TFLNetForMultipleChoice

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 token_type_ids: np.ndarray | tf.Tensor | None = None input_mask: np.ndarray | tf.Tensor | None = None attention_mask: np.ndarray | tf.Tensor | None = None mems: np.ndarray | tf.Tensor | None = None perm_mask: np.ndarray | tf.Tensor | None = None target_mapping: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None use_mems: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool]


TFLNetForMultipleChoice

 = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: bool = False  ) → transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput or tuple(tf.Tensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, num_choices, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.FloatTensor of shape (batch_size, num_choices, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   mems (List[torch.FloatTensor] of length config.n_layers) —
Contains pre-computed hidden-states (see mems output below) . Can be used to speed up sequential
decoding. The tok


TFLNetForMultipleChoice

en ids which have their past given to this model should not be passed as input_ids as
they have already been computed.
use_mems has to be set to True to make use of mems.   perm_mask (torch.FloatTensor of shape (batch_size, sequence_length, sequence_length), optional) —
Mask to indicate the attention pattern for each input token with values selected in [0, 1]:
if perm_mask[k, i, j] = 0, i attend to j in batch k;
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
If not set, each token attends to all the others (full bidirectional attention). Only used during
pretraining (to define factorization order) or for sequential decoding (generation).   target_mapping (torch.FloatTensor of shape (batch_size, num_predict, sequence_length), optional) —
Mask to indicate the output tokens to use. If target_mapping[k, i, j] = 1, the i-th predict in batch k is
on the j-th token. Only used during pretraining for partial prediction or for sequential deco


TFLNetForMultipleChoice

ding
(generation).   token_type_ids (torch.LongTensor of shape (batch_size, num_choices, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
What are token type IDs?   input_mask (torch.FloatTensor of shape batch_size, num_choices, sequence_length, optional) —
Mask to avoid performing attention on padding token indices. Negative of attention_mask, i.e. with 0 for
real tokens and 1 for padding which is kept for compatibility with the original code base.
Mask values selected in [0, 1]:
1 for tokens that are masked,
0 for tokens that are not masked.
You can only uses one of input_mask and attention_mask.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indi


TFLNetForMultipleChoice

cates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (tf.Tensor of shape (batch_size,), optional) —
Labels for computing the multiple choice classificat


TFLNetForMultipleChoice

ion loss. Indices should be in [0, ..., num_choices]
where num_choices is the size of the second dimension of the input tensors. (See input_ids above)   Returns
transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput or tuple(tf.Tensor)

A transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForMultipleChoiceOutput 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 (XLNetConfig) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (tf.Tensor of shape (batch_size, num_choices)) — num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
mems (List[tf.Tensor] of length config.n_layers) — Contains pre-computed hidden-states. Can be used (see mems input) to speed up sequential decoding. The
token ids which have


TFLNetForMultipleChoice

 their past given to this model should not be passed as input_ids as they have
already been computed.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The TFXLNetForMultipleChoice forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be


TFLNetForMultipleChoice

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, TFXLNetForMultipleChoice
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
>>> model = TFXLNetForMultipleChoice.from_pretrained("xlnet-base-cased")
>>> 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."
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs)  # batch size is 1
>>> # the linear classifier still needs to be trained
>>> logits = outputs.l


TFXLNetForTokenClassification

class transformers.TFXLNetForTokenClassification  < source > ( *args **kwargs  )   Parameters   config (XLNetConfig) — 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.    XLNet 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. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior. TensorFlow models and layers in transformers


TFXLNetForTokenClassification

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 mode


TFXLNetForTokenClassification

l([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 mems: np.ndarray | tf.Tensor | None = None perm_mask: np.ndarray | tf.Tensor | None = None target_mapping: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None input_mask: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None use_mems: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bo


TFXLNetForTokenClassification

ol] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: bool = False  ) → transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput or tuple(tf.Tensor)  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?   mems (List[torch.FloatTensor] of length config.n_layers) —
Contains pre-computed hidden-states (see mems output below) . Can be used to speed up sequential
decoding. The token ids which have


TFXLNetForTokenClassification

their past given to this model should not be passed as input_ids as
they have already been computed.
use_mems has to be set to True to make use of mems.   perm_mask (torch.FloatTensor of shape (batch_size, sequence_length, sequence_length), optional) —
Mask to indicate the attention pattern for each input token with values selected in [0, 1]:
if perm_mask[k, i, j] = 0, i attend to j in batch k;
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
If not set, each token attends to all the others (full bidirectional attention). Only used during
pretraining (to define factorization order) or for sequential decoding (generation).   target_mapping (torch.FloatTensor of shape (batch_size, num_predict, sequence_length), optional) —
Mask to indicate the output tokens to use. If target_mapping[k, i, j] = 1, the i-th predict in batch k is
on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
(generation).


TFXLNetForTokenClassification

   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?   input_mask (torch.FloatTensor of shape batch_size, sequence_length, optional) —
Mask to avoid performing attention on padding token indices. Negative of attention_mask, i.e. with 0 for
real tokens and 1 for padding which is kept for compatibility with the original code base.
Mask values selected in [0, 1]:
1 for tokens that are masked,
0 for tokens that are not masked.
You can only uses one of input_mask and attention_mask.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates th


TFXLNetForTokenClassification

e head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (tf.Tensor of shape (batch_size, sequence_length), optional) —
Labels for computing the token classification loss. Indices should be in [0, ..., config.num_


TFXLNetForTokenClassification

labels - 1].   Returns
transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput or tuple(tf.Tensor)

A transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForTokenClassificationOutput 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 (XLNetConfig) and inputs.
loss (tf.Tensor of shape (1,), optional, returned when labels is provided)  — Classification loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) — Classification scores (before SoftMax).
mems (List[tf.Tensor] of length config.n_layers) — Contains pre-computed hidden-states. Can be used (see mems input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as input_ids as they have
already been computed.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True


TFXLNetForTokenClassification

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


TFXLNetForTokenClassification

er silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, TFXLNetForTokenClassification
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
>>> model = TFXLNetForTokenClassification.from_pretrained("xlnet-base-cased")
>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )
>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-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] for t in predicted_token_class_ids[0].numpy().tolist()]    Copied >>> labels = predicted_token_class_ids
>>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss


TFXLNetForQuestionAnsweringSimple

class transformers.TFXLNetForQuestionAnsweringSimple  < source > ( *args **kwargs  )   Parameters   config (XLNetConfig) — 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.    XLNet 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). This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related


TFXLNetForQuestionAnsweringSimple

to general 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


TFXLNetForQuestionAnsweringSimple

 input Tensors IN THE ORDER given in the docstring:
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 mems: np.ndarray | tf.Tensor | None = None perm_mask: np.ndarray | tf.Tensor | None = None target_mapping: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None input_mask: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None =


TFXLNetForQuestionAnsweringSimple

 None use_mems: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None start_positions: np.ndarray | tf.Tensor | None = None end_positions: np.ndarray | tf.Tensor | None = None training: bool = False  ) → transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput or tuple(tf.Tensor)  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


TFXLNetForQuestionAnsweringSimple

?   mems (List[torch.FloatTensor] of length config.n_layers) —
Contains pre-computed hidden-states (see mems output below) . Can be used to speed up sequential
decoding. The token ids which have their past given to this model should not be passed as input_ids as
they have already been computed.
use_mems has to be set to True to make use of mems.   perm_mask (torch.FloatTensor of shape (batch_size, sequence_length, sequence_length), optional) —
Mask to indicate the attention pattern for each input token with values selected in [0, 1]:
if perm_mask[k, i, j] = 0, i attend to j in batch k;
if perm_mask[k, i, j] = 1, i does not attend to j in batch k.
If not set, each token attends to all the others (full bidirectional attention). Only used during
pretraining (to define factorization order) or for sequential decoding (generation).   target_mapping (torch.FloatTensor of shape (batch_size, num_predict, sequence_length), optional) —
Mask


TFXLNetForQuestionAnsweringSimple

 to indicate the output tokens to use. If target_mapping[k, i, j] = 1, the i-th predict in batch k is
on the j-th token. Only used during pretraining for partial prediction or for sequential decoding
(generation).   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?   input_mask (torch.FloatTensor of shape batch_size, sequence_length, optional) —
Mask to avoid performing attention on padding token indices. Negative of attention_mask, i.e. with 0 for
real tokens and 1 for padding which is kept for compatibility with the original code base.
Mask values selected in [0, 1]:
1 for tokens that are masked,
0 for tokens that are not masked.
You can only uses one of input_mask and attention_mask.   head


TFXLNetForQuestionAnsweringSimple

_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict


TFXLNetForQuestionAnsweringSimple

(bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   start_positions (tf.Tensor 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 (tf.Tensor 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.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput or tuple(tf.Tensor)

A transformers.models.xlnet.modeling_tf_xlnet.TFXLNetForQuestionAnsweringSimpleOutput or a


TFXLNetForQuestionAnsweringSimple

tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (XLNetConfig) and inputs.
loss (tf.Tensor 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 (tf.Tensor of shape (batch_size, sequence_length,)) — Span-start scores (before SoftMax).
end_logits (tf.Tensor of shape (batch_size, sequence_length,)) — Span-end scores (before SoftMax).
mems (List[tf.Tensor] of length config.n_layers) — Contains pre-computed hidden-states. Can be used (see mems input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as input_ids as they have
already been computed.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states


TFXLNetForQuestionAnsweringSimple

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


TFXLNetForQuestionAnsweringSimple

m.  Example:   Copied >>> from transformers import AutoTokenizer, TFXLNetForQuestionAnsweringSimple
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
>>> model = TFXLNetForQuestionAnsweringSimple.from_pretrained("xlnet-base-cased")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)
>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]    Copied >>> # target is "nice puppet"
>>> target_start_index = tf.constant([14])
>>> target_end_index = tf.constant([15])
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = tf.math.reduce_mean(outp


Overview

The ViTDet model was proposed in Exploring Plain Vision Transformer Backbones for Object Detection by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He.
VitDet leverages the plain Vision Transformer for the task of object detection.
The abstract from the paper is the following:
We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to


Overview

 build a simple feature pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient to use window attention (without shifting) aided with very few cross-window propagation blocks. With plain ViT backbones pre-trained as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the previous leading methods that were all based on hierarchical backbones, reaching up to 61.3 AP_box on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors.
Tips:
For the moment, only the backbone is available.
This model was contributed by nielsr.
The original code can be found here


VitDetConfig

class transformers.VitDetConfig  < source > ( hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 mlp_ratio = 4 hidden_act = 'gelu' dropout_prob = 0.0 initializer_range = 0.02 layer_norm_eps = 1e-06 image_size = 224 pretrain_image_size = 224 patch_size = 16 num_channels = 3 qkv_bias = True drop_path_rate = 0.0 window_block_indices = [] residual_block_indices = [] use_absolute_position_embeddings = True use_relative_position_embeddings = False window_size = 0 out_features = None out_indices = None **kwargs  )   Parameters   hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.   num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.   num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention la


VitDetConfig

yer in the Transformer encoder.   mlp_ratio (int, optional, defaults to 4) —
Ratio of mlp hidden dim to embedding dim.   hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu" and "gelu_new" are supported.   dropout_prob (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.   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-06) —
The epsilon used by the layer normalization layers.   image_size (int, optional, defaults to 224) —
The size (resolution) of each image.   pretrain_image_size (int, option


VitDetConfig

al, defaults to 224) —
The size (resolution) of each image during pretraining.   patch_size (int, optional, defaults to 16) —
The size (resolution) of each patch.   num_channels (int, optional, defaults to 3) —
The number of input channels.   qkv_bias (bool, optional, defaults to True) —
Whether to add a bias to the queries, keys and values.   drop_path_rate (float, optional, defaults to 0.0) —
Stochastic depth rate.   window_block_indices (List[int], optional, defaults to []) —
List of indices of blocks that should have window attention instead of regular global self-attention.   residual_block_indices (List[int], optional, defaults to []) —
List of indices of blocks that should have an extra residual block after the MLP.   use_absolute_position_embeddings (bool, optional, defaults to True) —
Whether to add absolute position embeddings to the pa


VitDetConfig

tch embeddings.   use_relative_position_embeddings (bool, optional, defaults to False) —
Whether to add relative position embeddings to the attention maps.   window_size (int, optional, defaults to 0) —
The size of the attention window.   out_features (List[str], optional) —
If used as backbone, list of features to output. Can be any of "stem", "stage1", "stage2", etc.
(depending on how many stages the model has). If unset and out_indices is set, will default to the
corresponding stages. If unset and out_indices is unset, will default to the last stage.   out_indices (List[int], optional) —
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and out_features is set, will default to the corresponding stages.
If unset and out_features is unset, will default t


VitDetConfig

o the last stage.    This is the configuration class to store the configuration of a VitDetModel. It is used to instantiate an
VitDet 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 VitDet
google/vitdet-base-patch16-224 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 VitDetConfig, VitDetModel
>>> # Initializing a VitDet configuration
>>> configuration = VitDetConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = VitDetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.conf


VitDetModel

class transformers.VitDetModel  < source > ( config: VitDetConfig  )   Parameters   config (VitDetConfig) — 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 VitDet Transformer model outputting raw hidden-states without any specific head on top.
This model is a PyTorch torch.nn.Module subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.  forward  < source > ( pixel_values: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.BaseModelOutput or tuple(torch.Float


VitDetModel

Tensor)  Parameters   pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See ViTImageProcessor.call()
for details.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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 t


VitDetModel

uple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutput 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 (VitDetConfig) 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 w


VitDetModel

hen 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 VitDetModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Examples:   Copied >>> from transformers import VitDetConfig, VitDetModel
>>> import torch
>>> config = VitDetConfig()
>>> model = VitDetModel(config)
>>> pixel_values = torch.randn(1, 3, 224, 224)
>>> with torch.no_grad():
...     outputs = model(pixel_values)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 768, 14, 14


Overview

The LUKE model was proposed in LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto.
It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attention mechanism, which helps
improve performance on various downstream tasks involving reasoning about entities such as named entity recognition,
extractive and cloze-style question answering, entity typing, and relation classification.
The abstract from the paper is the following:
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new
pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed
model treats words and entities in a given text as independent tokens, and outputs contextualized representations of
them. Our model is trained using a new pretraining task based on the masked l


Overview

anguage model of BERT. The task involves
predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also
propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the
transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model
achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains
state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question
answering).
Tips:
This implementation is the same as RobertaModel with the addition of entity embeddings as well
as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities. LUKE treats entities as input


Overview

 tokens; therefore, it takes entity_ids, entity_attention_mask,
entity_token_type_ids and entity_position_ids as extra input. You can obtain those using
LukeTokenizer. LukeTokenizer takes entities and entity_spans (character-based start and end
positions of the entities in the input text) as extra input. entities typically consist of [MASK] entities or
Wikipedia entities. The brief description when inputting these entities are as follows: Inputting [MASK] entities to compute entity representations: The [MASK] entity is used to mask entities to be
predicted during pretraining. When LUKE receives the [MASK] entity, it tries to predict the original entity by
gathering the information about the entity from the input text. Therefore, the [MASK] entity can be used to address
downstream tasks requiring the information of entities in text such as entity typing, relation classification, and
named entity recognition. Inputting Wikipedia entities to compute knowledge-enhanced toke


Overview

n representations: LUKE learns rich information
(or knowledge) about Wikipedia entities during pretraining and stores the information in its entity embedding. By
using Wikipedia entities as input tokens, LUKE outputs token representations enriched by the information stored in
the embeddings of these entities. This is particularly effective for tasks requiring real-world knowledge, such as
question answering. There are three head models for the former use case: LukeForEntityClassification, for tasks to classify a single entity in an input text such as
entity typing, e.g. the Open Entity dataset.
This model places a linear head on top of the output entity representation. LukeForEntityPairClassification, for tasks to classify the relationship between two entities
such as relation classification, e.g. the TACRED dataset. This
model places a linear head on top of the concatenated output representation of the pair of given entities. LukeForEntitySpanClassification, for tasks


Overview

to classify the sequence of entity spans, such as
named entity recognition (NER). This model places a linear head on top of the output entity representations. You
can address NER using this model by inputting all possible entity spans in the text to the model. LukeTokenizer has a task argument, which enables you to easily create an input to these
head models by specifying task="entity_classification", task="entity_pair_classification", or
task="entity_span_classification". Please refer to the example code of each head models. A demo notebook on how to fine-tune LukeForEntityPairClassification for relation
classification can be found here. There are also 3 notebooks available, which showcase how you can reproduce the results as reported in the paper with
the HuggingFace implementation of LUKE. They can be found here.
Example:
  Copied >>> from transformers import LukeTokenizer, LukeModel, LukeForEntityPairClassification
>>> model = LukeModel.from_pretrained("studio-ousia


Overview

/luke-base")
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
# Example 1: Computing the contextualized entity representation corresponding to the entity mention "Beyoncé"
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)]  # character-based entity span corresponding to "Beyoncé"
>>> inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Example 2: Inputting Wikipedia entities to obtain enriched contextualized representations
>>> entities = [
...     "Beyoncé",
...     "Los Angeles",
... ]  # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
>>> entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entities=entities,


Overview

 entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**inputs)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Example 3: Classifying the relationship between two entities using LukeForEntityPairClassification head model
>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = int(logits[0].argmax())
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
This model was contributed by ikuyamada and nielsr. The original code can be found


Documentation resources

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


LukeConfig

class transformers.LukeConfig  < source > ( vocab_size = 50267 entity_vocab_size = 500000 hidden_size = 768 entity_emb_size = 256 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 2 initializer_range = 0.02 layer_norm_eps = 1e-12 use_entity_aware_attention = True classifier_dropout = None pad_token_id = 1 bos_token_id = 0 eos_token_id = 2 **kwargs  )   Parameters   vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the LUKE model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling LukeModel.   entity_vocab_size (int, optional, defaults to 500000) —
Entity vocabulary size of the LUKE model. Defines the number of different entities that can be represented
by the entity_ids passed when calling LukeModel.   hidde


LukeConfig

n_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.   entity_emb_size (int, optional, defaults to 256) —
The number of dimensions of the entity embedding.   num_hidden_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.   num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.   intermediate_size (int, optional, defaults to 3072) —
Dimensionality of the “intermediate” (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" 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


LukeConfig

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 2) —
The vocabulary size of the token_type_ids passed when calling LukeModel.   initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.   layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.   use_entity_aware_attention (bool, defaults to True) —
Whether or not the model should use the entity-aware self-attention mechanism proposed in LUKE: Deep
Contextualized Entity Representation


LukeConfig

s with Entity-aware Self-attention (Yamada et
al.).   classifier_dropout (float, optional) —
The dropout ratio for the classification head.    This is the configuration class to store the configuration of a LukeModel. It is used to instantiate a LUKE
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 LUKE
studio-ousia/luke-base architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.  Examples:   Copied >>> from transformers import LukeConfig, LukeModel
>>> # Initializing a LUKE configuration
>>> configuration = LukeConfig()
>>> # Initializing a model from the configuration
>>> model = LukeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config


LukeTokenizer

class transformers.LukeTokenizer  < source > ( vocab_file merges_file entity_vocab_file task = None max_entity_length = 32 max_mention_length = 30 entity_token_1 = '<ent>' entity_token_2 = '<ent2>' entity_unk_token = '[UNK]' entity_pad_token = '[PAD]' entity_mask_token = '[MASK]' entity_mask2_token = '[MASK2]' errors = 'replace' bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' add_prefix_space = False **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   entity_vocab_file (str) —
Path to the entity vocabulary file.   task (str, optional) —
Task for which you want to prepare sequences. One of "entity_classification",
"entity_pair_classification", or "entity_span_classification". If you specify this argument, the entity
sequence is automatically created based on the given entity span(s).


LukeTokenizer

 max_entity_length (int, optional, defaults to 32) —
The maximum length of entity_ids.   max_mention_length (int, optional, defaults to 30) —
The maximum number of tokens inside an entity span.   entity_token_1 (str, optional, defaults to <ent>) —
The special token used to represent an entity span in a word token sequence. This token is only used when
task is set to "entity_classification" or "entity_pair_classification".   entity_token_2 (str, optional, defaults to <ent2>) —
The special token used to represent an entity span in a word token sequence. This token is only used when
task is set to "entity_pair_classification".   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   bos_token (str, optional, defaults to "<s>") —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence usin


LukeTokenizer

g special tokens, this is not the token that is used for the beginning of
sequence. The token used is the cls_token.
   eos_token (str, optional, defaults to "</s>") —
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.
   sep_token (str, optional, defaults to "</s>") —
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.   cls_token (str, optional, defaults to "<s>") —
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.   unk_token (str, optional, defaults to "<un


LukeTokenizer

k>") —
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.   pad_token (str, optional, defaults to "<pad>") —
The token used for padding, for example when batching sequences of different lengths.   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.   add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (LUKE tokenizer detect beginning of words by the preceding space).    Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will  be encod


LukeTokenizer

ed differently whether it is at the beginning of the sentence (without space) or not:   Copied >>> from transformers import LukeTokenizer
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2] You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. It also creates entity sequences, namely
entity_ids, entity_attention_mask, entity_token_type_ids, and entity_p


LukeTokenizer

osition_ids to be used by the LUKE
model.  __call__  < source > ( text: typing.Union[str, typing.List[str]] text_pair: typing.Union[str, typing.List[str], NoneType] = None entity_spans: typing.Union[typing.List[typing.Tuple[int, int]], typing.List[typing.List[typing.Tuple[int, int]]], NoneType] = None entity_spans_pair: typing.Union[typing.List[typing.Tuple[int, int]], typing.List[typing.List[typing.Tuple[int, int]]], NoneType] = None entities: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] = None entities_pair: typing.Union[typing.List[str], typing.List[typing.List[str]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None max_entity_length: typing.Optional[int] = None stride: int = 0 is_split_into


LukeTokenizer

_words: typing.Optional[bool] = False pad_to_multiple_of: typing.Optional[int] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs  ) → BatchEncoding  Parameters   text (str, List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.   text_pair (str, List[str], List[List[str]]) —
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.   entity_spans (List[Tuple[int, int]], Li


LukeTokenizer

st[List[Tuple[int, int]]], optional) —
The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
with two integers denoting character-based start and end positions of entities. If you specify
"entity_classification" or "entity_pair_classification" as the task argument in the constructor,
the length of each sequence must be 1 or 2, respectively. If you specify entities, the length of each
sequence must be equal to the length of each sequence of entities.   entity_spans_pair (List[Tuple[int, int]], List[List[Tuple[int, int]]], optional) —
The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
with two integers denoting character-based start and end positions of entities. If you specify the
task argument in the constructor, this argument is ignored. If you specify entities_pair, the
length of each sequence must be equal to the length of each sequence


LukeTokenizer

of entities_pair.   entities (List[str], List[List[str]], optional) —
The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the task argument in the constructor. The length of
each sequence must be equal to the length of each sequence of entity_spans. If you specify
entity_spans without specifying this argument, the entity sequence or the batch of entity sequences
is automatically constructed by filling it with the [MASK] entity.   entities_pair (List[str], List[List[str]], optional) —
The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the task argument in t


LukeTokenizer

he constructor. The length of
each sequence must be equal to the length of each sequence of entity_spans_pair. If you specify
entity_spans_pair without specifying this argument, the entity sequence or the batch of entity
sequences is automatically constructed by filling it with the [MASK] entity.   max_entity_length (int, optional) —
The maximum length of entity_ids.   add_special_tokens (bool, optional, defaults to True) —
Whether or not to add special tokens when encoding the sequences. This will use the underlying
PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are
automatically added to the input ids. This is usefull if you want to add bos or eos tokens
automatically.   padding (bool, str or PaddingStrategy, optional, defaults to False) —
Activates and controls padding. Accepts the following values:
True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single
se


LukeTokenizer

quence if provided).
'max_length': Pad to a maximum length specified with the argument max_length or to the maximum
acceptable input length for the model if that argument is not provided.
False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different
lengths).
   truncation (bool, str or TruncationStrategy, optional, defaults to False) —
Activates and controls truncation. Accepts the following values:
True or 'longest_first': Truncate to a maximum length specified with the argument max_length or
to the maximum acceptable input length for the model if that argument is not provided. This will
truncate token by token, removing a token from the longest sequence in the pair if a pair of
sequences (or a batch of pairs) is provided.
'only_first': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will on


LukeTokenizer

ly
truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
'only_second': Truncate to a maximum length specified with the argument max_length or to the
maximum acceptable input length for the model if that argument is not provided. This will only
truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
   max_length (int, optional) —
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to None, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.   stride (int, optional, d


LukeTokenizer

efaults to 0) —
If set to a number along with max_length, the overflowing tokens returned when
return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence
returned to provide some overlap between truncated and overflowing sequences. The value of this
argument defines the number of overlapping tokens.   is_split_into_words (bool, optional, defaults to False) —
Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the
tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
which it will tokenize. This is useful for NER or token classification.   pad_to_multiple_of (int, optional) —
If set will pad the sequence to a multiple of the provided value. Requires padding to be activated.
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
>= 7.5 (Volta).   return_tensors (str or Tenso


LukeTokenizer

rType, optional) —
If set, will return tensors instead of list of python integers. Acceptable values are:
'tf': Return TensorFlow tf.constant objects.
'pt': Return PyTorch torch.Tensor objects.
'np': Return Numpy np.ndarray objects.
   return_token_type_ids (bool, optional) —
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer’s default, defined by the return_outputs attribute.
What are token type IDs?   return_attention_mask (bool, optional) —
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer’s default, defined by the return_outputs attribute.
What are attention masks?   return_overflowing_tokens (bool, optional, defaults to False) —
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with truncation_strategy = longest_f


LukeTokenizer

irst or True, an error is raised instead
of returning overflowing tokens.   return_special_tokens_mask (bool, optional, defaults to False) —
Whether or not to return special tokens mask information.   return_offsets_mapping (bool, optional, defaults to False) —
Whether or not to return (char_start, char_end) for each token.
This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using
Python’s tokenizer, this method will raise NotImplementedError.   return_length  (bool, optional, defaults to False) —
Whether or not to return the lengths of the encoded inputs.   verbose (bool, optional, defaults to True) —
Whether or not to print more information and warnings.
**kwargs — passed to the self.tokenize() method   Returns
BatchEncoding

A BatchEncoding with the following fields:
input_ids — List of token ids to be fed to a model.
What are input IDs?
token_type_ids — List of token type ids to be fed to a model (whe


LukeTokenizer

n return_token_type_ids=True or
if “token_type_ids” is in self.model_input_names).
What are token type IDs?
attention_mask — List of indices specifying which tokens should be attended to by the model (when
return_attention_mask=True or if “attention_mask” is in self.model_input_names).
What are attention masks?
entity_ids — List of entity ids to be fed to a model.
What are input IDs?
entity_position_ids — List of entity positions in the input sequence to be fed to a model.
entity_token_type_ids — List of entity token type ids to be fed to a model (when
return_token_type_ids=True or if “entity_token_type_ids” is in self.model_input_names).
What are token type IDs?
entity_attention_mask — List of indices specifying which entities should be attended to by the model
(when return_attention_mask=True or if “entity_attention_mask” is in self.model_input_names).
What are attention masks?
entity_start_positions — List of the start positions of entiti


LukeTokenizer

es in the word token sequence (when
task="entity_span_classification").
entity_end_positions — List of the end positions of entities in the word token sequence (when
task="entity_span_classification").
overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and
return_overflowing_tokens=True).
num_truncated_tokens — Number of tokens truncated (when a max_length is specified and
return_overflowing_tokens=True).
special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).
length — The length of the inputs (when return_length=True)
  Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences, depending on the task you want to prepare them for.  save_vocabulary  < source > ( save_directory: str filename_prefix: typing.Optional[str


LukeModel

class transformers.LukeModel  < source > ( config: LukeConfig add_pooling_layer: bool = True  )   Parameters   config (LukeConfig) — 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 LUKE model transformer outputting raw hidden-states for both word tokens and entities without any specific head on top. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTens


LukeModel

or] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None entity_ids: typing.Optional[torch.LongTensor] = None entity_attention_mask: typing.Optional[torch.FloatTensor] = None entity_token_type_ids: typing.Optional[torch.LongTensor] = None entity_position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.luke.modeling_luke.BaseLukeModelOutputWithPooling 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 PreTrainedTo


LukeModel

kenizer.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?   entity_ids (torch.LongTensor of shape (batch_size, entity_length)) —
Indices of


LukeModel

 entity tokens in the entity vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.   entity_attention_mask (torch.FloatTensor of shape (batch_size, entity_length), optional) —
Mask to avoid performing attention on padding entity token indices. Mask values selected in [0, 1]:
1 for entity tokens that are not masked,
0 for entity tokens that are masked.
   entity_token_type_ids (torch.LongTensor of shape (batch_size, entity_length), optional) —
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in [0, 1]:
0 corresponds to a portion A entity token,
1 corresponds to a portion B entity token.
   entity_position_ids (torch.LongTensor of shape (batch_size, entity_length, max_mention_length), optional) —
Indices of positions of each input entity in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].


LukeModel

   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.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   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


LukeModel

 or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.models.luke.modeling_luke.BaseLukeModelOutputWithPooling or tuple(torch.FloatTensor)

A transformers.models.luke.modeling_luke.BaseLukeModelOutputWithPooling 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 (LukeConfig) 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.
entity_last_hidden_state (torch.FloatTensor of shape (batch_size, entity_length, hidden_size)) — Sequence of entity hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation f


LukeModel

unction.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size). Hidden-states of the model at the output of each layer
plus the initial embedding outputs.
entity_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, entity_length, hidden_size). Entity hidden-states of the model at the output of each
layer plus the initial entity 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


LukeModel

for each layer) of shape (batch_size, num_heads, sequence_length + entity_length, sequence_length + entity_length). Attentions weights after the attention softmax, used to
compute the weighted average in the self-attention heads.
  The LukeModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Examples:   Copied >>> from transformers import AutoTokenizer, LukeModel
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
# Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)]  # character-based entity span correspondi


LukeModel

ng to "Beyoncé"
>>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Input Wikipedia entities to obtain enriched contextualized representations of word tokens
>>> text = "Beyoncé lives in Los Angeles."
>>> entities = [
...     "Beyoncé",
...     "Los Angeles",
... ]  # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
>>> entity_spans = [
...     (0, 7),
...     (17, 28),
... ]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> encoding = tokenizer(
...     text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
... )
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidd


LukeForMaskedLM

class transformers.LukeForMaskedLM  < source > ( config  )   Parameters   config (LukeConfig) — 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 LUKE model with a language modeling head and entity prediction head on top for masked language modeling and
masked entity prediction. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Op


LukeForMaskedLM

tional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None entity_ids: typing.Optional[torch.LongTensor] = None entity_attention_mask: typing.Optional[torch.LongTensor] = None entity_token_type_ids: typing.Optional[torch.LongTensor] = None entity_position_ids: typing.Optional[torch.LongTensor] = None labels: typing.Optional[torch.LongTensor] = None entity_labels: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.luke.modeling_luke.LukeMaskedLMOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.Long


LukeForMaskedLM

Tensor 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


LukeForMaskedLM

sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?   entity_ids (torch.LongTensor of shape (batch_size, entity_length)) —
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.   entity_attention_mask (torch.FloatTensor of shape (batch_size, entity_length), optional) —
Mask to avoid performing attention on padding entity token indices. Mask values selected in [0, 1]:
1 for entity tokens that are not masked,
0 for entity tokens that are masked.
   entity_token_type_ids (torch.LongTensor of shape (batch_size, entity_length), optional) —
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in [0, 1]:
0 corresponds to a portion A entity token,
1 corresponds to a portion


LukeForMaskedLM

 B entity token.
   entity_position_ids (torch.LongTensor of shape (batch_size, entity_length, max_mention_length), optional) —
Indices of positions of each input entity in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   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.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   output_attentions (bool, optional) —
Whether or not to


LukeForMaskedLM

 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]   entity_labels (torch.LongTensor of shape (batch_size, entity_length), optional) —
Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_


LukeForMaskedLM

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.models.luke.modeling_luke.LukeMaskedLMOutput or tuple(torch.FloatTensor)

A transformers.models.luke.modeling_luke.LukeMaskedLMOutput 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 (LukeConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — The sum of masked language modeling (MLM) loss and entity prediction loss.
mlm_loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.
mep_loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked entity prediction (MEP) loss.
logits (torch.FloatT


LukeForMaskedLM

ensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
entity_logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the entity prediction head (scores for each entity 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 + 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.
entity_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.Floa


LukeForMaskedLM

tTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, entity_length, hidden_size). Entity hidden-states of the model at the output of each
layer plus the initial entity 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 LukeForMaskedLM 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 ignor


LukeForEntityClassification

class transformers.LukeForEntityClassification  < source > ( config  )   Parameters   config (LukeConfig) — 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 LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity
token) for entity classification tasks, such as Open Entity. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavi


LukeForEntityClassification

or.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None entity_ids: typing.Optional[torch.LongTensor] = None entity_attention_mask: typing.Optional[torch.FloatTensor] = None entity_token_type_ids: typing.Optional[torch.LongTensor] = None entity_position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.luke.modeling_luke.EntityClassificationOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.Lon


LukeForEntityClassification

gTensor 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


LukeForEntityClassification

 sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?   entity_ids (torch.LongTensor of shape (batch_size, entity_length)) —
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.   entity_attention_mask (torch.FloatTensor of shape (batch_size, entity_length), optional) —
Mask to avoid performing attention on padding entity token indices. Mask values selected in [0, 1]:
1 for entity tokens that are not masked,
0 for entity tokens that are masked.
   entity_token_type_ids (torch.LongTensor of shape (batch_size, entity_length), optional) —
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in [0, 1]:
0 corresponds to a portion A entity token,
1 corresponds to a portio


LukeForEntityClassification

n B entity token.
   entity_position_ids (torch.LongTensor of shape (batch_size, entity_length, max_mention_length), optional) —
Indices of positions of each input entity in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   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.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   output_attentions (bool, optional) —
Whether or not t


LukeForEntityClassification

o 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,) or (batch_size, num_labels), optional) —
Labels for computing the classification loss. If the shape is (batch_size,), the cross entropy loss is
used for the single-label classification. In this case, labels should contain the indices that should be in
[0, ..., config.num_labels - 1]. If the shape is (batch_size, num_labels), the binary cross entropy
loss is used for the multi-label classification. In this case, labels should only contain [0, 1], where 0
and 1 indicate false and true, respectively.   Retu


LukeForEntityClassification

rns
transformers.models.luke.modeling_luke.EntityClassificationOutput or tuple(torch.FloatTensor)

A transformers.models.luke.modeling_luke.EntityClassificationOutput 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 (LukeConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, 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 + 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


LukeForEntityClassification

the initial embedding outputs.
entity_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, entity_length, hidden_size). Entity hidden-states of the model at the output of each
layer plus the initial entity 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 LukeForEntityClassification forward method, overrides the __call__ special method. Although the recipe for forward


LukeForEntityClassification

pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Examples:   Copied >>> from transformers import AutoTokenizer, LukeForEntityClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)]  # character-based entity span corresponding to "Beyoncé"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: person


LukeForEntityPairClassification

class transformers.LukeForEntityPairClassification  < source > ( config  )   Parameters   config (LukeConfig) — 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 LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity
tokens) for entity pair classification tasks, such as TACRED. This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.


LukeForEntityPairClassification

forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None entity_ids: typing.Optional[torch.LongTensor] = None entity_attention_mask: typing.Optional[torch.FloatTensor] = None entity_token_type_ids: typing.Optional[torch.LongTensor] = None entity_position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.models.luke.modeling_luke.EntityPairClassificationOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of


LukeForEntityPairClassification

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


LukeForEntityPairClassification

the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
What are position IDs?   entity_ids (torch.LongTensor of shape (batch_size, entity_length)) —
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.   entity_attention_mask (torch.FloatTensor of shape (batch_size, entity_length), optional) —
Mask to avoid performing attention on padding entity token indices. Mask values selected in [0, 1]:
1 for entity tokens that are not masked,
0 for entity tokens that are masked.
   entity_token_type_ids (torch.LongTensor of shape (batch_size, entity_length), optional) —
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in [0, 1]:
0 corresponds to a portion A entity token,
1 corresponds to a portion B entity token.
   entity_p


LukeForEntityPairClassification

osition_ids (torch.LongTensor of shape (batch_size, entity_length, max_mention_length), optional) —
Indices of positions of each input entity in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].   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.   head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all


LukeForEntityPairClassification

 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,) or (batch_size, num_labels), optional) —
Labels for computing the classification loss. If the shape is (batch_size,), the cross entropy loss is
used for the single-label classification. In this case, labels should contain the indices that should be in
[0, ..., config.num_labels - 1]. If the shape is (batch_size, num_labels), the binary cross entropy
loss is used for the multi-label classification. In this case, labels should only contain [0, 1], where 0
and 1 indicate false and true, respectively.   Returns
transformers.models.luke.modeling_luke.Ent


LukeForEntityPairClassification

ityPairClassificationOutput or tuple(torch.FloatTensor)

A transformers.models.luke.modeling_luke.EntityPairClassificationOutput 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 (LukeConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification loss.
logits (torch.FloatTensor of shape (batch_size, 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 + 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.
entity_hidden_sta


LukeForEntityPairClassification

tes (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, entity_length, hidden_size). Entity hidden-states of the model at the output of each
layer plus the initial entity 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 LukeForEntityPairClassification forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one sh