TFWav2Vec2ForCTC

token_type_ids]) a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_values": input_values, "token_type_ids": token_type_ids}) Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!  call  < source > ( input_values: tf.Tensor attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None output_attentions: Optional[bool] = None labels: tf.Tensor | None = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: Optional[bool] = False  ) → transformers.modeling_tf_outputs.TFCausalLMOutput or tuple(tf.Tensor)  Parameters   input_values (np.ndarray, tf.Tensor, List[tf.Tensor] Dict[str, tf.Tensor]


TFWav2Vec2ForCTC

 or Dict[str, np.ndarray] and each example must have the shape ({0})) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?   attention_mask (np.ndarray or tf.Tensor of shape ({0}), 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 (np.ndarray or tf.Tensor of shape ({0}), 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 (np.ndarray or tf.Tensor of shape ({0}), optional) —
Indices of positions of each input sequence tokens in the position embeddings. Selected in


TFWav2Vec2ForCTC

 the range [0, config.max_position_embeddings - 1].
What are position IDs?   head_mask (np.ndarray or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (np.ndarray or tf.Tensor of shape ({0}, hidden_size), optional) —
Optionally, instead of passing input_values you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert input_values 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. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.


TFWav2Vec2ForCTC

 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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.   training (bool, optional, defaults to `False“) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).   labels (tf.Tensor or np.ndarray 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_values docstring) Tokens with indices set to -100 are ignored (masked),
the loss is only computed


TFWav2Vec2ForCTC

 for the tokens with labels in [0, ..., config.vocab_size]   Returns
transformers.modeling_tf_outputs.TFCausalLMOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFCausalLMOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (Wav2Vec2Config) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling loss (for next-token prediction).
logits (tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer)


TFWav2Vec2ForCTC

 of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The TFWav2Vec2ForCTC 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 >>> import tensorflow as tf
>>> from transformers import AutoProcessor, TFWav2Vec2ForCTC
>>> from datasets


TFWav2Vec2ForCTC

 import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
>>> model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = tf.argmax(logits, axis=-1)
>>> transcription = processor.decode(predicted_ids[0])
>>> # compute loss
>>> target_transcription = "A MAN SAID TO THE UNIVERSE SIR I EXIST"
>>> # Pass transcription as `text` to encode labels
>>> labels = processor(text=transcription, return_tensors="tf").input_ids
>>> loss = model(input_values, labels=labels).los


FlaxWav2Vec2Model

class transformers.FlaxWav2Vec2Model  < source > ( config: Wav2Vec2Config input_shape: typing.Tuple = (1, 1024) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs  )   Parameters   config (Wav2Vec2Config) — 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.   dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dt


FlaxWav2Vec2Model

ype of the model parameters, see to_fp16() and
to_bf16().    The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.
Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli. This model inherits from FlaxPreTrainedModel. 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 Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: Just-In-Time (JIT) compilation Automatic Differentiation Vectorization Parallelization  __call__  < source > ( input_values attention_mask = None mask_time_indices = None params: dict = None dr


FlaxWav2Vec2Model

opout_rng: PRNGKey = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None freeze_feature_encoder: bool = False return_dict: typing.Optional[bool] = None  ) → transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or tuple(torch.FloatTensor)  Parameters   input_values (jnp.ndarray 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 jnp.ndarray. See Wav2Vec2Processor.call() for details.   attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in


FlaxWav2Vec2Model

 [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks? .. warning:: 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
wav2vec2-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.   mask_time_indices (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
masked extracted features in config.proj_codevector_dim space.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. S


FlaxWav2Vec2Model

ee 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.   Returns
transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput or tuple(torch.FloatTensor)

A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2BaseModelOutput 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 (<class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
extract_features (jnp.ndarray of shape (batch_size, sequence_length, las


FlaxWav2Vec2Model

t_conv_dim)) — Sequence of extracted feature vectors of the last convolutional layer of the model with last_conv_dim
being the dimension of the last convolutional layer.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 FlaxWav2Vec2PreTrainedModel forward method, overrides the __call__ special method. A


FlaxWav2Vec2Model

lthough 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, FlaxWav2Vec2Model
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-lv60")
>>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60")
>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(
...     ds["speech"][0], sampling_rate=16_000, return_tensors="np"
... ).input_values  # Batch size 1
>>> hidden_states = model(input_values).last_hidden_


FlaxWav2Vec2ForCTC

class transformers.FlaxWav2Vec2ForCTC  < source > ( config: Wav2Vec2Config input_shape: typing.Tuple = (1, 1024) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs  )   Parameters   config (Wav2Vec2Config) — 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.   dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish


FlaxWav2Vec2ForCTC

to change the dtype of the model parameters, see to_fp16() and
to_bf16().    Wav2Vec2 Model with a language modeling head on top for Connectionist Temporal Classification (CTC).
Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli. This model inherits from FlaxPreTrainedModel. 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 Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: Just-In-Time (JIT) compilation Automatic Differentiation Vectorization Parallelization  __call__  < source > ( input_values attention_mask = None mask_time_indi


FlaxWav2Vec2ForCTC

ces = None params: dict = None dropout_rng: PRNGKey = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None freeze_feature_encoder: bool = False return_dict: typing.Optional[bool] = None  ) → transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)  Parameters   input_values (jnp.ndarray 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 jnp.ndarray. See Wav2Vec2Processor.call() for details.   attention_mask (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Mask to avoid performing convolution and attention on padding token indices. M


FlaxWav2Vec2ForCTC

ask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks? .. warning:: 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
wav2vec2-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.   mask_time_indices (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
masked extracted features in config.proj_codevector_dim space.   output_attentions (bool, optional) —
Whether or not to return the attentio


FlaxWav2Vec2ForCTC

ns 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.   Returns
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_flax_outputs.FlaxMaskedLMOutput 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 (<class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>) and inputs.
logits (jnp.ndarray 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(jnp.ndarray), optional, retur


FlaxWav2Vec2ForCTC

ned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 FlaxWav2Vec2PreTrainedModel 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 processi


FlaxWav2Vec2ForCTC

ng steps while
the latter silently ignores them.  Example:   Copied >>> import jax.numpy as jnp
>>> from transformers import AutoProcessor, FlaxWav2Vec2ForCTC
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-960h-lv60")
>>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60")
>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = processor(
...     ds["speech"][0], sampling_rate=16_000, return_tensors="np"
... ).input_values  # Batch size 1
>>> logits = model(input_values).logits
>>> predicted_ids = jnp.argmax(logits, axis=-1)
>>> transcription = processor.decode(predicted_ids[0])
>>> # should give:  "A MAN SAID TO THE UNIVERSE SIR I EXIST


class transformers.FlaxWav2Vec2ForPreTraining  < source > ( config: Wav2Vec2Config input_shape: typing.Tuple = (1, 1024) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True **kwargs  )   Parameters   config (Wav2Vec2Config) — 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.   dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtyp


e of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().    Wav2Vec2 Model with a quantizer and VQ head on top.
Wav2Vec2 was proposed in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech
Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael
Auli. This model inherits from FlaxPreTrainedModel. 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 Flax Linen
flax.nn.Module subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: Just-In-Time (JIT) compilation Automatic Di


fferentiation Vectorization Parallelization  __call__  < source > ( input_values attention_mask = None mask_time_indices = None gumbel_temperature: int = 1 params: dict = None dropout_rng: PRNGKey = None gumbel_rng: PRNGKey = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None freeze_feature_encoder: bool = False return_dict: typing.Optional[bool] = None  ) → transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor)  Parameters   input_values (jnp.ndarray 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 AutoProcess


or should be used for padding and
conversion into a tensor of type jnp.ndarray. See Wav2Vec2Processor.call() for details.   attention_mask (jnp.ndarray 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? .. warning:: 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
wav2vec2-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 whethe


r input_values is padded or not.   mask_time_indices (jnp.ndarray of shape (batch_size, sequence_length), optional) —
Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
masked extracted features in config.proj_codevector_dim space.   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.   Returns
transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2Vec2ForPreTrainingOutput or tuple(torch.FloatTensor)

A transformers.models.wav2vec2.modeling_flax_wav2vec2.FlaxWav2V


ec2ForPreTrainingOutput 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 (<class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>) and inputs.
loss (optional, returned when model is in train mode, jnp.ndarray of shape (1,)) — Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the official
paper . (classification) loss.
projected_states (jnp.ndarray of shape (batch_size, sequence_length, config.proj_codevector_dim)) — Hidden-states of the model projected to config.proj_codevector_dim that can be used to predict the masked
projected quantized states.
projected_quantized_states (jnp.ndarray of shape (batch_size, sequence_length, config.proj_codevector_dim)) — Quantized extracted feature vectors projected to config.proj_


codevector_dim representing the positive
target vectors for contrastive loss.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 FlaxWav2Vec2ForPreTraining 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 >>> import optax
>>> import numpy as np
>>> import jax.numpy as jnp
>>> from transformers import AutoFeatureExtractor, FlaxWav2Vec2ForPreTraining
>>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices
>>> from datasets import load_dataset
>>> import soundfile as sf
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60")
>>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60")
>>> def map_to_array(batch):
...     speech, _ = sf.read(batch["file"])
...     batch["speech"] = speech
...     return batch
>>> ds = load_dat


aset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> ds = ds.map(map_to_array)
>>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1
>>> # compute masked indices
>>> batch_size, raw_sequence_length = input_values.shape
>>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
>>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)
>>> outputs = model(input_values, mask_time_indices=mask_time_indices)
>>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
>>> cosine_sim = optax.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states)
>>> # show that cosine similarity is much higher than random
>>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.


Overview

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s
BERT model released in 2018 and Facebook’s RoBERTa model released in 2019.
It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in
RoBERTa.
The abstract from the paper is the following:
Recent progress in pre-trained neural language models has significantly improved the performance of many natural
language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with
disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the
disentangled attention mechanism, where each word is represented using two vectors that encode its content and
position, respectively, and the attention weights among words are computed using disentangled matrices on their
conten


Overview

ts and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
(90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.
The following information is visible directly on the original implementation
repository. DeBERTa v2 is the second version of the DeBERTa model. It includes
the 1.5B model used for the SuperGLUE single-model submission and achieving 89.9, versus human baseline 89.8. You can
find more details about this submission in t


Overview

he authors’
blog
New in v2:
Vocabulary In v2 the tokenizer is changed to use a new vocabulary of size 128K built from the training data.
Instead of a GPT2-based tokenizer, the tokenizer is now
sentencepiece-based tokenizer. nGiE(nGram Induced Input Encoding) The DeBERTa-v2 model uses an additional convolution layer aside with the first
transformer layer to better learn the local dependency of input tokens. Sharing position projection matrix with content projection matrix in attention layer Based on previous
experiments, this can save parameters without affecting the performance. Apply bucket to encode relative positions The DeBERTa-v2 model uses log bucket to encode relative positions
similar to T5. 900M model & 1.5B model Two additional model sizes are available: 900M and 1.5B, which significantly improves the
performance of downstream tasks.
This model was contributed by DeBERTa. This model TF 2.0 implementation was
contributed by kamalkraj. The original code can be found her


Documentation resources

Text classification task guide Token classification task guide Question answering task guide Masked language modeling task guide Multiple choice task guide


DebertaV2Config

class transformers.DebertaV2Config  < source > ( vocab_size = 128100 hidden_size = 1536 num_hidden_layers = 24 num_attention_heads = 24 intermediate_size = 6144 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 0 initializer_range = 0.02 layer_norm_eps = 1e-07 relative_attention = False max_relative_positions = -1 pad_token_id = 0 position_biased_input = True pos_att_type = None pooler_dropout = 0 pooler_hidden_act = 'gelu' **kwargs  )   Parameters   vocab_size (int, optional, defaults to 128100) —
Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by
the inputs_ids passed when calling DebertaV2Model.   hidden_size (int, optional, defaults to 1536) —
Dimensionality of the encoder


DebertaV2Config

 layers and the pooler layer.   num_hidden_layers (int, optional, defaults to 24) —
Number of hidden layers in the Transformer encoder.   num_attention_heads (int, optional, defaults to 24) —
Number of attention heads for each attention layer in the Transformer encoder.   intermediate_size (int, optional, defaults to 6144) —
Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.   hidden_act (str or Callable, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu", "gelu", "tanh", "gelu_fast", "mish", "linear", "sigmoid" and "gelu_new"
are supported.   hidden_dropout_prob (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embedd


DebertaV2Config

ings, encoder, and pooler.   attention_probs_dropout_prob (float, optional, defaults to 0.1) —
The dropout ratio for the attention 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 0) —
The vocabulary size of the token_type_ids passed when calling DebertaModel or TFDebertaModel.   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-7) —
The epsilon used by the layer normalization layers.   relative_attention (bool, optional, defaults to True) —


DebertaV2Config

Whether use relative position encoding.   max_relative_positions (int, optional, defaults to -1) —
The range of relative positions [-max_position_embeddings, max_position_embeddings]. Use the same value
as max_position_embeddings.   pad_token_id (int, optional, defaults to 0) —
The value used to pad input_ids.   position_biased_input (bool, optional, defaults to False) —
Whether add absolute position embedding to content embedding.   pos_att_type (List[str], optional) —
The type of relative position attention, it can be a combination of ["p2c", "c2p"], e.g. ["p2c"],
["p2c", "c2p"], ["p2c", "c2p"].   layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.    This is the configuration class to store the configuration of a DebertaV2Model. It is used to instantia


DebertaV2Config

te a
DeBERTa-v2 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 DeBERTa
microsoft/deberta-v2-xlarge 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 DebertaV2Config, DebertaV2Model
>>> # Initializing a DeBERTa-v2 microsoft/deberta-v2-xlarge style configuration
>>> configuration = DebertaV2Config()
>>> # Initializing a model (with random weights) from the microsoft/deberta-v2-xlarge style configuration
>>> model = DebertaV2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.co


DebertaV2Tokenizer

class transformers.DebertaV2Tokenizer  < source > ( vocab_file do_lower_case = False split_by_punct = False bos_token = '[CLS]' eos_token = '[SEP]' unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' sp_model_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None **kwargs  )   Parameters   vocab_file (str) —
SentencePiece file (generally has a .spm extension) that
contains the vocabulary necessary to instantiate a tokenizer.   do_lower_case (bool, optional, defaults to False) —
Whether or not to lowercase the input when tokenizing.   bos_token (string, optional, defaults to "[CLS]") —
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that i


DebertaV2Tokenizer

s used for the beginning of
sequence. The token used is the cls_token.   eos_token (string, optional, defaults to "[SEP]") —
The end of sequence token. When building a sequence using special tokens, this is not the token that is
used for the end of sequence. The token used is the sep_token.   unk_token (str, optional, defaults to "[UNK]") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   sep_token (str, optional, defaults to "[SEP]") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.   pad_token (str, optional, defaults to "[PAD]") —
The token


DebertaV2Tokenizer

used for padding, for example when batching sequences of different lengths.   cls_token (str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.   mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.   sp_model_kwargs (dict, optional) —
Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for
SentencePiece can be used, among other things,
to set:
enable_sampling: Enable subword regularization.
nbest_size: Sampling parameters for unigram. I


DebertaV2Tokenizer

nvalid for BPE-Dropout.
nbest_size = {0,1}: No sampling is performed.
nbest_size > 1: samples from the nbest_size results.
nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
    Constructs a DeBERTa-v2 tokenizer. Based on SentencePiece.  build_inputs_with_special_tokens  < source > ( token_ids_0 token_ids_1 = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of input IDs with the appropriate special tokens.
  Build model inputs from a sequ


DebertaV2Tokenizer

ence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A DeBERTa sequence has the following format: single sequence: [CLS] X [SEP] pair of sequences: [CLS] A [SEP] B [SEP]  get_special_tokens_mask  < source > ( token_ids_0 token_ids_1 = None already_has_special_tokens = False  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   already_has_special_tokens (bool, optional, defaults to False) —
Whether or not the token list is already formatted with special tokens for the model.   Returns
List[int]

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
  Retrieves sequence ids from a token list that has no special tokens added. This method is c


DebertaV2Tokenizer

alled when adding
special tokens using the tokenizer prepare_for_model or encode_plus methods.  create_token_type_ids_from_sequences  < source > ( token_ids_0 token_ids_1 = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of token type IDs according to the given sequence(s).
  Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa  sequence pair mask has the following format:   Copied 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence | If token_ids_1 is None, this method only returns the first portion of the mask (0s).  save_vocabulary  < source > ( save_directory: str filename_prefix: typing.Optional[str] = None


class transformers.DebertaV2TokenizerFast  < source > ( vocab_file = None tokenizer_file = None do_lower_case = False split_by_punct = False bos_token = '[CLS]' eos_token = '[SEP]' unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' **kwargs  )   Parameters   vocab_file (str) —
SentencePiece file (generally has a .spm extension) that
contains the vocabulary necessary to instantiate a tokenizer.   do_lower_case (bool, optional, defaults to False) —
Whether or not to lowercase the input when tokenizing.   bos_token (string, optional, defaults to "[CLS]") —
The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The


token used is the cls_token.   eos_token (string, optional, defaults to "[SEP]") —
The end of sequence token. When building a sequence using special tokens, this is not the token that is
used for the end of sequence. The token used is the sep_token.   unk_token (str, optional, defaults to "[UNK]") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   sep_token (str, optional, defaults to "[SEP]") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.   pad_token (str, optional, defaults to "[PAD]") —
The token used for padding, for example when batch


ing sequences of different lengths.   cls_token (str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.   mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.   sp_model_kwargs (dict, optional) —
Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for
SentencePiece can be used, among other things,
to set:
enable_sampling: Enable subword regularization.
nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.
nbest_size = {


0,1}: No sampling is performed.
nbest_size > 1: samples from the nbest_size results.
nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
    Constructs a DeBERTa-v2 fast tokenizer. Based on SentencePiece.  build_inputs_with_special_tokens  < source > ( token_ids_0 token_ids_1 = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of input IDs with the appropriate special tokens.
  Build model inputs from a sequence or a pair of sequence for


sequence classification tasks by concatenating and
adding special tokens. A DeBERTa sequence has the following format: single sequence: [CLS] X [SEP] pair of sequences: [CLS] A [SEP] B [SEP]  create_token_type_ids_from_sequences  < source > ( token_ids_0 token_ids_1 = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of token type IDs according to the given sequence(s).
  Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa  sequence pair mask has the following format:   Copied 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence | If token_ids_1 is None, this method only returns the first portion of the mask (0s)


DebertaV2Model

class transformers.DebertaV2Model  < source > ( config  )   Parameters   config (DebertaV2Config) — 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 DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch M


DebertaV2Model

odule and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: 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.BaseModelOutput 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 (t


DebertaV2Model

orch.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?   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
O


DebertaV2Model

ptionally, 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.   Returns
transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.retur


DebertaV2Model

n_dict=False) comprising various
elements depending on the configuration (DebertaV2Config) 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.
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


DebertaV2Model

 (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 DebertaV2Model 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, DebertaV2Model
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2Model.from_pretrained("microsoft/deberta-v2-xlarge")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_s


class transformers.DebertaV2PreTrainedModel  < source > ( config: PretrainedConfig *inputs **kwargs  )    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.  _forward_unimplemented  < source > ( *input: typing.Any  )    Defines the computation performed at every call. Should be overridden by all subclasses. 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
registered hooks while the latter silently ignores them.


class transformers.DebertaV2ForMaskedLM  < source > ( config  )   Parameters   config (DebertaV2Config) — 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.    DeBERTa Model with a language modeling head on top.
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for al


l matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: 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.MaskedLMOutput 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 o


f 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?   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]   Returns
transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.MaskedLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (DebertaV2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the


 output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The DebertaV2ForMaskedLM 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 processi


ng steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, DebertaV2ForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2ForMaskedLM.from_pretrained("microsoft/deberta-v2-xlarge")
>>> 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=labe


class transformers.DebertaV2ForSequenceClassification  < source > ( config  )   Parameters   config (DebertaV2Config) — 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.    DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. This model is also a PyTorch torch.nn.Module subclass.
Use it as a r


egular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: 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.SequenceClassifierOutput 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?   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing inpu


t_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 (DebertaV2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer


) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The DebertaV2ForSequenceClassification 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, DebertaV2ForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge")
>>> 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 = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge", 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, DebertaV2ForSequenceClassification
>>> tokenizer = AutoTokenize


r.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge", 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 = DebertaV2ForSequenceClassification.from_pretrained(
...     "microsoft/deberta-v2-xlarge", 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.DebertaV2ForTokenClassification  < source > ( config  )   Parameters   config (DebertaV2Config) — 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.    DeBERTa 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. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. 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.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: 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_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See P


reTrainedTokenizer.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 I


Ds?   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 token classificat


ion 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 (DebertaV2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output


of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The DebertaV2ForTokenClassification 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, DebertaV2ForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2ForTokenClassification.from_pretrained("microsoft/deberta-v2-xlarge")
>>> 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.DebertaV2ForQuestionAnswering  < source > ( config  )   Parameters   config (DebertaV2Config) — 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.    DeBERTa 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). The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBER


Ta on a majority of tasks with 80GB pretraining data. 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.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None start_positions: typing.Optional[torch.Tensor] = None end_positions: 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.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (t


orch.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_le


ngth), 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?   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.   start_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.   end_positions (torch.LongTensor of shape (batch_size,), optional) —
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence
are not taken into account for computing the loss.   Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Questi


onAnsweringModelOutput 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 (DebertaV2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each laye


r) 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 DebertaV2ForQuestionAnswering 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:   Co


pied >>> from transformers import AutoTokenizer, DebertaV2ForQuestionAnswering
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2ForQuestionAnswering.from_pretrained("microsoft/deberta-v2-xlarge")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> # target is "nice puppet"
>>> target_start_index = torch.tensor([2])
>>> target_end_index = torch.tensor([9])
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outpu


class transformers.DebertaV2ForMultipleChoice  < source > ( config  )   Parameters   config (DebertaV2Config) — 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.    DeBERTa 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. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. 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.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: 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.MultipleChoiceModelOutput 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?   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 multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choi


ces 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 (DebertaV2Config) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the e


mbeddings, 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 DebertaV2ForMultipleChoice 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 la


tter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, DebertaV2ForMultipleChoice
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
>>> model = DebertaV2ForMultipleChoice.from_pretrained("microsoft/deberta-v2-xlarge")
>>> 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.


TFDebertaV2Model

class transformers.TFDebertaV2Model  < source > ( *args **kwargs  )   Parameters   config (DebertaV2Config) — 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 DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to genera


TFDebertaV2Model

l usage and
behavior. TensorFlow models and layers in transformers accept two formats as input: having all inputs as keyword arguments (like PyTorch models), or having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument: a single Tensor with input_ids only and nothing else: model(input_ids) a list of varying length with one or several input Tensors IN THE ORDER given in the do


TFDebertaV2Model

cstring:
model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids]) a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids}) Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!  call  < source > ( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: Optional[bool] = False  ) → transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)  Parameters   input_ids (np.ndar


TFDebertaV2Model

ray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the 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 (np.ndarray or tf.Tensor 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 (np.ndarray or tf.Tensor 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 (np.ndarray or tf.Tensor of shape (batch_s


TFDebertaV2Model

ize, 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?   inputs_embeds (np.ndarray or tf.Tensor 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 [`~utils.ModelOutput“] instead o


TFDebertaV2Model

f a plain tuple.   Returns
transformers.modeling_tf_outputs.TFBaseModelOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFBaseModelOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (DebertaV2Config) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(tf.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output


TFDebertaV2Model

_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The TFDebertaV2Model 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, TFDebertaV2Model
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
>>> model = TFDebertaV2Model.from_pretrained("kamalkraj/deberta-v2-xlarge")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hidden


class transformers.TFDebertaV2PreTrainedModel  < source > ( *args **kwargs  )    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.  call  < source > ( inputs training = None mask = None  )    Calls the model on new inputs and returns the outputs as tensors. In this case call() just reapplies
all ops in the graph to the new inputs
(e.g. build a new computational graph from the provided inputs). Note: This method should not be called directly. It is only meant to be
overridden when subclassing tf.keras.Model.
To call a model on an input, always use the __call__() method,
i.e. model(inputs), which relies on the underlying call() method.


class transformers.TFDebertaV2ForMaskedLM  < source > ( *args **kwargs  )   Parameters   config (DebertaV2Config) — 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.    DeBERTa Model with a language modeling head on top.
The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled
Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build
on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior. TensorFlow models and laye


rs in transformers accept two formats as input: having all inputs as keyword arguments (like PyTorch models), or having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like model.fit() things should “just work” for you - just
pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second
format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with
the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument: a single Tensor with input_ids only and nothing else: model(input_ids) a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([in


put_ids, attention_mask, token_type_ids]) a dictionary with one or several input Tensors associated to the input names given in the docstring:
model({"input_ids": input_ids, "token_type_ids": token_type_ids}) Note that when creating models and layers with
subclassing then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!  call  < source > ( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False  ) → transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)  Parameters   input_ids (np.ndarray, tf.Tensor, List


[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, 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 (np.ndarray or tf.Tensor 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 (np.ndarray or tf.Tensor 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 (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optio


nal) —
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?   inputs_embeds (np.ndarray or tf.Tensor 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 [`~utils.ModelOutput“] instead of a plain tuple.   labels (tf.Tensor


 or np.ndarray 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]   Returns
transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (DebertaV2Config) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Masked language modeling (MLM) loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before So