Create train_flan_t5.py

train_flan_t5.py

@@ -0,0 +1,47 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments
+
+model_checkpoint = "google/flan-t5-large"
+output_dir = "./finetuned-flan-t5"
+
+# Load dataset
+dataset = load_dataset("json", data_files={"train": "train_data.jsonl"})
+
+# Tokenizer
+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
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+# Training arguments
+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)