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Add a check regarding the number of occurrences of ``` (#18389)

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2022-08-01 14:23:02 +02:00
committed by GitHub
parent 1cd7c6f154
commit bd6d1b4300
26 changed files with 77 additions and 75 deletions
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templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py
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@@ -2136,7 +2136,7 @@ class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids`
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)`
instead of all ``decoder_input_ids``` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
@@ -2483,7 +2483,7 @@ class {{cookiecutter.camelcase_modelname}}Decoder({{cookiecutter.camelcase_model
If `past_key_values` are used, the user can optionally input only the last
`decoder_input_ids` (those that don't have their past key value states given to this model) of
shape `(batch_size, 1)` instead of all ``decoder_input_ids``` of shape `(batch_size,
shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size,
sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices
into associated vectors than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):