source
stringclasses 470
values | url
stringlengths 49
167
| file_type
stringclasses 1
value | chunk
stringlengths 1
512
| chunk_id
stringlengths 5
9
|
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqspectrogramoutput | .md | one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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 shape `(batch_size, num_heads, sequence_length,
sequence_length)`. | 458_22_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqspectrogramoutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads. | 458_22_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#semanticsegmenteroutput | .md | modeling_outputs.SemanticSegmenterOutput
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.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}> | 458_23_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#semanticsegmenteroutput | .md | Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip> | 458_23_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#semanticsegmenteroutput | .md | original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
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 shape `(batch_size, patch_size, hidden_size)`. | 458_23_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#semanticsegmenteroutput | .md | one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`.
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 shape `(batch_size, num_heads, patch_size,
sequence_length)`. | 458_23_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#semanticsegmenteroutput | .md | 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. | 458_23_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#imageclassifieroutput | .md | modeling_outputs.ImageClassifierOutput
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.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). | 458_24_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#imageclassifieroutput | .md | Classification (or regression if config.num_labels==1) scores (before SoftMax).
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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage. | 458_24_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#imageclassifieroutput | .md | (also called feature maps) of the model at the output of each stage.
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_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads. | 458_24_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#imageclassifieroutputwithnoattention | .md | modeling_outputs.ImageClassifierOutput
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.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax). | 458_25_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#imageclassifieroutputwithnoattention | .md | Classification (or regression if config.num_labels==1) scores (before SoftMax).
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 stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states
(also called feature maps) of the model at the output of each stage. | 458_25_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#imageclassifieroutputwithnoattention | .md | (also called feature maps) of the model at the output of each stage.
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_size, num_heads, patch_size,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
WithNoAttention | 458_25_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#depthestimatoroutput | .md | modeling_outputs.DepthEstimatorOutput
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.FloatTensor` of shape `(batch_size, height, width)`):
Predicted depth for each pixel. | 458_26_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#depthestimatoroutput | .md | predicted_depth (`torch.FloatTensor` of shape `(batch_size, height, width)`):
Predicted depth for each pixel.
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 shape `(batch_size, num_channels, height, width)`. | 458_26_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#depthestimatoroutput | .md | one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
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 shape `(batch_size, num_heads, patch_size,
sequence_length)`. | 458_26_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#depthestimatoroutput | .md | 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. | 458_26_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#wav2vec2basemodeloutput | .md | modeling_outputs.Wav2Vec2BaseModelOutput
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 of the model.
extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`):
Sequence of extracted feature vectors of the last convolutional layer of the model. | 458_27_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#wav2vec2basemodeloutput | .md | Sequence of extracted feature vectors of the last convolutional layer of the model.
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 + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs. | 458_27_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#wav2vec2basemodeloutput | .md | 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_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads. | 458_27_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#xvectoroutput | .md | modeling_outputs.XVectorOutput
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)`):
Classification hidden states before AMSoftmax.
embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`):
Utterance embeddings used for vector similarity-based retrieval. | 458_28_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#xvectoroutput | .md | Utterance embeddings used for vector similarity-based retrieval.
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 + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs. | 458_28_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#xvectoroutput | .md | 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_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads. | 458_28_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | modeling_outputs.Seq2SeqTSModelOutput
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)`):
Sequence of hidden-states at the output of the last layer of the decoder of the model.
If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output. | 458_29_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
hidden_size)` is output.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape | 458_29_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
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. | 458_29_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | 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` 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 shape `(batch_size, sequence_length, hidden_size)`. | 458_29_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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 layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. | 458_29_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
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 `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | 458_29_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
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-states at the output of the last layer of the encoder of the model. | 458_29_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 shape `(batch_size, sequence_length, hidden_size)`. | 458_29_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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 layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. | 458_29_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
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 to give the model inputs of the same
magnitude and then used to shift back to the original magnitude. | 458_29_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | magnitude and then used to shift back to the original magnitude.
scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
Scaling values of each time series' context window which is used to give the model inputs of the same
magnitude and then used to rescale back to the original magnitude.
static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*): | 458_29_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtsmodeloutput | .md | static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*):
Static features of each time series' in a batch which are copied to the covariates at inference time. | 458_29_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | modeling_outputs.Seq2SeqTSPredictionOutput
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):
Distributional loss.
params (`torch.FloatTensor` of shape `(batch_size, num_samples, num_params)`):
Parameters of the chosen distribution. | 458_30_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | params (`torch.FloatTensor` of shape `(batch_size, num_samples, num_params)`):
Parameters of the chosen distribution.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape | 458_30_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
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. | 458_30_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | 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` 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 shape `(batch_size, sequence_length, hidden_size)`. | 458_30_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the decoder at the output of each layer plus the 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 layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. | 458_30_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
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 `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, | 458_30_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
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-states at the output of the last layer of the encoder of the model. | 458_30_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_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 shape `(batch_size, sequence_length, hidden_size)`. | 458_30_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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 shape `(batch_size, num_heads, sequence_length,
sequence_length)`. | 458_30_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
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 to give the model inputs of the same
magnitude and then used to shift back to the original magnitude. | 458_30_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | magnitude and then used to shift back to the original magnitude.
scale (`torch.FloatTensor` of shape `(batch_size,)` or `(batch_size, input_size)`, *optional*):
Scaling values of each time series' context window which is used to give the model inputs of the same
magnitude and then used to rescale back to the original magnitude.
static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*): | 458_30_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#seq2seqtspredictionoutput | .md | static_features (`torch.FloatTensor` of shape `(batch_size, feature size)`, *optional*):
Static features of each time series' in a batch which are copied to the covariates at inference time. | 458_30_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#sampletspredictionoutput | .md | modeling_outputs.SampleTSPredictionOutput
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, prediction_length, input_size)`):
Sampled values from the chosen distribution. | 458_31_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfbasemodeloutput | .md | [[autodoc]] modeling_tf_outputs.TFBaseModelOutput: No module named 'tensorflow' | 458_32_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfbasemodeloutputwithpooling | .md | [[autodoc]] modeling_tf_outputs.TFBaseModelOutput: No module named 'tensorflow'WithPooling | 458_33_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfbasemodeloutputwithpoolingandcrossattentions | .md | [[autodoc]] modeling_tf_outputs.TFBaseModelOutput: No module named 'tensorflow'WithPoolingAndCrossAttentions | 458_34_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfbasemodeloutputwithpast | .md | [[autodoc]] modeling_tf_outputs.TFBaseModelOutput: No module named 'tensorflow'WithPast | 458_35_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfbasemodeloutputwithpastandcrossattentions | .md | [[autodoc]] modeling_tf_outputs.TFBaseModelOutput: No module named 'tensorflow'WithPastAndCrossAttentions | 458_36_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfseq2seqmodeloutput | .md | [[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput: No module named 'tensorflow' | 458_37_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfcausallmoutput | .md | [[autodoc]] modeling_tf_outputs.TFCausalLMOutput: No module named 'tensorflow' | 458_38_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfcausallmoutputwithcrossattentions | .md | [[autodoc]] modeling_tf_outputs.TFCausalLMOutput: No module named 'tensorflow'WithCrossAttentions | 458_39_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfcausallmoutputwithpast | .md | [[autodoc]] modeling_tf_outputs.TFCausalLMOutput: No module named 'tensorflow'WithPast | 458_40_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfmaskedlmoutput | .md | [[autodoc]] modeling_tf_outputs.TFMaskedLMOutput: No module named 'tensorflow' | 458_41_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfseq2seqlmoutput | .md | [[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput: No module named 'tensorflow' | 458_42_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfnextsentencepredictoroutput | .md | [[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput: No module named 'tensorflow' | 458_43_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfsequenceclassifieroutput | .md | [[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput: No module named 'tensorflow' | 458_44_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfseq2seqsequenceclassifieroutput | .md | [[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput: No module named 'tensorflow' | 458_45_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfmultiplechoicemodeloutput | .md | [[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput: No module named 'tensorflow' | 458_46_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tftokenclassifieroutput | .md | [[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput: No module named 'tensorflow' | 458_47_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfquestionansweringmodeloutput | .md | [[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput: No module named 'tensorflow' | 458_48_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#tfseq2seqquestionansweringmodeloutput | .md | [[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput: No module named 'tensorflow' | 458_49_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxbasemodeloutput | .md | [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput: No module named 'flax' | 458_50_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxbasemodeloutputwithpast | .md | [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput: No module named 'flax'WithPast | 458_51_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxbasemodeloutputwithpooling | .md | [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput: No module named 'flax'WithPooling | 458_52_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxbasemodeloutputwithpastandcrossattentions | .md | [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput: No module named 'flax'WithPastAndCrossAttentions | 458_53_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqmodeloutput | .md | [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput: No module named 'flax' | 458_54_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxcausallmoutputwithcrossattentions | .md | [[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions: No module named 'flax' | 458_55_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxmaskedlmoutput | .md | [[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput: No module named 'flax' | 458_56_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqlmoutput | .md | [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput: No module named 'flax' | 458_57_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxnextsentencepredictoroutput | .md | [[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput: No module named 'flax' | 458_58_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxsequenceclassifieroutput | .md | [[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput: No module named 'flax' | 458_59_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqsequenceclassifieroutput | .md | [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput: No module named 'flax' | 458_60_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxmultiplechoicemodeloutput | .md | [[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput: No module named 'flax' | 458_61_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxtokenclassifieroutput | .md | [[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput: No module named 'flax' | 458_62_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxquestionansweringmodeloutput | .md | [[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput: No module named 'flax' | 458_63_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/output.md | https://huggingface.co/docs/transformers/en/main_classes/output/#flaxseq2seqquestionansweringmodeloutput | .md | [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput: No module named 'flax' | 458_64_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/ | .md | <!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | 459_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 459_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#processors | .md | Processors can mean two different things in the Transformers library:
- the objects that pre-process inputs for multi-modal models such as [Wav2Vec2](../model_doc/wav2vec2) (speech and text)
or [CLIP](../model_doc/clip) (text and vision)
- deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD. | 459_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#multi-modal-processors | .md | Any multi-modal model will require an object to encode or decode the data that groups several modalities (among text,
vision and audio). This is handled by objects called processors, which group together two or more processing objects
such as tokenizers (for the text modality), image processors (for vision) and feature extractors (for audio).
Those processors inherit from the following base class that implements the saving and loading functionality: | 459_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#multi-modal-processors | .md | Those processors inherit from the following base class that implements the saving and loading functionality:
This is a mixin used to provide saving/loading functionality for all processor classes. | 459_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#deprecated-processors | .md | All processors follow the same architecture which is that of the
[`~data.processors.utils.DataProcessor`]. The processor returns a list of
[`~data.processors.utils.InputExample`]. These
[`~data.processors.utils.InputExample`] can be converted to
[`~data.processors.utils.InputFeatures`] in order to be fed to the model.
data.processors.utils.DataProcessor
Base class for data converters for sequence classification data sets.
data.processors.utils.InputExample | 459_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#deprecated-processors | .md | Base class for data converters for sequence classification data sets.
data.processors.utils.InputExample
A single training/test example for simple sequence classification.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks. | 459_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#deprecated-processors | .md | text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
data.processors.utils.InputFeatures
A single set of features of data. Property names are the same names as the corresponding inputs to a model.
Args:
input_ids: Indices of input sequence tokens in the vocabulary. | 459_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#deprecated-processors | .md | Args:
input_ids: Indices of input sequence tokens in the vocabulary.
attention_mask: Mask to avoid performing attention on padding token indices.
Mask values selected in `[0, 1]`: Usually `1` for tokens that are NOT MASKED, `0` for MASKED (padded)
tokens.
token_type_ids: (Optional) Segment token indices to indicate first and second
portions of the inputs. Only some models use them.
label: (Optional) Label corresponding to the input. Int for classification problems,
float for regression problems. | 459_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#glue | .md | [General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com/) is a benchmark that evaluates the
performance of models across a diverse set of existing NLU tasks. It was released together with the paper [GLUE: A
multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7)
This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched), CoLA, SST2, STSB,
QQP, QNLI, RTE and WNLI. | 459_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#glue | .md | QQP, QNLI, RTE and WNLI.
Those processors are:
- [`~data.processors.utils.MrpcProcessor`]
- [`~data.processors.utils.MnliProcessor`]
- [`~data.processors.utils.MnliMismatchedProcessor`]
- [`~data.processors.utils.Sst2Processor`]
- [`~data.processors.utils.StsbProcessor`]
- [`~data.processors.utils.QqpProcessor`]
- [`~data.processors.utils.QnliProcessor`]
- [`~data.processors.utils.RteProcessor`]
- [`~data.processors.utils.WnliProcessor`] | 459_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#glue | .md | - [`~data.processors.utils.RteProcessor`]
- [`~data.processors.utils.WnliProcessor`]
Additionally, the following method can be used to load values from a data file and convert them to a list of
[`~data.processors.utils.InputExample`].
data.processors.glue.glue_convert_examples_to_features
Loads a data file into a list of `InputFeatures`
Args:
examples: List of `InputExamples` or `tf.data.Dataset` containing the examples.
tokenizer: Instance of a tokenizer that will tokenize the examples | 459_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#glue | .md | tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length. Defaults to the tokenizer's max_len
task: GLUE task
label_list: List of labels. Can be obtained from the processor using the `processor.get_labels()` method
output_mode: String indicating the output mode. Either `regression` or `classification`
Returns:
If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the task-specific | 459_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#glue | .md | Returns:
If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the task-specific
features. If the input is a list of `InputExamples`, will return a list of task-specific `InputFeatures` which
can be fed to the model. | 459_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#xnli | .md | [The Cross-Lingual NLI Corpus (XNLI)](https://www.nyu.edu/projects/bowman/xnli/) is a benchmark that evaluates the
quality of cross-lingual text representations. XNLI is crowd-sourced dataset based on [*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/): pairs of text are labeled with textual entailment annotations for 15
different languages (including both high-resource language such as English and low-resource languages such as Swahili). | 459_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#xnli | .md | different languages (including both high-resource language such as English and low-resource languages such as Swahili).
It was released together with the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053)
This library hosts the processor to load the XNLI data:
- [`~data.processors.utils.XnliProcessor`]
Please note that since the gold labels are available on the test set, evaluation is performed on the test set. | 459_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#xnli | .md | Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
An example using these processors is given in the [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py) script. | 459_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#squad | .md | [The Stanford Question Answering Dataset (SQuAD)](https://rajpurkar.github.io/SQuAD-explorer//) is a benchmark that
evaluates the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version
(v1.1) was released together with the paper [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250). The second version (v2.0) was released alongside the paper [Know What You Don't | 459_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/processors.md | https://huggingface.co/docs/transformers/en/main_classes/processors/#squad | .md | Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822).
This library hosts a processor for each of the two versions: | 459_6_1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.