How can I fine-tune a pre-trained transformer model for sentiment analysis?

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How can I fine-tune a pre-trained transformer model for sentiment analysis?

Asked on Oct 01, 2025

Answer

Fine-tuning a pre-trained transformer model for sentiment analysis involves adapting the model to a specific sentiment classification task using a labeled dataset. This process typically requires a smaller dataset and less computational power compared to training a model from scratch.

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

    # Load pre-trained 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 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",
        evaluation_strategy="epoch",
        per_device_train_batch_size=8,
        per_device_eval_batch_size=8,
        num_train_epochs=3,
        weight_decay=0.01,
    )

    # 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 -->

Additional Comment:

Start by selecting a pre-trained transformer model, such as BERT, which is well-suited for text classification tasks.
Use a tokenizer to preprocess your text data, ensuring it matches the input format expected by the model.
Load a sentiment analysis dataset, such as IMDb, and split it into training and evaluation sets.
Set up training arguments, specifying parameters like batch size, number of epochs, and weight decay for regularization.
Use the Trainer class from the Transformers library to handle the training loop and evaluation.
Fine-tuning adjusts the model's weights to better predict sentiment labels based on your specific dataset.

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How can I fine-tune a pre-trained transformer model for sentiment analysis?

Asked on Oct 01, 2025

Answer

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