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from datasets import load_dataset, DatasetDict
from transformers import AutoTokenizer, T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
from transformers import EarlyStoppingCallback
from transformers.integrations import TensorBoardCallback
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def generate_formal_text(text):
# Implement formal text generation here
return text # Placeholder
def prepare_data(example):
example["formal_text"] = generate_formal_text(example["text"])
return example
def tokenize_function(examples, tokenizer):
model_inputs = tokenizer(examples["formal_text"], max_length=128, truncation=True, padding="max_length")
labels = tokenizer(examples["text"], max_length=128, truncation=True, padding="max_length")
model_inputs["labels"] = labels["input_ids"]
return model_inputs
def main():
# Load the dataset and take only 10000 samples
logger.info("Loading dataset...")
dataset = load_dataset("LucasChu/reddit_comments")
dataset = dataset.shuffle(seed=42)
dataset["train"] = dataset["train"].select(range(10000))
logger.info("Dataset loaded, shuffled, and truncated to 10,000 samples.")
# Split the train dataset into train and test
train_testvalid = dataset["train"].train_test_split(test_size=0.2, seed=42)
test_valid = train_testvalid["test"].train_test_split(test_size=0.5, seed=42)
dataset = DatasetDict({
"train": train_testvalid["train"],
"test": test_valid["test"],
"validation": test_valid["train"]
})
# Prepare the dataset
logger.info("Preparing dataset...")
processed_dataset = {split: data.map(prepare_data) for split, data in dataset.items()}
logger.info("Dataset prepared.")
# Tokenize the dataset
model_name = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
logger.info("Tokenizing dataset...")
tokenized_dataset = {split: data.map(lambda examples: tokenize_function(examples, tokenizer), batched=True) for split, data in processed_dataset.items()}
logger.info("Dataset tokenized.")
# Check available splits in the dataset
available_splits = list(tokenized_dataset.keys())
logger.info(f"Available splits in the dataset: {available_splits}")
# Set up the model and trainer
logger.info("Setting up model and trainer...")
model = T5ForConditionalGeneration.from_pretrained(model_name)
training_args = Seq2SeqTrainingArguments(
output_dir="./results",
num_train_epochs=1,
per_device_train_batch_size=16,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=100,
evaluation_strategy="steps" if "test" in available_splits else "no",
eval_steps=500,
save_steps=1000,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False
)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset.get("test"),
tokenizer=tokenizer,
callbacks=[EarlyStoppingCallback(early_stopping_patience=3), TensorBoardCallback()]
)
logger.info("Model and trainer set up.")
# Return the trainer object
return trainer
if __name__ == "__main__":
main() |