import torch
print(torch.cuda.is_available())

from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Load the IMDb dataset
dataset = load_dataset('imdb')

# Initialize the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)

# Tokenize the dataset
def tokenize_function(examples):
    return tokenizer(examples['text'], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Set up training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=1,  # Start with fewer epochs for quicker runs
    weight_decay=0.01,
)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"].shuffle(seed=42).select(range(1000)),  # Use a subset for quicker runs
    eval_dataset=tokenized_datasets["test"].shuffle(seed=42).select(range(1000)),
)

# Train the model
trainer.train()

# Evaluate the model
results = trainer.evaluate()
print(results)