What role does tokenization play in self-supervised learning for text?

Tokenization is a foundational step in self-supervised learning for text, serving as the bridge between raw text data and the numerical representations models use. It breaks text into smaller units—such as words, subwords, or characters—enabling models to process and learn from sequences efficiently. For example, a sentence like “The quick brown fox” might be split into tokens like ["The", "quick", "brown", “fox”]. The choice of tokenization directly impacts a model’s ability to generalize, handle rare words, and manage computational resources. Without effective tokenization, models would struggle to parse linguistic patterns or scale to large datasets.

Tokenization methods like Byte-Pair Encoding (BPE) or WordPiece address challenges like out-of-vocabulary (OOV) words by splitting rare or unseen terms into smaller, reusable subword units. For instance, the word “unhappiness” could be divided into ["un", “happiness”], allowing a model to recognize the prefix "un-" and root “happiness” separately. This subword approach reduces vocabulary size while maintaining the flexibility to handle diverse text. In self-supervised tasks like masked language modeling (used in BERT), tokenization determines what the model predicts: masking a subword token like [“hug”] in “hugging” forces the model to learn contextual relationships between subwords. Poor tokenization—such as inconsistent splits—could make these tasks ambiguous or overly complex, harming learning.

Finally, tokenization impacts training efficiency. Self-supervised models like GPT or RoBERTa process vast datasets, so tokenization must balance sequence length and computational cost. Subword methods keep sequences concise compared to character-level tokenization, which would create excessively long inputs. For example, the word “tokenization” split into ["token", “ization”] requires fewer tokens than individual characters, reducing memory usage. Additionally, tokenizers often include special tokens (e.g., [CLS], [SEP]) to mark sentence boundaries, which helps models learn structure during pretraining. By defining how text is segmented, tokenization shapes the entire learning pipeline, from input representation to the model’s ability to capture meaningful linguistic features.