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Fix typo: s/languaged/language/ (#8165)

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Santiago Castro 2020-10-30 11:22:03 -04:00 committed by GitHub
parent 10f8c63620
commit 6279072f5f
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5 changed files with 8 additions and 8 deletions
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4
src/transformers/modeling_outputs.py
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@ -260,7 +260,7 @@ class MaskedLMOutput(ModelOutput):
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Masked languaged modeling (MLM) loss.
Masked language modeling (MLM) loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
@ -289,7 +289,7 @@ class Seq2SeqLMOutput(ModelOutput):
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Languaged modeling loss.
Language modeling loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (:obj:`List[torch.FloatTensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):

4
src/transformers/modeling_prophetnet.py
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@ -225,7 +225,7 @@ class ProphetNetSeq2SeqLMOutput(ModelOutput):
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Languaged modeling loss.
Language modeling loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, decoder_sequence_length, config.vocab_size)`):
Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).
@ -438,7 +438,7 @@ class ProphetNetDecoderLMOutput(ModelOutput):
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Languaged modeling loss.
Language modeling loss.
logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, decoder_sequence_length, config.vocab_size)`):
Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax).

2
src/transformers/modeling_t5.py
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@ -1124,7 +1124,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
>>> model = T5ForConditionalGeneration.from_pretrained('t5-small', return_dict=True)
>>> input_ids = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='pt').input_ids
labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt').input_ids
>>> labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='pt').input_ids
>>> outputs = model(input_ids=input_ids, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits

4
src/transformers/modeling_tf_outputs.py
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@ -227,7 +227,7 @@ class TFMaskedLMOutput(ModelOutput):
Args:
loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Masked languaged modeling (MLM) loss.
Masked language modeling (MLM) loss.
logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
@ -256,7 +256,7 @@ class TFSeq2SeqLMOutput(ModelOutput):
Args:
loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Languaged modeling loss.
Language modeling loss.
logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (:obj:`List[tf.Tensor]`, `optional`, returned when ``use_cache=True`` is passed or when ``config.use_cache=True``):

2
src/transformers/modeling_tf_t5.py
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@ -1213,7 +1213,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
>>> model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
>>> inputs = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='tf').input_ids
labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='tf').input_ids
>>> labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='tf').input_ids
>>> outputs = model(inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits