Initial commit with training script

humanizer.py

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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()

requirements.txt

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+datasets
+transformers
+torch

run_trainer.py

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+from humanizer import main
+
+if __name__ == "__main__":
+    trainer = main()
+    trainer.train()