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Remove a TF usage warning and rework the documentation (#9756)

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* Rework documentation

* Update the template

* Trigger CI

* Restore the warning but with the TF logger

* Update convbert doc
This commit is contained in:
Julien Plu
2021-01-27 10:45:42 +01:00
committed by GitHub
parent 285c6262a8
commit a1720694a5
29 changed files with 299 additions and 131 deletions
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33
src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
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@@ -576,12 +576,15 @@ BLENDERBOT_SMALL_INPUTS_DOCSTRING = r"""
decoding (see :obj:`past_key_values`). Set to :obj:`False` during training, :obj:`True` during generation
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
@@ -666,12 +669,18 @@ class TFBlenderbotSmallEncoder(tf.keras.layers.Layer):
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
returned tensors for more detail.
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
for more detail.
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
inputs = input_processing(
func=self.call,
@@ -859,12 +868,18 @@ class TFBlenderbotSmallDecoder(tf.keras.layers.Layer):
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under
returned tensors for more detail.
returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value
in the config will be used instead.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors
for more detail.
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. This
argument can be used in eager mode, in graph mode the value will always be set to True.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
inputs = input_processing(
func=self.call,