Architectures de modèles
Encoder-only Transformers
Let every input token attend to tokens on both sides, producing contextual representations rather than an open-ended generation loop.
Modèle mental
Read the entire input, contextualize each position, then pool or attach a task head.
Flux de données
- Token sequence
- Bidirectional self-attention blocks
- Contextual token states
- Pooler or task head
- Class, span, score, or vector
Entraînement
Masked-token reconstruction is the classic pretraining objective, followed by task-specific classification, token labeling, span prediction, or metric learning.
Exécution de l’inférence
One forward pass processes the available input. It is naturally suited to understanding and representation, but does not by itself generate an arbitrary continuation.
Atouts
- Rich bidirectional context for classification and extraction
- Parallel processing across the input
- Often efficient for high-volume bounded tasks
Compromis
- Not an open-ended generator
- Maximum input length and quadratic attention can constrain long documents
- A task head or pooling strategy is usually required
À utiliser lorsque
- Classification, named entities, reranking, or embeddings dominate
- The complete input is available before prediction
- A smaller specialized model can meet the rubric
À éviter ou remettre en question lorsque
- The primary output is long free-form text
- Streaming generation is required
- Inputs routinely exceed the evaluated context strategy
Familles publiées à titre d’exemple
- • BERT and RoBERTa research families
- • Encoder-based rerankers and classifiers
Souvent combinée avec
Embedding modelsVision encodersContrastive / dual encodersRetrieval systems