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#!/usr/bin/python3
from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments
from datasets import load_dataset
# Load model and tokenizer
model_name = "t5-small" # or another transformer-based model
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
# Load dataset
dataset = load_dataset("json", data_files={"train": "train.json"})
evalset = load_dataset("json", data_files={"eval": "eval.json"})
def preprocess_function(examples):
inputs = ["Generate a question for: " + (ans if isinstance(ans, str) else "Unknown") for ans in examples["answer"]]
model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding="max_length") # <-- Added padding
labels = [q if isinstance(q, str) else "" for q in examples["question"]]
labels = tokenizer(labels, max_length=128, truncation=True, padding="max_length") # <-- Added padding
model_inputs["labels"] = labels["input_ids"]
return model_inputs
tokenized_datasets = dataset.map(preprocess_function, batched=True)
tokenized_evalsets = evalset.map(preprocess_function, batched=True)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
save_strategy="epoch",
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
logging_dir="./logs",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_evalsets["eval"]
)
# Train model
trainer.train()
# Save trained model
output_dir = "./aq_model" # Change the folder name if needed
trainer.save_model(output_dir)
tokenizer.save_pretrained(output_dir)
print(f"Model saved to {output_dir}")
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