Transformerとは？
Transformerは、自己注意メカニズムを採用した深層学習モデルであり、入力データの異なる部分の重要性を評価して処理を行い、自然言語処理などのタスクに優れています。
「Attention is All You Need」という論文で紹介されたTransformerは、AI分野、特に自然言語処理（NLP）に革命をもたらしました。データを順番に処理する再帰型ニューラルネットワーク（RNN）とは異なり、Transformerは自己注意を用いてすべての入力データを同時に分析し、並列化と高速なトレーニングを可能にします。このアーキテクチャにより、モデルは入力の異なる部分間の関係を理解できるようになり、機械翻訳、テキスト要約、質問応答などのタスクでパフォーマンスが向上します。自己注意メカニズムにより、モデルは位置に関係なく、入力シーケンスの最も関連性の高い部分に焦点を当てることができ、長距離依存関係を効果的に捉えることができます。
graph LR
Center["Transformerとは？"]:::main
Pre_neural_network["neural-network"]:::pre --> Center
click Pre_neural_network "/terms/neural-network"
Pre_linear_algebra["linear-algebra"]:::pre --> Center
click Pre_linear_algebra "/terms/linear-algebra"
Pre_deep_learning["deep-learning"]:::pre --> Center
click Pre_deep_learning "/terms/deep-learning"
Rel_transformer_architecture["transformer-architecture"]:::related -.-> Center
click Rel_transformer_architecture "/terms/transformer-architecture"
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🧠 理解度チェック
🧒 5歳でもわかるように説明
It's like a super-smart reader that can look at all the words in a sentence at once and figure out which words are most important to understand the meaning of each individual word.
🤓 Expert Deep Dive
The Transformer model's success stems from its ability to model dependencies without regard to their distance in the input or output sequences. The self-attention mechanism computes a weighted sum of value vectors, where the weight assigned to each value is determined by the compatibility (dot product) of its corresponding key vector with a query vector. This allows for direct modeling of relationships between any two positions in the sequence. Multi-head attention further enhances this by allowing the model to jointly attend to information from different representation subspaces at different positions. The encoder uses stacked self-attention and point-wise feed-forward layers, while the decoder adds masked self-attention (to prevent attending to future tokens) and encoder-decoder attention. The absence of recurrence makes it highly parallelizable, leading to faster training times on modern hardware compared to RNNs. However, the quadratic complexity of self-attention with respect to sequence length ($O(n^2)$) remains a bottleneck for very long sequences, prompting research into more efficient variants.