Remove outdated BERT tips (#6217)

* Remove out-dated BERT tips

* Update modeling_outputs.py

* Update bert.rst

* Update bert.rst

src/transformers/modeling_outputs.py

@@ -45,10 +45,6 @@ class BaseModelOutputWithPooling(ModelOutput):
             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.
-
-            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.
         hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
             Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
             of shape :obj:`(batch_size, sequence_length, hidden_size)`.