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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
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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: Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): 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)`): 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). 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``): 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: Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): 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)`): 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). 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``): 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: Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): 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)`): 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 Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax). SoftMax).
@ -438,7 +438,7 @@ class ProphetNetDecoderLMOutput(ModelOutput):
Args: Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): 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)`): 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 Prediction scores of the main stream language modeling head (scores for each vocabulary token before
SoftMax). 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) >>> 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 >>> 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) >>> outputs = model(input_ids=input_ids, labels=labels)
>>> loss = outputs.loss >>> loss = outputs.loss
>>> logits = outputs.logits >>> logits = outputs.logits

4
src/transformers/modeling_tf_outputs.py
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@ -227,7 +227,7 @@ class TFMaskedLMOutput(ModelOutput):
Args: Args:
loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): 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)`): 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). 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``): 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: Args:
loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided): 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)`): 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). 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``): 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') >>> model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
>>> inputs = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='tf').input_ids >>> 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) >>> outputs = model(inputs, labels=labels)
>>> loss = outputs.loss >>> loss = outputs.loss
>>> logits = outputs.logits >>> logits = outputs.logits