
help="Use 'no' to save only final model (saves disk space)",
)
args = parser.parse_args()
train(
data_path=args.data,
output_dir=args.output_dir,
model_name=args.model_name,
epochs=args.epochs,
batch_size=args.batch_size,
eval_batch_size=args.eval_batch_size,
learning_rate=args.learning_rate,
max_length=args.max_length,
max_samples=args.max_samples,
threshold=args.threshold,
seed=args.seed,
push_to_hub=args.push_to_hub,
hub_model_id=args.hub_model_id,
fp16=args.fp16,
save_strategy=args.save_strategy,
)
if __name__ == "__main__":
main()
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