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5499d76 | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import evaluate
import numpy as np
from datasets import Dataset, DatasetDict
from sklearn.model_selection import train_test_split
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainingArguments,
)
from src.data_utils import filter_rare_classes, load_dataset_frame, save_json
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Train article topic classifier.")
parser.add_argument("--data-path", type=str, required=True, help="Path to CSV/JSONL/Parquet dataset.")
parser.add_argument("--text-cols", nargs="*", default=None, help="Optional explicit text columns: title abstract")
parser.add_argument("--label-col", type=str, default=None, help="Optional explicit label column.")
parser.add_argument("--output-dir", type=str, default="artifacts/article_topic_model")
parser.add_argument("--model-name", type=str, default="allenai/scibert_scivocab_uncased")
parser.add_argument("--max-length", type=int, default=256)
parser.add_argument("--epochs", type=int, default=3)
parser.add_argument("--lr", type=float, default=2e-5)
parser.add_argument("--weight-decay", type=float, default=0.01)
parser.add_argument("--train-batch-size", type=int, default=8)
parser.add_argument("--eval-batch-size", type=int, default=16)
parser.add_argument("--test-size", type=float, default=0.15)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--min-examples-per-class", type=int, default=20)
return parser.parse_args()
def main() -> None:
args = parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
df = load_dataset_frame(args.data_path, text_cols=args.text_cols, label_col=args.label_col)
df = filter_rare_classes(df, min_examples_per_class=args.min_examples_per_class)
labels = sorted(df["label"].unique().tolist())
label2id = {label: idx for idx, label in enumerate(labels)}
id2label = {idx: label for label, idx in label2id.items()}
df["label_id"] = df["label"].map(label2id)
train_df, valid_df = train_test_split(
df,
test_size=args.test_size,
random_state=args.seed,
stratify=df["label_id"],
)
ds = DatasetDict(
{
"train": Dataset.from_pandas(train_df[["text", "label_id"]], preserve_index=False),
"validation": Dataset.from_pandas(valid_df[["text", "label_id"]], preserve_index=False),
}
)
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
def tokenize_batch(batch: dict) -> dict:
return tokenizer(batch["text"], truncation=True, max_length=args.max_length)
tokenized = ds.map(tokenize_batch, batched=True)
tokenized = tokenized.rename_column("label_id", "labels")
tokenized.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name,
num_labels=len(labels),
id2label={int(k): v for k, v in id2label.items()},
label2id=label2id,
)
accuracy_metric = evaluate.load("accuracy")
f1_metric = evaluate.load("f1")
def compute_metrics(eval_pred):
logits, labels_np = eval_pred
preds = np.argmax(logits, axis=-1)
accuracy = accuracy_metric.compute(predictions=preds, references=labels_np)["accuracy"]
f1_macro = f1_metric.compute(predictions=preds, references=labels_np, average="macro")["f1"]
f1_weighted = f1_metric.compute(predictions=preds, references=labels_np, average="weighted")["f1"]
return {
"accuracy": accuracy,
"f1_macro": f1_macro,
"f1_weighted": f1_weighted,
}
training_args = TrainingArguments(
output_dir=str(output_dir / "checkpoints"),
learning_rate=args.lr,
per_device_train_batch_size=args.train_batch_size,
per_device_eval_batch_size=args.eval_batch_size,
num_train_epochs=args.epochs,
weight_decay=args.weight_decay,
eval_strategy="epoch",
save_strategy="epoch",
logging_strategy="steps",
logging_steps=50,
load_best_model_at_end=True,
metric_for_best_model="f1_macro",
greater_is_better=True,
save_total_limit=2,
report_to="none",
seed=args.seed,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized["train"],
eval_dataset=tokenized["validation"],
processing_class=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
compute_metrics=compute_metrics,
)
trainer.train()
metrics = trainer.evaluate()
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
save_json(
{
"label2id": label2id,
"id2label": {str(k): v for k, v in id2label.items()},
},
output_dir / "label_mapping.json",
)
save_json(metrics, output_dir / "metrics.json")
print(json.dumps(metrics, indent=2))
if __name__ == "__main__":
main()
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