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Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
Tu... | 24 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 24 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of sha... | 24 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
encoder_z_loss: torch.FloatTensor = None
decoder_z_loss: torch.FloatTensor = None
encoder_aux_loss: torch.FloatTensor = None
decoder_aux_loss: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTenso... | 24 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class NextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided):
Next sequence prediction (classificatio... | 25 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 25 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class SequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torc... | 26 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 26 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class Seq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) ... | 27 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 27 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of ... | 27 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 27 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of sha... | 27 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
cross_attentions: Optio... | 27 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class MultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choic... | 28 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 28 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class TokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequenc... | 29 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 29 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class QuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end posi... | 30 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 30 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class Seq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models. | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
Span-start scor... | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of ... | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of sha... | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
decoder_attentions: Optional[Tuple[torch.FloatT... | 31 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class SemanticSegmenterOutput(ModelOutput):
"""
Base class for outputs of semantic segmentation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.F... | 32 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of ... | 32 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None | 32 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class ImageClassifierOutput(ModelOutput):
"""
Base class for outputs of image classification models. | 33 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labe... | 33 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`. | 33 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.... | 33 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class ImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logi... | 34 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None | 34 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class DepthEstimatorOutput(ModelOutput):
"""
Base class for outputs of depth estimation models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
predicted_depth (`torch.... | 35 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 35 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class ImageSuperResolutionOutput(ModelOutput):
"""
Base class for outputs of image super resolution models. | 36 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Reconstruction loss.
reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Reconstructed images, possibly upscaled.
hidden_states (`tuple(... | 36 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`. | 36 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
reconstruction: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tupl... | 36 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class Wav2Vec2BaseModelOutput(ModelOutput):
"""
Base class for models that have been trained with the Wav2Vec2 loss objective.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer o... | 37 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 37 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class XVectorOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ForXVector`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
... | 38 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 38 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class BackboneOutput(ModelOutput):
"""
Base class for outputs of backbones.
Args:
feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`):
Feature maps of the stages.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `out... | 39 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each stage plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 39 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class BaseModelOutputWithPoolingAndProjection(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states. | 40 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the fi... | 40 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. | 40 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 40 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
projection_state: Optional[Tuple[torch.FloatTensor]] = None | 40 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class Seq2SeqSpectrogramOutput(ModelOutput):
"""
Base class for sequence-to-sequence spectrogram outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Spectrogram generation loss.
spectrogram (`torch.FloatTensor` of shape `(ba... | 41 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 41 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of ... | 41 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 41 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of sha... | 41 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
spectrogram: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
cross_attentions: ... | 41 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class Seq2SeqTSModelOutput(ModelOutput):
"""
Base class for time series model's encoder outputs that also contains pre-computed hidden states that can speed up
sequential decoding.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
S... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each laye... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each laye... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
Shift values of each time series' context window which is used t... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
last_hidden_state: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
cross_attentions: Optional[Tuple[torch.FloatTensor, ...]]... | 42 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class Seq2SeqTSPredictionOutput(ModelOutput):
"""
Base class for time series model's decoder outputs that also contain the loss as well as the parameters of the
chosen distribution.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when a `future_values` is provided):
... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of ... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of sha... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
loc (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
Shift values of each time series' context window which is used t... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
params: Optional[Tuple[torch.FloatTensor]] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
decoder_attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
cross_... | 43 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class SampleTSPredictionOutput(ModelOutput):
"""
Base class for time series model's predictions outputs that contains the sampled values from the chosen
distribution.
Args:
sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length)` or `(batch_size, num_samples, predi... | 44 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class MaskedImageModelingOutput(ModelOutput):
"""
Base class for outputs of masked image completion / in-painting models. | 45 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Reconstruction loss.
reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Reconstructed / completed images.
hidden_states (`tupl... | 45 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
""" | 45 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
loss: Optional[torch.FloatTensor] = None
reconstruction: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
@property
def logits(self):
warnings.warn(
"logits attribute is deprecated a... | 45 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_outputs.py |
class PretrainedConfig(PushToHubMixin):
# no-format
r"""
Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
methods for loading/downloading/saving configurations.
<Tip>
A configuration file can be loaded and saved to disk. Loading... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
- **model_type** (`str`) -- An identifier for the model type, serialized into the JSON file, and used to recreate
the correct object in [`~transformers.AutoConfig`].
- **is_composition** (`bool`) -- Whether the config class is composed of multiple sub-configs. In this case the
config has to be initializ... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
- **vocab_size** (`int`) -- The number of tokens in the vocabulary, which is also the first dimension of the
embeddings matrix (this attribute may be missing for models that don't have a text modality like ViT).
- **hidden_size** (`int`) -- The hidden size of the model.
- **num_attention_heads** (`int`) -... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
Arg:
name_or_path (`str`, *optional*, defaults to `""`):
Store the string that was passed to [`PreTrainedModel.from_pretrained`] or
[`TFPreTrainedModel.from_pretrained`] as `pretrained_model_name_or_path` if the configuration was created
with such a method.
output_hid... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
Whether the model is used as decoder or not (in which case it's used as an encoder).
cross_attention_hidden_size** (`bool`, *optional*):
The hidden size of the cross-attention layer in case the model is used as a decoder in an encoder-decoder
setting and the cross-attention hidden dimens... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
prune_heads (`Dict[int, List[int]]`, *optional*, defaults to `{}`):
Pruned heads of the model. The keys are the selected layer indices and the associated values, the list of
heads to prune in said layer. | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
For instance `{1: [0, 2], 2: [2, 3]}` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer 2.
chunk_size_feed_forward (`int`, *optional*, defaults to `0`):
The chunk size of all feed forward layers in the residual attention blocks. A chunk size of `0` means that
the feed forwar... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
architectures (`List[str]`, *optional*):
Model architectures that can be used with the model pretrained weights.
finetuning_task (`str`, *optional*):
Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow
or PyTorch) checkp... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
`"single_label_classification"` or `"multi_label_classification"`. | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
> Parameters linked to the tokenizer
tokenizer_class (`str`, *optional*):
The name of the associated tokenizer class to use (if none is set, will use the tokenizer associated to the
model by default).
prefix (`str`, *optional*):
A specific prompt that should be added... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
torchscript (`bool`, *optional*, defaults to `False`):
Whether or not the model should be used with Torchscript.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the
... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
`"float16"` string. | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
This attribute is currently not being used during model loading time, but this may change in the future
versions. But we can already start preparing for the future by saving the dtype with save_pretrained.
> TensorFlow specific parameters
use_bfloat16 (`bool`, *optional*, defaults to `Fals... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
model_type: str = ""
base_config_key: str = ""
sub_configs: Dict[str, "PretrainedConfig"] = {}
is_composition: bool = False
attribute_map: Dict[str, str] = {}
base_model_tp_plan: Optional[Dict[str, Any]] = None
_auto_class: Optional[str] = None
def __setattr__(self, key, value):
if ... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
def __init__(self, **kwargs):
# Attributes with defaults
self.return_dict = kwargs.pop("return_dict", True)
self.output_hidden_states = kwargs.pop("output_hidden_states", False)
self.output_attentions = kwargs.pop("output_attentions", False)
self.torchscript = kwargs.pop("torchsc... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
# Is decoder is used in encoder-decoder models to differentiate encoder from decoder
self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
self.is_decoder = kwargs.pop("is_decoder", False)
self.cross_attention_hidden_size = kwargs.pop("cross_attention_hidden_size", None)
self... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
# Fine-tuning task arguments
self.architectures = kwargs.pop("architectures", None)
self.finetuning_task = kwargs.pop("finetuning_task", None)
self.id2label = kwargs.pop("id2label", None)
self.label2id = kwargs.pop("label2id", None)
if self.label2id is not None and not isinstance... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
self.id2label = {int(key): value for key, value in self.id2label.items()}
# Keys are always strings in JSON so convert ids to int here.
else:
self.num_labels = kwargs.pop("num_labels", 2) | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
if self.torch_dtype is not None and isinstance(self.torch_dtype, str):
# we will start using self.torch_dtype in v5, but to be consistent with
# from_pretrained's torch_dtype arg convert it to an actual torch.dtype object
if is_torch_available():
import torch
... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
# task specific arguments
self.task_specific_params = kwargs.pop("task_specific_params", None)
# regression / multi-label classification
self.problem_type = kwargs.pop("problem_type", None)
allowed_problem_types = ("regression", "single_label_classification", "multi_label_classification... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
# Name or path to the pretrained checkpoint
self._name_or_path = str(kwargs.pop("name_or_path", ""))
# Config hash
self._commit_hash = kwargs.pop("_commit_hash", None)
# Attention implementation to use, if relevant.
self._attn_implementation_internal = kwargs.pop("attn_implement... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
# Additional attributes without default values
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
@property
def na... | 46 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/configuration_utils.py |
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