In Transformers 3.0.2 and prior, there used to be a small caveat accompanying the description of 3.0.2 BertModel.forward() 2:

Returns
pooler_output (torch.FloatTensor: of shape (batch_size, hidden_size)):
Last layer hidden-state of the first token of the sequence (classification token) 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 pre-training.
This output is usually not a good summary of the semantic content of the input, you’re often better with averaging or pooling the sequence of hidden-states for the whole input sequence.

However, the caveat was removed in 3.1.0 (current master). The description of 3.1.0 BertModel.forward() now just says:

Returns
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) - Last layer hidden-state of the first token of the sequence (classification token) 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.

Is there any deeply meaningful reason why that line was removed? Is there a new important secret to that [CLS] token that you’re not telling us?