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[LED] Correct Docs (#10419)

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* correct docs

* correct tf model docs as well
This commit is contained in:
Patrick von Platen
2021-02-26 17:53:28 +03:00
committed by GitHub
parent 7fc686efb1
commit d03695f3a2
2 changed files with 23 additions and 5 deletions
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13
src/transformers/models/led/modeling_led.py
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@@ -1516,8 +1516,17 @@ LED_INPUTS_DOCSTRING = r"""
`What are attention masks? <../glossary.html#attention-mask>`__ `What are attention masks? <../glossary.html#attention-mask>`__
decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for translation and summarization training. By default, the model will create this tensor by Indices of decoder input sequence tokens in the vocabulary.
shifting the :obj:`input_ids` to the right, following the paper.
Indices can be obtained using :class:`~transformers.LedTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
LED uses the :obj:`eos_token_id` as the starting token for :obj:`decoder_input_ids` generation. If
:obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see
:obj:`past_key_values`).
decoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): decoder_attention_mask (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default. also be used by default.

15
src/transformers/models/led/modeling_tf_led.py
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@@ -1533,9 +1533,18 @@ LED_INPUTS_DOCSTRING = r"""
- 0 for tokens that are **masked**. - 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__ `What are attention masks? <../glossary.html#attention-mask>`__
decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): decoder_input_ids (:obj:`tf.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for translation and summarization training. By default, the model will create this tensor by Indices of decoder input sequence tokens in the vocabulary.
shifting the input_ids right, following the paper.
Indices can be obtained using :class:`~transformers.LedTokenizer`. See
:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
LED uses the :obj:`eos_token_id` as the starting token for :obj:`decoder_input_ids` generation. If
:obj:`past_key_values` is used, optionally only the last :obj:`decoder_input_ids` have to be input (see
:obj:`past_key_values`).
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`): decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases. will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
head_mask (:obj:`tf.Tensor` of shape :obj:`(encoder_layers, encoder_attention_heads)`, `optional`): head_mask (:obj:`tf.Tensor` of shape :obj:`(encoder_layers, encoder_attention_heads)`, `optional`):