Contents
What is pooled output and sequence output in BERT?
Pooled output is the embedding of the [CLS] token (from Sequence output), further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
What is sequence output and pooled output?
So ‘sequence output’ will give output of dimension [1, 8, 768] since there are 8 tokens including [CLS] and [SEP] and ‘pooled output’ will give output of dimension [1, 1, 768] which is the embedding of [CLS] token.
What is the output from Bert?
The bert model gives us the two outputs, one gives us the [batch,maxlen,hiddenstates] and other one is [batch, hidden States of cls token].
What is Pooler output?
Pooler: It takes the output representation corresponding to the first token and uses it for downstream tasks.
What is mean pooling in BERT?
It’s “pooled” from all input tokens in the sense that the multiple attention layers will force it to depend on all other tokens. derpderp3200.
What are logits in Transformers?
The output you get is the non-normalized probability for each class (i.e. logits). You applied the softmax function to normalize these probabilities, which leads to 0.5022980570793152 for the first class and 0.49770188331604004 for the second class.
What kind of pooling is used in Bert?
It’s simply taking the representation from the [CLS] token from the top-most layer, and feeding that through another dense layer. It’s “pooling” in the sense that it’s extracting a representation for the whole sequence. The BERT author Jacob Devlin does not explain in the BERT paper which kind of pooling is applied.
How is the sequence output different from the pooled output?
So the sequence output is all the token representations, while the pooled_output is just a linear layer applied to the first token of the sequence. In classification case, you just need a global representation of your input, and predict the class from this representation.
What kind of representations can you use in Bert?
There are many choices of representations you can make from BERT. For classification and regression tasks, you usually use the representations of the CLS token. For question answering, you would have a classification head for each token representation in the second sentence.
What does it mean when CLS output is pooled?
That’s the embedding of the initial CLS token. It’s “pooled” from all input tokens in the sense that the multiple attention layers will force it to depend on all other tokens. Erm, what does this mean?