Encoder-Only Transformer-based Model

Context:
- It can (typically) be responsible for encoding input sequences into continuous representations.
- It can (typically) process input tokens through self-attention layers to capture contextual relationships.
- It can (typically) learn bidirectional context through masked language modeling.
- It can (typically) generate contextual embeddings for downstream tasks.
- It can (often) perform transfer learning via fine-tuning.
- It can (often) handle multi-task learning through task-specific heads.
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- It can range from being a Base Model to being a Large Model, depending on its parameter count.
- It can range from being a Task-Specific Model to being a General-Purpose Model, depending on its training objectives.
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Example(s):
- an Encoder-Only Transformer-Based Language Model, such as:
  - BERT Familys, such as:
    - BERT Model for general language understanding.
    - RoBERTa Model for optimized training.
    - ALBERT Model for parameter-efficient learning.
  - XLM Familys, such as:
    - XLM Model for multilingual understanding.
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Counter-Example(s):
- a Decoder-Only Transformer Model, which focuses on sequence generation.
- an Encoder-Decoder Transformer Model, which uses both encoder and decoder components.
- a Recurrent Neural Network, which uses sequential processing instead of parallel attention.
See: Encoder Architecture, Self-Attention, Bidirectional Model, Encoder/Decoder Transformer Model.

References

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- An Encoder-Only Transformer Model consists solely of an encoder architecture. This model is responsible for encoding input sequences into continuous representations, which can be used for different NLP tasks, including text classification, sentiment analysis, and named entity recognition A well-known example of an Encoder-Only Transformer Model is the BERT (Bidirectional Encoder Representations from Transformers) model, developed by Google AI.