The Data2Vec model was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli. Data2Vec proposes a unified framework for self-supervised learning across different data modalities - text, audio and images. Importantly, predicted targets for pre-training are contextualized latent representations of the inputs, rather than modality-specific, context-independent targets.
The abstract from the paper is the following:
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches. Models and code are available at www.github.com/pytorch/fairseq/tree/master/examples/data2vec.
Tips:
This model was contributed by edugp and patrickvonplaten. sayakpaul and Rocketknight1 contributed Data2Vec for vision in TensorFlow.
The original code (for NLP and Speech) can be found here. The original code for vision can be found here.
( vocab_size = 30522 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 2 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 1 bos_token_id = 0 eos_token_id = 2 position_embedding_type = 'absolute' use_cache = True classifier_dropout = None **kwargs )
Parameters
int
, optional, defaults to 30522) —
Vocabulary size of the DATA2VEC model. Defines the number of different tokens that can be represented by
the inputs_ids
passed when calling Data2VecModel
.
int
, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
int
, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
int
, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
int
, optional, defaults to 3072) —
Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
str
or Callable
, optional, defaults to "gelu"
) —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu"
,
"relu"
, "silu"
and "gelu_new"
are supported.
float
, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
float
, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.
int
, optional, defaults to 512) —
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
int
, optional, defaults to 2) —
The vocabulary size of the token_type_ids
passed when calling Data2VecModel
.
float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
float
, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
str
, optional, defaults to "absolute"
) —
Type of position embedding. Choose one of "absolute"
, "relative_key"
, "relative_key_query"
. For
positional embeddings use "absolute"
. For more information on "relative_key"
, please refer to
Self-Attention with Relative Position Representations (Shaw et al.).
For more information on "relative_key_query"
, please refer to Method 4 in Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.).
bool
, optional, defaults to True
) —
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if config.is_decoder=True
.
float
, optional) —
The dropout ratio for the classification head.
This is the configuration class to store the configuration of a Data2VecTextModel and Data2VecTextModel. It is used to instantiate a Data2VecText model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Data2VecText facebook/data2vec-text-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Examples:
>>> from transformers import Data2VecTextConfig, Data2VecTextModel
>>> # Initializing a Data2VecText facebook/data2vec-text-base style configuration
>>> configuration = Data2VecTextConfig()
>>> # Initializing a model (with random weights) from the facebook/data2vec-text-base style configuration
>>> model = Data2VecTextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( vocab_size = 32 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout = 0.1 activation_dropout = 0.1 attention_dropout = 0.1 feat_proj_dropout = 0.0 final_dropout = 0.1 layerdrop = 0.1 initializer_range = 0.02 layer_norm_eps = 1e-05 feat_extract_activation = 'gelu' conv_dim = (512, 512, 512, 512, 512, 512, 512) conv_stride = (5, 2, 2, 2, 2, 2, 2) conv_kernel = (10, 3, 3, 3, 3, 2, 2) conv_bias = False num_conv_pos_embedding_groups = 16 conv_pos_kernel_size = 19 num_conv_pos_embeddings = 5 mask_time_prob = 0.05 mask_time_length = 10 mask_time_min_masks = 2 mask_feature_prob = 0.0 mask_feature_length = 10 mask_feature_min_masks = 0 ctc_loss_reduction = 'sum' ctc_zero_infinity = False use_weighted_layer_sum = False classifier_proj_size = 256 tdnn_dim = (512, 512, 512, 512, 1500) tdnn_kernel = (5, 3, 3, 1, 1) tdnn_dilation = (1, 2, 3, 1, 1) xvector_output_dim = 512 pad_token_id = 0 bos_token_id = 1 eos_token_id = 2 add_adapter = False adapter_kernel_size = 3 adapter_stride = 2 num_adapter_layers = 3 output_hidden_size = None **kwargs )
Parameters
int
, optional, defaults to 32) —
Vocabulary size of the Data2VecAudio model. Defines the number of different tokens that can be represented
by the inputs_ids
passed when calling Data2VecAudioModel or TFData2VecAudioModel
. Vocabulary size
of the model. Defines the different tokens that can be represented by the inputs_ids passed to the
forward method of Data2VecAudioModel.
int
, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
int
, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
int
, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
int
, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
str
or function
, optional, defaults to "gelu"
) —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu"
,
"relu"
, "selu"
and "gelu_new"
are supported.
float
, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
float
, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.
float
, optional, defaults to 0.1) —
The dropout probability for the final projection layer of Data2VecAudioForCTC.
float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
float
, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
float
, optional, defaults to 0.0) —
The dropout probability for output of the feature encoder.
str,
optional, defaults to
“gelu”) -- The non-linear activation function (function or string) in the 1D convolutional layers of the feature extractor. If string,
“gelu”,
“relu”,
“selu”and
“gelu_new”` are supported.
Tuple[int]
or List[int]
, optional, defaults to (512, 512, 512, 512, 512, 512, 512)
) —
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
feature encoder. The length of conv_dim defines the number of 1D convolutional layers.
Tuple[int]
or List[int]
, optional, defaults to (5, 2, 2, 2, 2, 2, 2)
) —
A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
of conv_stride defines the number of convolutional layers and has to match the length of conv_dim.
Tuple[int]
or List[int]
, optional, defaults to (10, 3, 3, 3, 3, 3, 3)
) —
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
length of conv_kernel defines the number of convolutional layers and has to match the length of
conv_dim.
bool
, optional, defaults to False
) —
Whether the 1D convolutional layers have a bias.
int
, optional, defaults to 128) —
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
embeddings layer.
int
, optional, defaults to 16) —
Number of groups of 1D convolutional positional embeddings layer.
float
, optional, defaults to 0.05) —
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates ”mask_time_problen(time_axis)/mask_time_length” independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, mask_time_prob should be `prob_vector_startmask_time_length`. Note that overlap may decrease the
int
, optional, defaults to 10) —
Length of vector span along the time axis.
int
, optional, defaults to 2), —
The minimum number of masks of length mask_feature_length
generated along the time axis, each time step,
irrespectively of mask_feature_prob
. Only relevant if ”mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks”
float
, optional, defaults to 0.0) —
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates ”mask_feature_problen(feature_axis)/mask_time_length” independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, mask_feature_prob should be `prob_vector_startmask_feature_length. Note that overlap may decrease the actual percentage of masked vectors. This is only relevant if
apply_spec_augment is
True`.
int
, optional, defaults to 10) —
Length of vector span along the feature axis.
int
, optional, defaults to 0), —
The minimum number of masks of length mask_feature_length
generated along the feature axis, each time
step, irrespectively of mask_feature_prob
. Only relevant if
”mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks”
str
, optional, defaults to "sum"
) —
Specifies the reduction to apply to the output of torch.nn.CTCLoss
. Only relevant when training an
instance of Data2VecAudioForCTC.
bool
, optional, defaults to False
) —
Whether to zero infinite losses and the associated gradients of torch.nn.CTCLoss
. Infinite losses mainly
occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
of Data2VecAudioForCTC.
bool
, optional, defaults to False
) —
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of Data2VecAudioForSequenceClassification.
int
, optional, defaults to 256) —
Dimensionality of the projection before token mean-pooling for classification.
Tuple[int]
or List[int]
, optional, defaults to (512, 512, 512, 512, 1500)
) —
A tuple of integers defining the number of output channels of each 1D convolutional layer in the TDNN
module of the XVector model. The length of tdnn_dim defines the number of TDNN layers.
Tuple[int]
or List[int]
, optional, defaults to (5, 3, 3, 1, 1)
) —
A tuple of integers defining the kernel size of each 1D convolutional layer in the TDNN module of the
XVector model. The length of tdnn_kernel has to match the length of tdnn_dim.
Tuple[int]
or List[int]
, optional, defaults to (1, 2, 3, 1, 1)
) —
A tuple of integers defining the dilation factor of each 1D convolutional layer in TDNN module of the
XVector model. The length of tdnn_dilation has to match the length of tdnn_dim.
int
, optional, defaults to 512) —
Dimensionality of the XVector embedding vectors.
bool
, optional, defaults to False
) —
Whether a convolutional network should be stacked on top of the Data2VecAudio Encoder. Can be very useful
for warm-starting Data2VecAudio for SpeechEncoderDecoder models.
int
, optional, defaults to 3) —
Kernel size of the convolutional layers in the adapter network. Only relevant if add_adapter is True
.
int
, optional, defaults to 2) —
Stride of the convolutional layers in the adapter network. Only relevant if add_adapter is True
.
int
, optional, defaults to 3) —
Number of convolutional layers that should be used in the adapter network. Only relevant if add_adapter is True
.
int
, optional) —
Dimensionality of the encoder output layer. If not defined, this defaults to hidden-size. Only relevant
if add_adapter is True
.
This is the configuration class to store the configuration of a Data2VecAudioModel. It is used to instantiate an Data2VecAudio model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Data2VecAudio facebook/data2vec-audio-base-960h architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> from transformers import Data2VecAudioConfig, Data2VecAudioModel
>>> # Initializing a Data2VecAudio facebook/data2vec-audio-base-960h style configuration
>>> configuration = Data2VecAudioConfig()
>>> # Initializing a model (with random weights) from the facebook/data2vec-audio-base-960h style configuration
>>> model = Data2VecAudioModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( vocab_size = 8192 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.0 attention_probs_dropout_prob = 0.0 initializer_range = 0.02 layer_norm_eps = 1e-12 is_encoder_decoder = False image_size = 224 patch_size = 16 num_channels = 3 use_mask_token = False use_absolute_position_embeddings = False use_relative_position_bias = False use_shared_relative_position_bias = False layer_scale_init_value = 0.1 drop_path_rate = 0.1 use_mean_pooling = True out_indices = [3, 5, 7, 11] pool_scales = [1, 2, 3, 6] use_auxiliary_head = True auxiliary_loss_weight = 0.4 auxiliary_channels = 256 auxiliary_num_convs = 1 auxiliary_concat_input = False semantic_loss_ignore_index = 255 **kwargs )
Parameters
int
, optional, defaults to 8092) —
Vocabulary size of the Data2VecVision model. Defines the number of different image tokens that can be used
during pre-training.
int
, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
int
, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
int
, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
int
, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
str
or function
, optional, defaults to "gelu"
) —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu"
,
"relu"
, "selu"
and "gelu_new"
are supported.
float
, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
float
, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
int
, optional, defaults to 224) —
The size (resolution) of each image.
int
, optional, defaults to 16) —
The size (resolution) of each patch.
int
, optional, defaults to 3) —
The number of input channels.
bool
, optional, defaults to False
) —
Whether to use a mask token for masked image modeling.
bool
, optional, defaults to False
) —
Whether to use BERT-style absolute position embeddings.
bool
, optional, defaults to False
) —
Whether to use T5-style relative position embeddings in the self-attention layers.
bool
, optional, defaults to False
) —
Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
float
, optional, defaults to 0.1) —
Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
float
, optional, defaults to 0.1) —
Stochastic depth rate per sample (when applied in the main path of residual layers).
bool
, optional, defaults to True
) —
Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
CLS token, before applying the classification head.
List[int]
, optional, defaults to [3, 5, 7, 11]
) —
Indices of the feature maps to use for semantic segmentation.
Tuple[int]
, optional, defaults to [1, 2, 3, 6]
) —
Pooling scales used in Pooling Pyramid Module applied on the last feature map.
bool
, optional, defaults to True
) —
Whether to use an auxiliary head during training.
float
, optional, defaults to 0.4) —
Weight of the cross-entropy loss of the auxiliary head.
int
, optional, defaults to 256) —
Number of channels to use in the auxiliary head.
int
, optional, defaults to 1) —
Number of convolutional layers to use in the auxiliary head.
bool
, optional, defaults to False
) —
Whether to concatenate the output of the auxiliary head with the input before the classification layer.
int
, optional, defaults to 255) —
The index that is ignored by the loss function of the semantic segmentation model.
This is the configuration class to store the configuration of a Data2VecVisionModel. It is used to instantiate an Data2VecVision model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Data2VecVision facebook/data2vec-vision-base architecture.
Example:
>>> from transformers import Data2VecVisionConfig, Data2VecVisionModel
>>> # Initializing a Data2VecVision data2vec_vision-base-patch16-224-in22k style configuration
>>> configuration = Data2VecVisionConfig()
>>> # Initializing a model (with random weights) from the data2vec_vision-base-patch16-224-in22k style configuration
>>> model = Data2VecVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
( config: Data2VecAudioConfig )
Parameters
The bare Data2VecAudio Model transformer outputting raw hidden-states without any specific head on top. Data2VecAudio was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_values: typing.Optional[torch.Tensor]
attention_mask: typing.Optional[torch.Tensor] = None
mask_time_indices: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, sequence_length)
) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install
soundfile). To prepare the array into input_values, the Wav2Vec2Processor should be used for padding
and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
:
attention_mask
should only be passed if the corresponding processor has config.return_attention_mask == True
. For all models whose processor has config.return_attention_mask == False
, such as
data2vec-audio-base, attention_mask
should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values
should simply be padded with 0 and passed without attention_mask
. Be aware that these
models also yield slightly different results depending on whether input_values
is padded or not.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.Wav2Vec2BaseModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Wav2Vec2BaseModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecAudioConfig) and inputs.
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.
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.
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.
The Data2VecAudioModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import Wav2Vec2Processor, Data2VecAudioModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
>>> model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base-960h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 292, 768]
( config )
Parameters
Data2VecAudio Model with a frame classification head on top for tasks like Speaker Diarization.
Data2VecAudio was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_values: typing.Optional[torch.Tensor]
attention_mask: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, sequence_length)
) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install
soundfile). To prepare the array into input_values, the Wav2Vec2Processor should be used for padding
and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
:
attention_mask
should only be passed if the corresponding processor has config.return_attention_mask == True
. For all models whose processor has config.return_attention_mask == False
, such as
data2vec-audio-base, attention_mask
should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values
should simply be padded with 0 and passed without attention_mask
. Be aware that these
models also yield slightly different results depending on whether input_values
is padded or not.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecAudioConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Classification loss.
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.num_labels)
) — Classification 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecAudioForAudioFrameClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import Wav2Vec2FeatureExtractor, Data2VecAudioForAudioFrameClassification
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-data2vec-audio-frame")
>>> model = Data2VecAudioForAudioFrameClassification.from_pretrained("hf-internal-testing/tiny-random-data2vec-audio-frame")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], return_tensors="pt", sampling_rate=sampling_rate)
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> probabilities = torch.sigmoid(logits[0])
>>> # labels is a one-hot array of shape (num_frames, num_speakers)
>>> labels = (probabilities > 0.5).long()
>>> labels[0].tolist()
[1, 1]
( config )
Parameters
Data2VecAudio Model with a language modeling
head on top for Connectionist Temporal Classification (CTC).
Data2VecAudio was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_values: typing.Optional[torch.Tensor]
attention_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Optional[torch.Tensor] = None
)
→
transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, sequence_length)
) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install
soundfile). To prepare the array into input_values, the Wav2Vec2Processor should be used for padding
and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
:
attention_mask
should only be passed if the corresponding processor has config.return_attention_mask == True
. For all models whose processor has config.return_attention_mask == False
, such as
data2vec-audio-base, attention_mask
should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values
should simply be padded with 0 and passed without attention_mask
. Be aware that these
models also yield slightly different results depending on whether input_values
is padded or not.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, target_length)
, optional) —
Labels for connectionist temporal classification. Note that target_length
has to be smaller or equal to
the sequence length of the output logits. Indices are selected in [-100, 0, ..., config.vocab_size - 1]
.
All labels set to -100
are ignored (masked), the loss is only computed for labels in [0, ..., config.vocab_size - 1]
.
Returns
transformers.modeling_outputs.CausalLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecAudioConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecAudioForCTC forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import Wav2Vec2Processor, Data2VecAudioForCTC
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
>>> model = Data2VecAudioForCTC.from_pretrained("facebook/data2vec-audio-base-960h")
>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
( config )
Parameters
Data2VecAudio Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting.
Data2VecAudio was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_values: typing.Optional[torch.Tensor]
attention_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Optional[torch.Tensor] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, sequence_length)
) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install
soundfile). To prepare the array into input_values, the Wav2Vec2Processor should be used for padding
and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
:
attention_mask
should only be passed if the corresponding processor has config.return_attention_mask == True
. For all models whose processor has config.return_attention_mask == False
, such as
data2vec-audio-base, attention_mask
should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values
should simply be padded with 0 and passed without attention_mask
. Be aware that these
models also yield slightly different results depending on whether input_values
is padded or not.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecAudioConfig) and inputs.
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).
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)
.
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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecAudioForSequenceClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import Wav2Vec2FeatureExtractor, Data2VecAudioForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-data2vec-seq-class")
>>> model = Data2VecAudioForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-data2vec-seq-class")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_ids = torch.argmax(logits, dim=-1).item()
>>> predicted_label = model.config.id2label[predicted_class_ids]
>>> predicted_label
'LABEL_1'
( config )
Parameters
Data2VecAudio Model with an XVector feature extraction head on top for tasks like Speaker Verification.
Data2VecAudio was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving etc.).
This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_values: typing.Optional[torch.Tensor]
attention_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Optional[torch.Tensor] = None
)
→
transformers.modeling_outputs.XVectorOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, sequence_length)
) —
Float values of input raw speech waveform. Values can be obtained by loading a .flac or .wav audio file
into an array of type List[float] or a numpy.ndarray, e.g. via the soundfile library (pip install
soundfile). To prepare the array into input_values, the Wav2Vec2Processor should be used for padding
and conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
:
attention_mask
should only be passed if the corresponding processor has config.return_attention_mask == True
. For all models whose processor has config.return_attention_mask == False
, such as
data2vec-audio-base, attention_mask
should
not be passed to avoid degraded performance when doing batched inference. For such models
input_values
should simply be padded with 0 and passed without attention_mask
. Be aware that these
models also yield slightly different results depending on whether input_values
is padded or not.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.XVectorOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.XVectorOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecAudioConfig) and inputs.
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.
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.
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.
The Data2VecAudioForXVector forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import Wav2Vec2FeatureExtractor, Data2VecAudioForXVector
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-data2vec-xvector")
>>> model = Data2VecAudioForXVector.from_pretrained("hf-internal-testing/tiny-random-data2vec-xvector")
>>> # audio file is decoded on the fly
>>> inputs = feature_extractor(
... [d["array"] for d in dataset[:2]["audio"]], sampling_rate=sampling_rate, return_tensors="pt", padding=True
... )
>>> with torch.no_grad():
... embeddings = model(**inputs).embeddings
>>> embeddings = torch.nn.functional.normalize(embeddings, dim=-1).cpu()
>>> # the resulting embeddings can be used for cosine similarity-based retrieval
>>> cosine_sim = torch.nn.CosineSimilarity(dim=-1)
>>> similarity = cosine_sim(embeddings[0], embeddings[1])
>>> threshold = 0.7 # the optimal threshold is dataset-dependent
>>> if similarity < threshold:
... print("Speakers are not the same!")
>>> round(similarity.item(), 2)
1.0
( config add_pooling_layer = True )
Parameters
The bare Data2VecText Model for text transformer outputting raw hidden-states without any specific head on top. Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the is_decoder
argument of the configuration set
to True
. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder
argument and
add_cross_attention
set to True
; an encoder_hidden_states
is then expected as an input to the forward pass.
.. _Attention is all you need: https://arxiv.org/abs/1706.03762
(
input_ids: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
token_type_ids: typing.Optional[torch.Tensor] = None
position_ids: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
encoder_hidden_states: typing.Optional[torch.Tensor] = None
encoder_attention_mask: typing.Optional[torch.Tensor] = None
past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
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. Used in the cross-attention if
the model is configured as a decoder.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]
:
tuple(tuple(torch.FloatTensor))
of length config.n_layers
with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values
are used, the user can optionally input only the last decoder_input_ids
(those that
don’t have their past key value states given to this model) of shape (batch_size, 1)
instead of all
decoder_input_ids
of shape (batch_size, sequence_length)
.
bool
, optional) —
If set to True
, past_key_values
key value states are returned and can be used to speed up decoding (see
past_key_values
).
Returns
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
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 first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
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)
.
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, sequence_length, sequence_length)
.
Attentions weights 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
and config.add_cross_attention=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 of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
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 optionally if
config.is_encoder_decoder=True
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 optionally if
config.is_encoder_decoder=True
in the cross-attention blocks) that can be used (see past_key_values
input) to speed up sequential decoding.
The Data2VecTextModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import RobertaTokenizer, Data2VecTextModel
>>> import torch
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextModel.from_pretrained("facebook/data2vec-text-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
( config )
Parameters
Data2VecText Model with a language modeling
head on top for CLM fine-tuning.
Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
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. Used in the cross-attention if
the model is configured as a decoder.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
[-100, 0, ..., config.vocab_size]
(see input_ids
docstring) Tokens with indices set to -100
are
ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
tuple(tuple(torch.FloatTensor))
of length config.n_layers
with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values
are used, the user can optionally input only the last decoder_input_ids
(those that
don’t have their past key value states given to this model) of shape (batch_size, 1)
instead of all
decoder_input_ids
of shape (batch_size, sequence_length)
.
bool
, optional) —
If set to True
, past_key_values
key value states are returned and can be used to speed up decoding (see
past_key_values
).
Returns
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Language modeling loss (for next-token prediction).
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights 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, sequence_length)
.
Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor))
, optional, returned when use_cache=True
is passed or when config.use_cache=True
) — Tuple of torch.FloatTensor
tuples of length config.n_layers
, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True
.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values
input) to speed up sequential decoding.
The Data2VecTextForCausalLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import Data2VecTextTokenizer, Data2VecTextForCausalLM, Data2VecTextConfig
>>> import torch
>>> tokenizer = Data2VecTextTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> config = Data2VecTextConfig.from_pretrained("data2vec-base")
>>> config.is_decoder = True
>>> model = Data2VecTextForCausalLM.from_pretrained("data2vec-base", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
( config )
Parameters
data2vec Model with a language modeling
head on top.
Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
encoder_hidden_states: typing.Optional[torch.FloatTensor] = None
encoder_attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size]
(see input_ids
docstring) Tokens with indices set to -100
are ignored (masked), the
loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
Dict[str, any]
, optional, defaults to {}) —
Used to hide legacy arguments that have been deprecated.
Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MaskedLMOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Masked language modeling (MLM) loss.
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.vocab_size)
) — Prediction scores of the language modeling head (scores for each vocabulary token 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecTextForMaskedLM forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import RobertaTokenizer, Data2VecTextForMaskedLM
>>> import torch
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextForMaskedLM.from_pretrained("facebook/data2vec-text-base")
>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # retrieve index of <mask>
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
( config )
Parameters
Data2VecText Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
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).
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)
.
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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecTextForSequenceClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of single-label classification:
>>> import torch
>>> from transformers import RobertaTokenizer, Data2VecTextForSequenceClassification
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextForSequenceClassification.from_pretrained("facebook/data2vec-text-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = Data2VecTextForSequenceClassification.from_pretrained("facebook/data2vec-text-base", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
Example of multi-label classification:
>>> import torch
>>> from transformers import RobertaTokenizer, Data2VecTextForSequenceClassification
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextForSequenceClassification.from_pretrained("facebook/data2vec-text-base", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = Data2VecTextForSequenceClassification.from_pretrained(
... "facebook/data2vec-text-base", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
... torch.float
... )
>>> loss = model(**inputs, labels=labels).loss
>>> loss.backward()
( config )
Parameters
Data2VecText Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_ids: typing.Optional[torch.LongTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, num_choices, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, num_choices, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, num_choices, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1]
where num_choices
is the size of the second dimension of the input tensors. (See
input_ids
above)
Returns
transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
loss (torch.FloatTensor
of shape (1,), optional, returned when labels
is provided) — Classification loss.
logits (torch.FloatTensor
of shape (batch_size, num_choices)
) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecTextForMultipleChoice forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import RobertaTokenizer, Data2VecTextForMultipleChoice
>>> import torch
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextForMultipleChoice.from_pretrained("facebook/data2vec-text-base")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0) # choice0 is correct (according to Wikipedia ;)), batch size 1
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels) # batch size is 1
>>> # the linear classifier still needs to be trained
>>> loss = outputs.loss
>>> logits = outputs.logits
( config )
Parameters
Data2VecText Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1]
.
Returns
transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.TokenClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
loss (torch.FloatTensor
of shape (1,)
, optional, returned when labels
is provided) — Classification loss.
logits (torch.FloatTensor
of shape (batch_size, sequence_length, config.num_labels)
) — Classification 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecTextForTokenClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import RobertaTokenizer, Data2VecTextForTokenClassification
>>> import torch
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextForTokenClassification.from_pretrained("facebook/data2vec-text-base")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_token_class_ids = logits.argmax(-1)
>>> # Note that tokens are classified rather then input words which means that
>>> # there might be more predicted token classes than words.
>>> # Multiple token classes might account for the same word
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
( config )
Parameters
Data2VecText Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute span start logits
and span end logits
).
Data2VecText was proposed in data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
token_type_ids: typing.Optional[torch.LongTensor] = None
position_ids: typing.Optional[torch.LongTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
start_positions: typing.Optional[torch.LongTensor] = None
end_positions: typing.Optional[torch.LongTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor
of shape (batch_size, sequence_length)
) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using RobertaTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor
of shape (batch_size, sequence_length)
, optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]
:
torch.LongTensor
of shape (batch_size, sequence_length)
, optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]
.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
torch.FloatTensor
of shape (batch_size, sequence_length, hidden_size)
, optional) —
Optionally, instead of passing input_ids
you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids
indices into associated vectors than the
model’s internal embedding lookup matrix.
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence
are not taken into account for computing the loss.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length
). Position outside of the sequence
are not taken into account for computing the loss.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecTextConfig) and inputs.
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 scores (before SoftMax).
end_logits (torch.FloatTensor
of shape (batch_size, sequence_length)
) — Span-end 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 layer) of shape (batch_size, sequence_length, 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, sequence_length, sequence_length)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecTextForQuestionAnswering forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import RobertaTokenizer, Data2VecTextForQuestionAnswering
>>> import torch
>>> tokenizer = RobertaTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Data2VecTextForQuestionAnswering.from_pretrained("facebook/data2vec-text-base")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
( config: Data2VecVisionConfig add_pooling_layer: bool = False )
Parameters
The bare Data2VecVision Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
pixel_values: typing.Optional[torch.Tensor] = None
bool_masked_pos: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.data2vec.modeling_data2vec_vision.Data2VecVisionModelOutputWithPooling
or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Pixel values can be obtained using BeitFeatureExtractor. See
BeitFeatureExtractor.call() for details.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.models.data2vec.modeling_data2vec_vision.Data2VecVisionModelOutputWithPooling
or tuple(torch.FloatTensor)
A transformers.models.data2vec.modeling_data2vec_vision.Data2VecVisionModelOutputWithPooling
or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecVisionConfig) and inputs.
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)
) — Average of the last layer hidden states of the patch tokens (excluding the [CLS] token) if
config.use_mean_pooling is set to True. If set to False, then the final hidden state of the [CLS] token
will be returned.
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.
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.
The Data2VecVisionModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import BeitFeatureExtractor, Data2VecVisionModel
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base")
>>> model = Data2VecVisionModel.from_pretrained("facebook/data2vec-vision-base")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 197, 768]
( config: Data2VecVisionConfig )
Parameters
Data2VecVision Model transformer with an image classification head on top (a linear layer on top of the average of the final hidden states of the patch tokens) e.g. for ImageNet.
This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Pixel values can be obtained using BeitFeatureExtractor. See
BeitFeatureExtractor.call() for details.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size,)
, optional) —
Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.ImageClassifierOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecVisionConfig) and inputs.
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).
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.
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.
The Data2VecVisionForImageClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import BeitFeatureExtractor, Data2VecVisionForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k")
>>> model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k")
>>> inputs = feature_extractor(image, return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
remote control, remote
( config: Data2VecVisionConfig )
Parameters
Data2VecVision Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
This model is a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
(
pixel_values: typing.Optional[torch.Tensor] = None
head_mask: typing.Optional[torch.Tensor] = None
labels: typing.Optional[torch.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.SemanticSegmenterOutput or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor
of shape (batch_size, num_channels, height, width)
) —
Pixel values. Pixel values can be obtained using BeitFeatureExtractor. See
BeitFeatureExtractor.call() for details.
torch.FloatTensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
torch.LongTensor
of shape (batch_size, height, width)
, optional) —
Ground truth semantic segmentation maps for computing the loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels > 1
, a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SemanticSegmenterOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.SemanticSegmenterOutput or a tuple of
torch.FloatTensor
(if return_dict=False
is passed or when config.return_dict=False
) comprising various
elements depending on the configuration (Data2VecVisionConfig) and inputs.
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.
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.
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)
.
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)
.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The Data2VecVisionForSemanticSegmentation forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoFeatureExtractor, Data2VecVisionForSemanticSegmentation
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/data2vec-vision-base")
>>> model = Data2VecVisionForSemanticSegmentation.from_pretrained("facebook/data2vec-vision-base")
>>> inputs = feature_extractor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # logits are of shape (batch_size, num_labels, height, width)
>>> logits = outputs.logits
( *args **kwargs )
Parameters
The bare Data2VecVision Model transformer outputting raw hidden-states without any specific head on top. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.).
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
pixel_values
only and nothing else: model(pixel_values)
model([pixel_values, attention_mask])
or model([pixel_values, attention_mask, token_type_ids])
model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
pixel_values: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
bool_masked_pos: typing.Optional[tensorflow.python.framework.ops.Tensor] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
training: bool = False
)
→
transformers.models.data2vec.modeling_tf_data2vec_vision.TFData2VecVisionModelOutputWithPooling
or tuple(tf.Tensor)
Parameters
np.ndarray
, tf.Tensor
, List[tf.Tensor]
`Dict[str, tf.Tensor]
or Dict[str, np.ndarray]
and each example must have the shape (batch_size, num_channels, height, width)
) —
Pixel values. Pixel values can be obtained using BeitFeatureExtractor. See
BeitFeatureExtractor.call() for details.
np.ndarray
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
Returns
transformers.models.data2vec.modeling_tf_data2vec_vision.TFData2VecVisionModelOutputWithPooling
or tuple(tf.Tensor)
A transformers.models.data2vec.modeling_tf_data2vec_vision.TFData2VecVisionModelOutputWithPooling
or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (Data2VecVisionConfig) and inputs.
last_hidden_state (tf.Tensor
of shape (batch_size, sequence_length, hidden_size)
) — Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor
of shape (batch_size, hidden_size)
) — Average of the last layer hidden states of the patch tokens (excluding the [CLS] token) if
config.use_mean_pooling is set to True. If set to False, then the final hidden state of the [CLS] token
will be returned.
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(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.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(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.
The TFData2VecVisionModel forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import BeitFeatureExtractor, TFData2VecVisionModel
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base")
>>> model = TFData2VecVisionModel.from_pretrained("facebook/data2vec-vision-base")
>>> inputs = feature_extractor(image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 197, 768]
( *args **kwargs )
Parameters
Data2VecVision Model transformer with an image classification head on top (a linear layer on top of the average of the final hidden states of the patch tokens) e.g. for ImageNet.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.).
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
pixel_values
only and nothing else: model(pixel_values)
model([pixel_values, attention_mask])
or model([pixel_values, attention_mask, token_type_ids])
model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
pixel_values: typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None
head_mask: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
labels: typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None
training: typing.Optional[bool] = False
)
→
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters
np.ndarray
, tf.Tensor
, List[tf.Tensor]
`Dict[str, tf.Tensor]
or Dict[str, np.ndarray]
and each example must have the shape (batch_size, num_channels, height, width)
) —
Pixel values. Pixel values can be obtained using BeitFeatureExtractor. See
BeitFeatureExtractor.call() for details.
np.ndarray
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
or np.ndarray
of shape (batch_size,)
, optional) —
Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels == 1
a regression loss is computed (Mean-Square loss), If
config.num_labels > 1
a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (Data2VecVisionConfig) and inputs.
loss (tf.Tensor
of shape (batch_size, )
, optional, returned when labels
is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor
of shape (batch_size, config.num_labels)
) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(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.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(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.
The TFData2VecVisionForImageClassification forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import BeitFeatureExtractor, TFData2VecVisionForImageClassification
>>> import tensorflow as tf
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> feature_extractor = BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k")
>>> model = TFData2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k")
>>> inputs = feature_extractor(image, return_tensors="tf")
>>> logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
>>> print(model.config.id2label[predicted_label])
remote control, remote
( *args **kwargs )
Parameters
Data2VecVision Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.).
This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers
accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit()
things should “just work” for you - just
pass your inputs and labels in any format that model.fit()
supports! If, however, you want to use the second
format outside of Keras methods like fit()
and predict()
, such as when creating your own layers or models with
the Keras Functional
API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
pixel_values
only and nothing else: model(pixel_values)
model([pixel_values, attention_mask])
or model([pixel_values, attention_mask, token_type_ids])
model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})
Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!
(
pixel_values: typing.Optional[tensorflow.python.framework.ops.Tensor] = None
head_mask: typing.Optional[tensorflow.python.framework.ops.Tensor] = None
labels: typing.Optional[tensorflow.python.framework.ops.Tensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_tf_outputs.TFSemanticSegmenterOutput
or tuple(tf.Tensor)
Parameters
np.ndarray
, tf.Tensor
, List[tf.Tensor]
`Dict[str, tf.Tensor]
or Dict[str, np.ndarray]
and each example must have the shape (batch_size, num_channels, height, width)
) —
Pixel values. Pixel values can be obtained using BeitFeatureExtractor. See
BeitFeatureExtractor.call() for details.
np.ndarray
or tf.Tensor
of shape (num_heads,)
or (num_layers, num_heads)
, optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail.
bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail.
bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used
in eager mode, in graph mode the value will always be set to True.
bool
, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
tf.Tensor
of shape (batch_size, height, width)
, optional) —
Ground truth semantic segmentation maps for computing the loss. Indices should be in [0, ..., config.num_labels - 1]
. If config.num_labels > 1
, a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_tf_outputs.TFSemanticSegmenterOutput
or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.TFSemanticSegmenterOutput
or a tuple of tf.Tensor
(if
return_dict=False
is passed or when config.return_dict=False
) comprising various elements depending on the
configuration (Data2VecVisionConfig) and inputs.
loss (tf.Tensor
of shape (1,)
, optional, returned when labels
is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor
of shape (batch_size, config.num_labels, logits_height, logits_width)
) — Classification scores for each pixel.
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.
hidden_states (tuple(tf.Tensor)
, optional, returned when output_hidden_states=True
is passed or when config.output_hidden_states=True
) — Tuple of tf.Tensor
(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)
.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(tf.Tensor)
, optional, returned when output_attentions=True
is passed or when config.output_attentions=True
) — Tuple of tf.Tensor
(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.
The TFData2VecVisionForSemanticSegmentation forward method, overrides the __call__
special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
>>> from transformers import AutoFeatureExtractor, TFData2VecVisionForSemanticSegmentation
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/data2vec-vision-base")
>>> model = TFData2VecVisionForSemanticSegmentation.from_pretrained("facebook/data2vec-vision-base")
>>> inputs = feature_extractor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # logits are of shape (batch_size, num_labels, height, width)
>>> logits = outputs.logits