Transformer (NLP)
Also known as: Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.
Key highlights
- Self-attention captures long-range dependencies across a whole sequence, unlike word-by-word recurrent models.
- Processes sequences in parallel, making training on large text corpora far more scalable.
- Pre-trained models (BERT, GPT) can be fine-tuned, transferring knowledge to tasks with limited labelled data.
- Handles diverse language tasks — classification, NER, and language modelling — within one architecture.
- No distributional assumptions: it does not require normally distributed data.
Intuition
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How it works
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When to use it
A good fit for text classification, named-entity recognition, and language modelling on large text datasets — at least about 500 documents — where deep contextual understanding of language matters. It does not require normally distributed data, but it does assume a large text corpus, and using a pre-trained model (such as BERT or GPT) is recommended. Below roughly 500 examples the attention mechanism cannot learn reliable patterns, and below about 100 a deep model is pointless; classical machine learning such as Random Forest or XGBoost is the safer choice.
Strengths & limitations
- Self-attention captures long-range dependencies across a whole sequence, unlike word-by-word recurrent models.
- Processes sequences in parallel, making training on large text corpora far more scalable.
- Pre-trained models (BERT, GPT) can be fine-tuned, transferring knowledge to tasks with limited labelled data.
- Handles diverse language tasks — classification, NER, and language modelling — within one architecture.
- No distributional assumptions: it does not require normally distributed data.
- Needs large text datasets (about 500 examples or more) to learn reliable patterns.
- Below roughly 100 examples, deep model training is meaningless and classical ML suffices.
- Training is computationally expensive and typically relies on pre-trained models.
- Its internal representations are hard to interpret compared with explicit-coefficient models.
Common pitfalls
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Applications
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Frequently asked
How much text data do I need?
At least about 500 examples for the attention mechanism to learn reliable patterns. Below roughly 100, a deep model is not worth training and classical machine learning such as Random Forest or XGBoost is a better choice.
Should I train a Transformer from scratch?
Usually no. Using a pre-trained model such as BERT or GPT and fine-tuning it on your task is recommended, since it transfers knowledge learned from large corpora and needs far less labelled data.
What tasks does it handle?
Text classification, named-entity recognition, and language modelling, all built on the same self-attention architecture.
Why is the attention mechanism important?
It lets every token in a sequence attend directly to every other token, capturing long-range context that earlier word-by-word recurrent models handled poorly.
Sources
- 1.Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
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Cite this page
ScholarGate. (2026, June 1). Transformer. ScholarGate. https://scholargate.app/deep-learning/transformer-nlp