Transformer Neural Network Architecture

The Transformer is a neural network architecture that uses self-attention to model relationships among all elements of an input sequence in parallel, forming the basis of modern language models.

트랜스포머는 자기주의(self-attention)를 이용해 입력 시퀀스의 모든 요소 간 관계를 병렬로 모형화하는 신경망 구조로, 현대 언어모형의 기반이 된다.

How does a Transformer model work, and when should I use it for my data?

트랜스포머 모형은 어떻게 작동하며, 내 자료에는 언제 사용해야 하나요?

A Transformer processes inputs with multi-head self-attention layers that weigh how much each element should attend to every other element, followed by feed-forward layers and positional information. It fits sequence data such as text or long time series and, with adapted designs, tabular prediction, typically with large samples or pretrained models.

When to use Transformer Neural Network Architecture

• Text classification or regression using a pretrained Transformer
• Sequences where long-range dependencies matter
• Large datasets that can support a high-capacity model

When to use something else

• Small tabular datasets: use gradient boosting such as XGBoost
• Short univariate series with limited history: use ARIMA or exponential smoothing

Assumptions and requirements

• Large labeled datasets or a suitable pretrained model for fine-tuning
• Tokenized or embedded inputs with positional information
• Validation data for early stopping and hyperparameter tuning
• Computational resources for training

What the analysis reports

• Training and validation loss curves
• Test-set performance (accuracy, F1, AUC or RMSE)
• Attention weights or attribution scores
• Hyperparameter settings

How to report it

The fine-tuned Transformer achieved a macro F1 score of [value] on the held-out test set, compared with [value] for the baseline model.

Related methods

• BERT
• Recurrent Neural Network (LSTM)
• XGBoost

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