I just started learning about transformers and looked into the following 3 variants

1. The original one from Attention Is All You Need (Encoder & Decoder)

2. BERT (Encoder only)

3. GPT-2 (Decoder only)

How does one generally decide whether their transformer model should include encoders only, decoders only, or both encoders and decoders?

As an example, if I want to train a transformer to read a sequence of images of my backend then predict whether it will rain in an hour ("rain" or "not rain"), should this transformer model generally have only decoders?

I just started learning about transformers and looked into the following 3 variants

1. The original one from Attention Is All You Need (Encoder & Decoder)

2. BERT (Encoder only)

3. GPT-2 (Decoder only)

How does one generally decide whether their transformer model should include encoders only, decoders only, or both encoders and decoders?

As an example, if I want to train a transformer to read a sequence of images of my backyard then predict whether it will rain in an hour (2 classes "rain" or "not rain"), should this transformer model generally have only decoders?

I just started learning about transformers and looked into the following 3 variants

1. The original one from Attention Is All You Need (Encoder & Decoder)

2. BERT (Encoder only)

3. GPT-2 (Decoder only)

How does one generally decide whether their transformer model should include encoders only, decoders only, or both encoders and decoders?

As an example, if I want to train a transformer to read a sequence of images of my backend then predict whether it will rain in an hour ("rain" or "not rain"), should this transformer model generally have only decoders?

I just started learning about transformers and looked into the following 3 variants

1. The original one from Attention Is All You Need (Encoder & Decoder)

2. BERT (Encoder only)

3. GPT-2 (Decoder only)

How does one generally decide whether their transformer model should include encoders only, decoders only, or both encoders and decoders?

As an example, if I want to train a transformer to read a sequence of images of my backend then predict whether it will rain in an hour ("rain" or "not rain"), should this transformer model generally have only decoders?