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from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments
model_checkpoint = "google/flan-t5-large"
output_dir = "./finetuned-flan-t5"
dataset = load_dataset("json", data_files={"train": "train_data.jsonl"})
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
def preprocess_function(examples):
inputs = examples["input"]
targets = examples["output"]
model_inputs = tokenizer(inputs, max_length=512, truncation=True)
labels = tokenizer(targets, max_length=128, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
tokenized_datasets = dataset.map(preprocess_function, batched=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
training_args = Seq2SeqTrainingArguments(
output_dir=output_dir,
evaluation_strategy="no",
learning_rate=5e-5,
per_device_train_batch_size=2,
num_train_epochs=3,
weight_decay=0.01,
save_total_limit=2,
push_to_hub=False
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"]
)
trainer.train()
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
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