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# train_docgen.py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
from datasets import load_dataset
import argparse
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--data", type=str, default="data_examples/sample_dataset.jsonl", help="jsonl with {'code','doc'}")
p.add_argument("--output_dir", type=str, default="./code-explain-viz-model")
p.add_argument("--epochs", type=int, default=1)
return p.parse_args()
def preprocess_batch(examples, tokenizer, max_src=512, max_tgt=256):
inputs = ["explain: " + c for c in examples["code"]]
model_inputs = tokenizer(inputs, truncation=True, padding="max_length", max_length=max_src)
labels = tokenizer(text_target=examples["doc"], truncation=True, padding="max_length", max_length=max_tgt)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
def main():
args = parse_args()
model_name = "Salesforce/codet5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
ds = load_dataset("json", data_files={"train": args.data})
tokenized = ds["train"].map(lambda x: preprocess_batch(x, tokenizer), batched=True, remove_columns=ds["train"].column_names)
training_args = Seq2SeqTrainingArguments(
output_dir=args.output_dir,
num_train_epochs=args.epochs,
per_device_train_batch_size=2,
save_strategy="epoch",
logging_steps=50
)
trainer = Seq2SeqTrainer(model=model, args=training_args, train_dataset=tokenized)
trainer.train()
trainer.save_model(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
print("Saved model to", args.output_dir)
if __name__ == "__main__":
main()
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