train_model.py

from datasets import load_dataset
from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments

# Step 1: Load dataset
dataset = load_dataset("DetectiveShadow/MVPQuestion")["train"]

# Optional: Rename columns if needed
# dataset = dataset.rename_columns({"your_input_column": "input", "your_output_column": "output"})

# Step 2: Load tokenizer and model
model_name = "t5-small"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Step 3: Tokenization function
def tokenize(example):
    input_enc = tokenizer(example["input"], truncation=True, padding="max_length", max_length=64)
    target_enc = tokenizer(example["output"], truncation=True, padding="max_length", max_length=64)
    input_enc["labels"] = target_enc["input_ids"]
    return input_enc

tokenized = dataset.map(tokenize)

# Step 4: Training configuration
training_args = TrainingArguments(
    output_dir="./MVPTrivia",
    per_device_train_batch_size=8,
    num_train_epochs=3,
    logging_steps=10,
    save_strategy="epoch",
    push_to_hub=True,
    hub_model_id="DetectiveShadow/MVPTrivia",  # This is where your model will go
    hub_strategy="every_save"
)

# Step 5: Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized
)

# Step 6: Train and push
trainer.train()
trainer.push_to_hub()
tokenizer.push_to_hub("DetectiveShadow/MVPTrivia")