<!-- BEGIN COPY / PASTE -->
    from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
    from datasets import load_dataset

    # Load a pre-trained BERT model and tokenizer
    model_name = "bert-base-uncased"
    tokenizer = BertTokenizer.from_pretrained(model_name)
    model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)

    # Load and preprocess the dataset
    dataset = load_dataset("imdb")
    def tokenize_function(examples):
        return tokenizer(examples["text"], padding="max_length", truncation=True)

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

    # Define training arguments
    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=3,
        per_device_train_batch_size=8,
        evaluation_strategy="epoch"
    )

    # Initialize Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_datasets["train"],
        eval_dataset=tokenized_datasets["test"]
    )

    # Fine-tune the model
    trainer.train()
    <!-- END COPY / PASTE -->