Data2VecTextConfig

ention probabilities.   max_position_embeddings (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).   type_vocab_size (int, optional, defaults to 2) —
The vocabulary size of the token_type_ids passed when calling Data2VecModel.   initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.   position_embedding_type (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


Data2VecTextConfig

 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.).   is_decoder (bool, optional, defaults to False) —
Whether the model is used as a decoder or not. If False, the model is used as an encoder.   use_cache (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.   classifier_dropout (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 Data2Ve


Data2VecTextConfig

cText 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:   Copied >>> 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.confi


Data2VecAudioConfig

class transformers.Data2VecAudioConfig  < source > ( 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


Data2VecAudioConfig

, 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   vocab_size (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.   hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.   num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.   num_attention_heads (int, optional, defaults to 12) —
Number of attention heads f


Data2VecAudioConfig

or each attention layer in the Transformer encoder.   intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.   hidden_act (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.   hidden_dropout (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.   activation_dropout (float, optional, defaults to 0.1) —
The dropout ratio for activations inside the fully connected layer.   attention_dropout (float, optional, defaults to 0.1) —
The dropout ratio for the attention probabilities.   final_dropout (float, optional, defaults to 0.1) —
The dropout probability for the final projection layer of Data2VecAudioForCTC.   layerdrop


Data2VecAudioConfig

 (float, optional, defaults to 0.1) —
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.   initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.   feat_proj_dropout (float, optional, defaults to 0.0) —
The dropout probability for output of the feature encoder.   feat_extract_activation (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.   conv_dim (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


Data2VecAudioConfig

 each 1D convolutional layer in the
feature encoder. The length of conv_dim defines the number of 1D convolutional layers.   conv_stride (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.   conv_kernel (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.   conv_bias (bool, optional, defaults to False) —
Whether the 1D convolutional layers have a bias.   num_conv_pos_embeddings (int, optional, defaults to 128) —
Number of convolutional positional embeddings. Defines the kern


Data2VecAudioConfig

el size of 1D convolutional positional
embeddings layer.   num_conv_pos_embedding_groups (int, optional, defaults to 16) —
Number of groups of 1D convolutional positional embeddings layer.   mask_time_prob (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   mask_time_length (int, optional, defaults to 10) —
Length of vector span along the time axis.   mask_time_min_masks (int, optional, defaults to 2), —
The minimum number of masks of length mask_feature_length generated along the time axis, each time step,
irrespectively


Data2VecAudioConfig

of mask_feature_prob. Only relevant if ”mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks”   mask_feature_prob (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`.   mask_feature_length (int, optional, defaults to 10) —
Length of vector span along the feature axis.   mask_feature_min_masks (int, optional, defaults to 0), —
The minimum number of masks of length mask_feature_length generated along the featu


Data2VecAudioConfig

re 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”   ctc_loss_reduction (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.   ctc_zero_infinity (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.   use_weighted_layer_sum (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.   classifier_proj_size (int, optional, defaults to 256) —
Dimensionality of


Data2VecAudioConfig

 the projection before token mean-pooling for classification.   tdnn_dim (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.   tdnn_kernel (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.   tdnn_dilation (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.   xvector_output_dim (int, optional, defaults to 512) —
Dimensionality


Data2VecAudioConfig

 of the XVector embedding vectors.   add_adapter (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.   adapter_kernel_size (int, optional, defaults to 3) —
Kernel size of the convolutional layers in the adapter network. Only relevant if add_adapter is True.   adapter_stride (int, optional, defaults to 2) —
Stride of the convolutional layers in the adapter network. Only relevant if add_adapter is True.   num_adapter_layers (int, optional, defaults to 3) —
Number of convolutional layers that should be used in the adapter network. Only relevant if add_adapter is True.   output_hidden_size (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 st


Data2VecAudioConfig

ore 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:   Copied >>> 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.c


class transformers.Data2VecVisionConfig  < source > ( 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 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   hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layer


s and the pooler layer.   num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.   num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.   intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.   hidden_act (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.   hidden_dropout_prob (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.   attention_probs_dropout_prob (float, optional, defaults to 0.0) —
The dropout ratio for the


 attention probabilities.   initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.   image_size (int, optional, defaults to 224) —
The size (resolution) of each image.   patch_size (int, optional, defaults to 16) —
The size (resolution) of each patch.   num_channels (int, optional, defaults to 3) —
The number of input channels.   use_mask_token (bool, optional, defaults to False) —
Whether to use a mask token for masked image modeling.   use_absolute_position_embeddings (bool, optional, defaults to False) —
Whether to use BERT-style absolute position embeddings.   use_relative_position_bias (bool, optional, defaults to False) —
Whether to use T5-style rela


tive position embeddings in the self-attention layers.   use_shared_relative_position_bias (bool, optional, defaults to False) —
Whether to use the same relative position embeddings across all self-attention layers of the Transformer.   layer_scale_init_value (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.   drop_path_rate (float, optional, defaults to 0.1) —
Stochastic depth rate per sample (when applied in the main path of residual layers).   use_mean_pooling (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.   out_indices (List[int], optional, defaults to [3, 5, 7, 11]) —
Indices of the feature maps to use for semantic seg


mentation.   pool_scales (Tuple[int], optional, defaults to [1, 2, 3, 6]) —
Pooling scales used in Pooling Pyramid Module applied on the last feature map.   use_auxiliary_head (bool, optional, defaults to True) —
Whether to use an auxiliary head during training.   auxiliary_loss_weight (float, optional, defaults to 0.4) —
Weight of the cross-entropy loss of the auxiliary head.   auxiliary_channels (int, optional, defaults to 256) —
Number of channels to use in the auxiliary head.   auxiliary_num_convs (int, optional, defaults to 1) —
Number of convolutional layers to use in the auxiliary head.   auxiliary_concat_input (bool, optional, defaults to False) —
Whether to concatenate the output of the auxiliary head with the input before the classification layer.   semantic_loss_ignore_index (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:   Copied >>> 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.c


Data2VecAudioModel

class transformers.Data2VecAudioModel  < source > ( config: Data2VecAudioConfig  )   Parameters   config (Data2VecAudioConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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


Data2VecAudioModel

to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( 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   input_values (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 AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor


Data2VecAudioModel

.call() for details.   attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
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.
   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions


Data2VecAudioModel

under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (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 convolution


Data2VecAudioModel

al 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 th


Data2VecAudioModel

e 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:   Copied >>> from transformers import AutoProcessor, 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 = AutoProcessor.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]


class transformers.Data2VecAudioForAudioFrameClassification  < source > ( config  )   Parameters   config (Data2VecAudioConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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.  forward  < source > ( 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   input_values (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 inp


ut_values, the AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.   attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
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 y


ield slightly different results depending on whether input_values is padded or not.
   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (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_o


utputs.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).
H


idden-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:   Copied >>> from transformers import AutoFea


tureExtractor, 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 = AutoFeatureExtractor.from_pretrained("facebook/data2vec-audio-base-960h")
>>> model = Data2VecAudioForAudioFrameClassification.from_pretrained("facebook/data2vec-audio-base-960h")
>>> # 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(


Data2VecAudioForCTC

class transformers.Data2VecAudioForCTC  < source > ( config  )   Parameters   config (Data2VecAudioConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 PyTorc


Data2VecAudioForCTC

h Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( 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   input_values (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 AutoProcessor should be used for padding and
conversion into a tensor of type torch.Flo


Data2VecAudioForCTC

atTensor. See Wav2Vec2Processor.call() for details.   attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
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.
   output_attentions (bool, optional) —
Whether or not to re


Data2VecAudioForCTC

turn the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (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
tor


Data2VecAudioForCTC

ch.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 embe


Data2VecAudioForCTC

dding 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:   Copied >>> from transformers import AutoProcessor, Data2VecAudioForCTC
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_


Data2VecAudioForCTC

demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> processor = AutoProcessor.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'
>>> inputs["labels"] = processor(text=dataset[0]["text"], return_tensors="pt").input_ids
>>> # compute loss
>>> loss = model(**inputs).loss
>>> round(loss.item(), 2)
66.9


class transformers.Data2VecAudioForSequenceClassification  < source > ( config  )   Parameters   config (Data2VecAudioConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 PyT


orch 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.  forward  < source > ( 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   input_values (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 AutoProcessor sho


uld be used for padding and
conversion into a tensor of type torch.FloatTensor. See Wav2Vec2Processor.call() for details.   attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
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 pad


ded or not.
   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (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.SequenceClassifie


rOutput 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 embe


dding 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:   Copied >>> from transformers import AutoFeatureExtractor, Data2VecAudioForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> dataset = loa


d_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/data2vec-audio-base-960h")
>>> model = Data2VecAudioForSequenceClassification.from_pretrained("facebook/data2vec-audio-base-960h")
>>> # 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]
>>> # compute loss - target_label is e.g. "down"
>>> target_label = model.config.id2label[0]
>>> inputs["labels"] = torch.tensor([model.config.label2id[target_label]])
>>> loss = model(**inputs).l


class transformers.Data2VecAudioForXVector  < source > ( config  )   Parameters   config (Data2VecAudioConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 PyTorc


h Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( 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   input_values (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 AutoProcessor should be used for padding and
conversion into a tensor of type torch.FloatT


ensor. See Wav2Vec2Processor.call() for details.   attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
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.
   output_attentions (bool, optional) —
Whether or not to return


the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (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=Fals


e) 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:   Copied >>> from transformers import AutoFeatureExtractor, Data2VecAudioForXVector
>>> from datasets import load_dataset
>>> import torch
>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.s


ort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/data2vec-audio-base-960h")
>>> model = Data2VecAudioForXVector.from_pretrained("facebook/data2vec-audio-base-960h")
>>> # 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 sa


Data2VecTextModel

class transformers.Data2VecTextModel  < source > ( config add_pooling_layer = True  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 r


Data2VecTextModel

egular 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  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tenso


Data2VecTextModel

r] = 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   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attent


Data2VecTextModel

ion_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (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]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?   position_ids (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].
What are position IDs?   head_mask (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


Data2VecTextModel

in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (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.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   encoder_hidden_states  (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidd


Data2VecTextModel

en-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.   encoder_attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
   past_key_values (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


Data2VecTextModel

 all
decoder_input_ids of shape (batch_size, sequence_length).   use_cache (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 auxiliar


Data2VecTextModel

y 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 we


Data2VecTextModel

ights 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_


Data2VecTextModel

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:   Copied >>> from transformers import AutoTokenizer, Data2VecTextModel
>>> import torch
>>> tokenizer = AutoTokenizer.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_hidd


class transformers.Data2VecTextForCausalLM  < source > ( config  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 su


bclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( 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_dic


t: typing.Optional[bool] = None  ) → transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (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]:
0 corresponds to a sentence A token


,
1 corresponds to a sentence B token.
What are token type IDs?   position_ids (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].
What are position IDs?   head_mask (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]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (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 embeddin


g lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   encoder_hidden_states  (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.   encoder_attention_mask (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-at


tention if the model is configured as a decoder. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
   labels (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]   past_key_values (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).   use_cache (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.voc


ab_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:   Copied >>> from transformers import AutoTokenizer, Data2VecTextForCausalLM, Data2VecTextConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> config = Data2VecTextConfig.from_pretrained("facebook/data2vec-text-base")
>>> config.is_decoder = True
>>> model = Data2VecTextForCausalLM.from_pretrained("facebook/data2vec-text-base", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logit


class transformers.Data2VecTextForMaskedLM  < source > ( config  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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.M


odule subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( 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 tu


ple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (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]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?   position


_ids (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].
What are position IDs?   head_mask (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]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (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.   output_attentions (bool


, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (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]   kwargs (Dict[str, any], optional, defaults to {}) —
Used to hide legacy arguments that have been deprecated.   Returns
t


ransformers.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 f


or 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 i


gnores them.  Example:   Copied >>> from transformers import AutoTokenizer, Data2VecTextForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.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)
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>> # mask labels of non-<mask> tokens
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
>>> outputs = model(**inputs, labels=la


class transformers.Data2VecTextForSequenceClassification  < source > ( config  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 s


aving, 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.  forward  < source > ( 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   in


put_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (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]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?   position_ids (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].
What are position IDs?   head_mask (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]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (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.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all


attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (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), o


ptional, 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:   Copied >>> import torch
>>> from transformers import AutoTokenizer, Data2VecTextForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/data


2vec-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()
>>> # 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  Example of multi-label classification:   Copied >>> import torch
>>> from transformers import AutoTokenizer, Data2VecTextForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> model = Da


ta2VecTextForSequenceClassification.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_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
>>> # 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.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels)


class transformers.Data2VecTextForMultipleChoice  < source > ( config  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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.  forward  < source > ( 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   input_ids (torch.LongTensor of shape (batch_size, num_choices, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrained


Tokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (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]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (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]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?   position_ids (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].
What are position IDs?   head_mask (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]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (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.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain


 tuple.   labels (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_hi


dden_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 ca


re of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, Data2VecTextForMultipleChoice
>>> import torch
>>> tokenizer = AutoTokenizer.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.log


class transformers.Data2VecTextForTokenClassification  < source > ( config  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 mode


l 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.  forward  < source > ( 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   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtain


ed using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   token_type_ids (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]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?   position_ids (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].
What are position IDs?   head_mask (torch.FloatTe


nsor 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]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (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.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to r


eturn a ModelOutput instead of a plain tuple.   labels (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 torc


h.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:   Copied >>> from transformers import AutoTokenizer, Data2VecTextForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.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]]
>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels


class transformers.Data2VecTextForQuestionAnswering  < source > ( config  )   Parameters   config (Data2VecTextConfig) — Model configuration class with all the parameters of the
model. Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.    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 downl


oading 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.  forward  < source > ( 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(