d be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
y1) represents the position of the lower right corner.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.   pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for toke


ns that are masked.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are attention masks?   token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are token type IDs?   position_ids (Numpy array or tf.Tensor 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].
Note that sequence_lengt


h = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are position IDs?   head_mask (tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more deta


il.   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_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if
return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the
configuration (LayoutLMv3Config) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple


(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The TFLayoutLMv3ForSequenceClassification forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Modu


le
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 AutoProcessor, TFAutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> import tensorflow as tf
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, words, boxes=boxes, return_tensors="tf")
>>> sequence_label = tf.convert_to_tensor([1])
>>> outputs = model(**encoding, labels=sequence_label)
>>> loss = outputs.loss
>>> logits = output


class transformers.TFLayoutLMv3ForTokenClassification  < source > ( *args **kwargs  )   Parameters   config (LayoutLMv3Config) — 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.    LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g.
for sequence labeling (information extraction) tasks such as FUNSD,
SROIE, CORD and
Kleister-NDA. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0


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


 model(input_ids) a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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: tf.Tensor | None = None bbox: tf.Tensor | None = None attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None labels: tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_stat


es: Optional[bool] = None return_dict: Optional[bool] = None pixel_values: tf.Tensor | None = None training: Optional[bool] = False  ) → transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)  Parameters   input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   bbox (Numpy array or tf.Tensor of shape (batch_size, sequence_length, 4), optional) —
Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-1]. Each bounding box should be a normalized version in (x0, y0, x1, y1)
f


ormat, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
y1) represents the position of the lower right corner.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.   pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
Note that sequence_length = token_seque


nce_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are attention masks?   token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are token type IDs?   position_ids (Numpy array or tf.Tensor 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].
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [C


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


tates 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_labels - 1].   Returns
transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFTokenClassifierOutput 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 (LayoutLMv3Config) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of unmasked labels, returned when labels is provided)  — Classification loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) — Classificatio


n scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
  The TFLayoutLMv3ForTokenClassification forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this fu


nction, 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 AutoProcessor, TFAutoModelForTokenClassification
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7)
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> word_labels = example["ner_tags"]
>>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="tf")
>>> outputs = model(**encoding)
>>> loss = outputs.loss
>>> logits = outputs.logit


class transformers.TFLayoutLMv3ForQuestionAnswering  < source > ( *args **kwargs  )   Parameters   config (LayoutLMv3Config) — 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.    LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as
DocVQA (a linear layer on top of the text part 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 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 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: tf.Tensor | None = None attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None position_ids: tf.Tensor | None = None head_mask: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None start_positions: tf.Tensor | None = None end_positions: tf.Tensor | None = None output_attentio


ns: Optional[bool] = None output_hidden_states: Optional[bool] = None bbox: tf.Tensor | None = None pixel_values: tf.Tensor | None = None return_dict: Optional[bool] = None training: bool = False  ) → transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)  Parameters   input_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   bbox (Numpy array or tf.Tensor of shape (batch_size, sequence_length, 4), optional) —
Bounding boxes of each input sequence tokens. Selected in the range [0, config.max_2d_position_embeddings-


1]. Each bounding box should be a normalized version in (x0, y0, x1, y1)
format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1,
y1) represents the position of the lower right corner.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.   pixel_values (tf.Tensor of shape (batch_size, num_channels, height, width)) —
Batch of document images. Each image is divided into patches of shape (num_channels, config.patch_size, config.patch_size) and the total number of patches (=patch_sequence_length) equals to ((height / config.patch_size) * (width / config.patch_size)).   attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not


masked,
0 for tokens that are masked.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are attention masks?   token_type_ids (Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
Note that sequence_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are token type IDs?   position_ids (Numpy array or tf.Tensor 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].
Note that seque


nce_length = token_sequence_length + patch_sequence_length + 1 where 1 is for [CLS]
token. See pixel_values for patch_sequence_length.
What are position IDs?   head_mask (tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more deta


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


urns
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)

A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput 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 (LayoutLMv3Config) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are 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).
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 ou


tput 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 TFLayoutLMv3ForQuestionAnswering 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 AutoProcessor, TFAutoModelForQuestionAnswering
>>> from datasets import load_dataset
>>> import tensorflow as tf
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base")
>>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
>>> example = dataset[0]
>>> image = example["image"]
>>> question = "what's his name?"
>>> words = example["tokens"]
>>> boxes = example["bboxes"]
>>> encoding = processor(image, question, words, boxes=boxes, return_tensors="tf")
>>> start_positions = tf.convert_to_tensor([1])
>>> end_positions = tf.convert_to_tensor([3])
>>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logi


Overview

The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from Transformers by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders
(one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a
combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked
visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. The pretraining
consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
The abstract from the paper is the following:
Vision-and-language reasoning requires an understanding of visual concepts, language semantics,


Overview

and, most importantly,
the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality
Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we
build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
cross-modality matching, and image question answering. These tasks help


Overview

 in learning both intra-modality and
cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous
best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel
model components and pretraining strategies significantly contribute to our strong results; and also present several
attention visualizations for the different encoders
Tips:
Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial


Overview

 features
will work. Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
cross-modality layer, so they contain information from both modalities. To access a modality that only attends to
itself, select the vision/language hidden states from the first input in the tuple. The bidirectional cross-modality encoder attention only returns attention values when the language modality is used
as the input and the vision modality is used as the context vector. Further, while the cross-modality encoder
contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
both self attention outputs are disregarded.
This model was contributed by eltoto1219. The original code can be found here


LxmertConfig

class transformers.LxmertConfig  < source > ( vocab_size = 30522 hidden_size = 768 num_attention_heads = 12 num_qa_labels = 9500 num_object_labels = 1600 num_attr_labels = 400 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 l_layers = 9 x_layers = 5 r_layers = 5 visual_feat_dim = 2048 visual_pos_dim = 4 visual_loss_normalizer = 6.67 task_matched = True task_mask_lm = True task_obj_predict = True task_qa = True visual_obj_loss = True visual_attr_loss = True visual_feat_loss = True **kwargs  )   Parameters   vocab_size (int, optional, defaults to 30522) —
Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
inputs_ids passed when calling LxmertModel or TFLxmertModel.   hidden_size (int, optional, defaults to 768) —
Dim


LxmertConfig

ensionality of the encoder layers and the pooler layer.   num_attention_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.   num_qa_labels (int, optional, defaults to 9500) —
This represents the total number of different question answering (QA) labels there are. If using more than
one dataset with QA, the user will need to account for the total number of labels that all of the datasets
have in total.   num_object_labels (int, optional, defaults to 1600) —
This represents the total number of semantically unique objects that lxmert will be able to classify a
pooled-object feature as belonging too.   num_attr_labels (int, optional, defaults to 400) —
This represents the total number of semantically unique attributes that lxmert will be able to classify a
pooled-object feature as possessing.   intermediate_size (int, optional, defaults to 3072) —
Dimensionalit


LxmertConfig

y 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 embeddings, 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 into BertModel.   initializer_range (float, optional, defa


LxmertConfig

ults 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.   l_layers (int, optional, defaults to 9) —
Number of hidden layers in the Transformer language encoder.   x_layers (int, optional, defaults to 5) —
Number of hidden layers in the Transformer cross modality encoder.   r_layers (int, optional, defaults to 5) —
Number of hidden layers in the Transformer visual encoder.   visual_feat_dim (int, optional, defaults to 2048) —
This represents the last dimension of the pooled-object features used as input for the model, representing
the size of each object feature itself.   visual_pos_dim (int, optional, defaults to 4) —
This represents the number of spacial features that are mixed into the visual features. The default is set
to 4 because most commonly this will r


LxmertConfig

epresent the location of a bounding box. i.e., (x, y, width, height)   visual_loss_normalizer (float, optional, defaults to 6.67) —
This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
decided to train with multiple vision-based loss objectives.   task_matched (bool, optional, defaults to True) —
This task is used for sentence-image matching. If the sentence correctly describes the image the label will
be 1. If the sentence does not correctly describe the image, the label will be 0.   task_mask_lm (bool, optional, defaults to True) —
Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
objective.   task_obj_predict (bool, optional, defaults to True) —
Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.   task_qa (bool, optional, defaults to True) —
Whether or not to


LxmertConfig

add the question-answering loss to the objective   visual_obj_loss (bool, optional, defaults to True) —
Whether or not to calculate the object-prediction loss objective   visual_attr_loss (bool, optional, defaults to True) —
Whether or not to calculate the attribute-prediction loss objective   visual_feat_loss (bool, optional, defaults to True) —
Whether or not to calculate the feature-regression loss objective    This is the configuration class to store the configuration of a LxmertModel or a TFLxmertModel. It is used
to instantiate a LXMERT 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 Lxmert
unc-nlp/lxmert-base-uncased architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information


LxmertTokenizer

class transformers.LxmertTokenizer  < source > ( vocab_file do_lower_case = True do_basic_tokenize = True never_split = None unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' tokenize_chinese_chars = True strip_accents = None **kwargs  )   Parameters   vocab_file (str) —
File containing the vocabulary.   do_lower_case (bool, optional, defaults to True) —
Whether or not to lowercase the input when tokenizing.   do_basic_tokenize (bool, optional, defaults to True) —
Whether or not to do basic tokenization before WordPiece.   never_split (Iterable, optional) —
Collection of tokens which will never be split during tokenization. Only has an effect when
do_basic_tokenize=True   unk_token (str, optional, defaults to "[UNK]") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   sep_token (str, optiona


LxmertTokenizer

l, defaults to "[SEP]") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.   pad_token (str, optional, defaults to "[PAD]") —
The token used for padding, for example when batching sequences of different lengths.   cls_token (str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.   mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.   tokenize_chinese


LxmertTokenizer

_chars (bool, optional, defaults to True) —
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
issue).   strip_accents (bool, optional) —
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for lowercase (as in the original Lxmert).    Construct a Lxmert tokenizer. Based on WordPiece. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.  build_inputs_with_special_tokens  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of


LxmertTokenizer

 input IDs with the appropriate special tokens.
  Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Lxmert sequence has the following format: single sequence: [CLS] X [SEP] pair of sequences: [CLS] A [SEP] B [SEP]  convert_tokens_to_string  < source > ( tokens  )    Converts a sequence of tokens (string) in a single string.  create_token_type_ids_from_sequences  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of token type IDs according to the given sequence(s).
  Create a mask from the two sequences passed to be used in a sequence-pair classification task. A Lxmert  sequence pair mask has the following


LxmertTokenizer

format:   Copied 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence | If token_ids_1 is None, this method only returns the first portion of the mask (0s).  get_special_tokens_mask  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   already_has_special_tokens (bool, optional, defaults to False) —
Whether or not the token list is already formatted with special tokens for the model.   Returns
List[int]

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
  Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer prepare_for_model met


LxmertTokenizerFast

class transformers.LxmertTokenizerFast  < source > ( vocab_file = None tokenizer_file = None do_lower_case = True unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' tokenize_chinese_chars = True strip_accents = None **kwargs  )   Parameters   vocab_file (str) —
File containing the vocabulary.   do_lower_case (bool, optional, defaults to True) —
Whether or not to lowercase the input when tokenizing.   unk_token (str, optional, defaults to "[UNK]") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   sep_token (str, optional, defaults to "[SEP]") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.   pad_t


LxmertTokenizerFast

oken (str, optional, defaults to "[PAD]") —
The token used for padding, for example when batching sequences of different lengths.   cls_token (str, optional, defaults to "[CLS]") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.   mask_token (str, optional, defaults to "[MASK]") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.   clean_text (bool, optional, defaults to True) —
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.   tokenize_chinese_chars (bool, optional, defaults to True) —
Whether or not to tokenize Chinese characters. This should likely be deactiv


LxmertTokenizerFast

ated for Japanese (see this
issue).   strip_accents (bool, optional) —
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for lowercase (as in the original Lxmert).   wordpieces_prefix (str, optional, defaults to "##") —
The prefix for subwords.    Construct a “fast” Lxmert tokenizer (backed by HuggingFace’s tokenizers library). Based on WordPiece. This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.  build_inputs_with_special_tokens  < source > ( token_ids_0 token_ids_1 = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   Returns
List[int]

List of input IDs with the appropriate special tokens.
  B


LxmertTokenizerFast

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

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


Lxmert specific outputs

class transformers.models.lxmert.modeling_lxmert.LxmertModelOutput  < source > ( language_output: typing.Optional[torch.FloatTensor] = None vision_output: typing.Optional[torch.FloatTensor] = None pooled_output: typing.Optional[torch.FloatTensor] = None language_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None vision_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None language_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None vision_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   language_output (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) —
Sequence of hidden-states at the output of the last layer of the language encoder.   vision_output (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) —
Sequence of hidden-states a


Lxmert specific outputs

t the output of the last layer of the visual encoder.   pooled_output (torch.FloatTensor of shape (batch_size, hidden_size)) —
Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear   language_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).   vision_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).   language_attentions (tuple(torch.Float


Lxmert specific outputs

Tensor), 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.   vision_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.   cross_encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_


Lxmert specific outputs

length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.    Lxmert’s outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the “relation-ship”
encoder”)
 class transformers.models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None prediction_logits: typing.Optional[torch.FloatTensor] = None cross_relationship_score: typing.Optional[torch.FloatTensor] = None question_answering_score: typing.Optional[torch.FloatTensor] = None language_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None vision_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None language_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None vi


Lxmert specific outputs

sion_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) —
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.   prediction_logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).   cross_relationship_score (torch.FloatTensor of shape (batch_size, 2)) —
Prediction scores of the textual matching objective (classification) head (scores of True/False
continuation before SoftMax).   question_answering_score (torch.FloatTensor of shape (batch_size, n_qa_answers)) —
Prediction scores of question answering objective (classification).   language_hidden_states (tup


Lxmert specific outputs

le(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).   vision_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).   language_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-att


Lxmert specific outputs

ention heads.   vision_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.   cross_encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.    Output type of LxmertForPreTraining.
 class transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = Non


Lxmert specific outputs

e question_answering_score: typing.Optional[torch.FloatTensor] = None language_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None vision_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None language_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None vision_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) —
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.k.   question_answering_score (torch.FloatTensor of shape (batch_size, n_qa_answers), optional) —
Prediction scores of question answering objective (classification).   language_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is pass


Lxmert specific outputs

ed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).   vision_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).   language_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.   vision_attentions (tuple(torch.FloatTensor), optional, returned


Lxmert specific outputs

when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.   cross_encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.    Output type of LxmertForQuestionAnswering.
 class transformers.models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput  < source > ( language_output: tf.Tensor | None = None vision_output: tf.Tensor | None = None pooled_output: tf.Tensor | None = None language_hi


Lxmert specific outputs

dden_states: Tuple[tf.Tensor] | None = None vision_hidden_states: Tuple[tf.Tensor] | None = None language_attentions: Tuple[tf.Tensor] | None = None vision_attentions: Tuple[tf.Tensor] | None = None cross_encoder_attentions: Tuple[tf.Tensor] | None = None  )   Parameters   language_output (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) —
Sequence of hidden-states at the output of the last layer of the language encoder.   vision_output (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) —
Sequence of hidden-states at the output of the last layer of the visual encoder.   pooled_output (tf.Tensor of shape (batch_size, hidden_size)) —
Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear   language_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.o


Lxmert specific outputs

utput_hidden_states=True) —
Tuple of tf.Tensor (one for input features + one for the output of each cross-modality layer) of shape
(batch_size, sequence_length, hidden_size).   vision_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 input features + one for the output of each cross-modality layer) of shape
(batch_size, sequence_length, hidden_size).   language_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.   vision_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attenti


Lxmert specific outputs

ons=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.   cross_encoder_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.    Lxmert’s outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the “relation-ship”
encoder”)
 class transformers.models.lxmert.modeling_tf_lxmert.TFLxmertForPreTrainingOutput  < source > ( loss: tf.Tensor |


Lxmert specific outputs

 None = None prediction_logits: tf.Tensor | None = None cross_relationship_score: tf.Tensor | None = None question_answering_score: tf.Tensor | None = None language_hidden_states: Tuple[tf.Tensor] | None = None vision_hidden_states: Tuple[tf.Tensor] | None = None language_attentions: Tuple[tf.Tensor] | None = None vision_attentions: Tuple[tf.Tensor] | None = None cross_encoder_attentions: Tuple[tf.Tensor] | None = None  )   Parameters   loss (optional, returned when labels is provided, tf.Tensor of shape (1,)) —
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.   prediction_logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).   cross_relationship_score (tf.Tensor of shape (batch_size, 2)) —
Prediction scores of the textual matching objective (classificat


Lxmert specific outputs

ion) head (scores of True/False
continuation before SoftMax).   question_answering_score (tf.Tensor of shape (batch_size, n_qa_answers)) —
Prediction scores of question answering objective (classification).   language_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 input features + one for the output of each cross-modality layer) of shape
(batch_size, sequence_length, hidden_size).   vision_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 input features + one for the output of each cross-modality layer) of shape
(batch_size, sequence_length, hidden_size).   language_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (


Lxmert specific outputs

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.   vision_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.   cross_encoder_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.    Output type of LxmertFor


LxmertModel

class transformers.LxmertModel  < source > ( config  )   Parameters   config (LxmertConfig) — 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 Lxmert Model transformer outputting raw hidden-states without any specific head on top. The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from
Transformers by Hao Tan and Mohit Bansal. It’s a vision and language transformer
model, pretrained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MSCOCO captions, and Visual
genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
for question answering attribute prediction, and object tag prediction. This model inherits from PreTrainedModel. Check the superclass documentation for


LxmertModel

the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None visual_feats: typing.Optional[torch.FloatTensor] = None visual_pos: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None visual_attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = 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.lxmert.modeling_lxmert.LxmertModelOutput or tuple(torch.FloatTensor)  Pa


LxmertModel

rameters   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?   visual_feats (torch.FloatTensor of shape (batch_size, num_visual_features, visual_feat_dim)) —
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.   visual_pos (torch.FloatTensor of shape (batch_size, num_visual_features, visual_pos_dim)) —
This input represents spacial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
These are currently not provided by the transformers library.   attention_mask (torch.FloatTensor of shape (batch_size, sequence_l


LxmertModel

ength), 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?   visual_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?   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 usef


LxmertModel

ul if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.models.lxmert.modeling_lxmert.LxmertModelOutput or tuple(torch.FloatTensor)

A transformers.models.lxmert.modeling_lxmert.LxmertModelOutput 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 (LxmertConfig) and inputs.
language_output (torch.FloatTensor of shape (batch_size, sequence_length,


LxmertModel

 hidden_size)) — Sequence of hidden-states at the output of the last layer of the language encoder.
vision_output (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the visual encoder.
pooled_output (torch.FloatTensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear
language_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).
vision_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


LxmertModel

tTensor (one for input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).
language_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.
vision_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is p


LxmertModel

assed 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 LxmertModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, LxmertModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("unc-nlp/lxmert-base-uncased")
>>> model = LxmertModel.from_pretrained("unc-nlp/lxmert-base-uncased")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidde


LxmertForPreTraining

class transformers.LxmertForPreTraining  < source > ( config  )   Parameters   config (LxmertConfig) — 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.    Lxmert Model with a specified pretraining head on top. The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from
Transformers by Hao Tan and Mohit Bansal. It’s a vision and language transformer
model, pretrained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MSCOCO captions, and Visual
genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
for question answering attribute prediction, and object tag prediction. This model inherits from PreTrainedM


LxmertForPreTraining

odel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None visual_feats: typing.Optional[torch.FloatTensor] = None visual_pos: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None visual_attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None obj_labels: typing.Union[typing.Dict[str, typing.Tuple[torch.Float


LxmertForPreTraining

Tensor, torch.FloatTensor]], NoneType] = None matched_label: typing.Optional[torch.LongTensor] = None ans: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs  ) → transformers.models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   visual_feats (torch.FloatTensor of shape (batch_size, num_visual_features, visual_feat_dim)) —
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently


LxmertForPreTraining

 not provided by the transformers library.   visual_pos (torch.FloatTensor of shape (batch_size, num_visual_features, visual_pos_dim)) —
This input represents spacial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
These are currently not provided by the transformers library.   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?   visual_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


LxmertForPreTraining

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?   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) —
Whe


LxmertForPreTraining

ther 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]   obj_labels (Dict[Str -- Tuple[Torch.FloatTensor, Torch.FloatTensor]], optional):
each key is named after each one of the visual losses and each element of the tuple is of the shape
(batch_size, num_features) and (batch_size, num_features, visual_feature_dim) for each the label id and
the label score respectively   matched_label (torch.LongT


LxmertForPreTraining

ensor of shape (batch_size,), optional) —
Labels for computing the whether or not the text input matches the image (classification) loss. Input
should be a sequence pair (see input_ids docstring) Indices should be in [0, 1]:
0 indicates that the sentence does not match the image,
1 indicates that the sentence does match the image.
   ans (Torch.Tensor of shape (batch_size), optional) —
a one hot representation hof the correct answer optional   Returns
transformers.models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput or tuple(torch.FloatTensor)

A transformers.models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput 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 (LxmertConfig) and inputs.
loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) — Total loss


LxmertForPreTraining

 as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
cross_relationship_score (torch.FloatTensor of shape (batch_size, 2)) — Prediction scores of the textual matching objective (classification) head (scores of True/False
continuation before SoftMax).
question_answering_score (torch.FloatTensor of shape (batch_size, n_qa_answers)) — Prediction scores of question answering objective (classification).
language_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 input features + one for the output of each cross-modality layer) of


LxmertForPreTraining

shape (batch_size, sequence_length, hidden_size).
vision_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).
language_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.
vision_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one f


LxmertForPreTraining

or each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
  The LxmertForPreTraining 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 t


LxmertForQuestionAnswering

class transformers.LxmertForQuestionAnswering  < source > ( config  )   Parameters   config (LxmertConfig) — 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.    Lxmert Model with a visual-answering head on top for downstream QA tasks The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from
Transformers by Hao Tan and Mohit Bansal. It’s a vision and language transformer
model, pretrained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MSCOCO captions, and Visual
genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
for question answering attribute prediction, and object tag prediction. This model inherits from PreTrainedM


LxmertForQuestionAnswering

odel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None visual_feats: typing.Optional[torch.FloatTensor] = None visual_pos: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None visual_attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] =


LxmertForQuestionAnswering

None return_dict: typing.Optional[bool] = None  ) → transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   visual_feats (torch.FloatTensor of shape (batch_size, num_visual_features, visual_feat_dim)) —
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.   visual_pos (torch.FloatTensor of shape (batch_size, num_visual_features, visual_pos_dim)) —
This input represents spacial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model


LxmertForQuestionAnswering

 expects these spacial features to be normalized bounding boxes on a scale of 0 to
These are currently not provided by the transformers library.   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?   visual_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 corres


LxmertForQuestionAnswering

ponds to a sentence B token.
What are token type IDs?   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (Torch.Tensor of shape (batch_size), optional) —
A one-hot representation of the correct answ


LxmertForQuestionAnswering

er   Returns
transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput or tuple(torch.FloatTensor)

A transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput 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 (LxmertConfig) and inputs.
loss (optional, returned when labels is provided, torch.FloatTensor of shape (1,)) — Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.k.
question_answering_score (torch.FloatTensor of shape (batch_size, n_qa_answers), optional) — Prediction scores of question answering objective (classification).
language_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 f


LxmertForQuestionAnswering

or input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).
vision_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 input features + one for the output of each cross-modality layer) of
shape (batch_size, sequence_length, hidden_size).
language_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.
vision_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_atten


LxmertForQuestionAnswering

tions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length). Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
  The LxmertForQuestionAnswering 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


LxmertForQuestionAnswering

the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, LxmertForQuestionAnswering
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("unc-nlp/lxmert-base-uncased")
>>> model = LxmertForQuestionAnswering.from_pretrained("unc-nlp/lxmert-base-uncased")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
...     outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> # target is "nice puppet"
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss


TFLxmertModel

class transformers.TFLxmertModel  < source > ( *args **kwargs  )   Parameters   config (LxmertConfig) — 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 Lxmert Model transformer outputting raw hidden-states without any specific head on top. The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from
Transformers by Hao Tan and Mohit Bansal. It’s a vision and language transformer
model, pre-trained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual
genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
for question answering attribute prediction, and object tag prediction. This model is also


TFLxmertModel

a tf.keras.Model subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior. TensorFlow models and layers in transformers accept two formats as input: 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
pos


TFLxmertModel

itional argument: a single Tensor with input_ids only and nothing else: model(input_ids) a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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 visual_feats: tf.Tensor | None = None visual_pos: tf.Tensor | None = None attention_mask: np.ndarray | tf.Tensor | None = None visual_attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None inputs_embeds:


TFLxmertModel

 np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False  ) → transformers.models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput or tuple(tf.Tensor)  Parameters   input_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?   visual_feats (tf.Tensor of shape (batch_size, num_visual_features, visual_feat_dim)) —
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.   visual_pos (tf.Tensor of shape (batch_size, num_visual_features, visual_feat_dim)) —
This input represents sp


TFLxmertModel

acial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
These are currently not provided by the transformers library.   attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   visual_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
MMask 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 (tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to indicate first and second portions of the inputs.


TFLxmertModel

 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?   inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. This argument can be used on


TFLxmertModel

ly in eager mode, in graph mode the value in the config will be
used instead.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.   training (bool, optional, defaults to False) —
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).   Returns
transformers.models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput or tuple(tf.Tensor)

A transformers.models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput 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 (LxmertConfig) and inputs.
language_output (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the las


TFLxmertModel

t layer of the language encoder.
vision_output (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the visual encoder.
pooled_output (tf.Tensor of shape (batch_size, hidden_size)) — Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear
language_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 input features + one for the output of each cross-modality layer) of shape
(batch_size, sequence_length, hidden_size).
vision_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 input features + one for the output of each cros


TFLxmertModel

s-modality layer) of shape
(batch_size, sequence_length, hidden_size).
language_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.
vision_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.
cross_encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of


TFLxmertModel

 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 TFLxmertModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, TFLxmertModel
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("unc-nlp/lxmert-base-uncased")
>>> model = TFLxmertModel.from_pretrained("unc-nlp/lxmert-base-uncased")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hid


TFLxmertForPreTraining

class transformers.TFLxmertForPreTraining  < source > ( *args **kwargs  )   Parameters   config (LxmertConfig) — 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.    Lxmert Model with a language modeling head on top. The LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from
Transformers by Hao Tan and Mohit Bansal. It’s a vision and language transformer
model, pre-trained on a variety of multi-modal datasets comprising of GQA, VQAv2.0, MCSCOCO captions, and Visual
genome, using a combination of masked language modeling, region of interest feature regression, cross entropy loss
for question answering attribute prediction, and object tag prediction. This model is also a tf.keras.Model subclass. Use it
as a regula


TFLxmertForPreTraining

r TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior. TensorFlow models and layers in transformers accept two formats as input: 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


TFLxmertForPreTraining

 and nothing else: model(input_ids) a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
model([input_ids, attention_mask]) or model([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 visual_feats: tf.Tensor | None = None visual_pos: tf.Tensor | None = None attention_mask: tf.Tensor | None = None visual_attention_mask: tf.Tensor | None = None token_type_ids: tf.Tensor | None = None inputs_embeds: tf.Tensor | None = None masked_lm_labels: tf.Tensor | None = None obj_labels: Dict[str, Tuple[tf.Te


TFLxmertForPreTraining

nsor, tf.Tensor]] | None = None matched_label: tf.Tensor | None = None ans: tf.Tensor | None = None output_attentions: bool | None = None output_hidden_states: bool | None = None return_dict: bool | None = None training: bool = False  ) → transformers.models.lxmert.modeling_tf_lxmert.TFLxmertForPreTrainingOutput or tuple(tf.Tensor)  Parameters   input_ids (np.ndarray or tf.Tensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and
PreTrainedTokenizer.encode() for details.
What are input IDs?   visual_feats (tf.Tensor of shape (batch_size, num_visual_features, visual_feat_dim)) —
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.   visual_pos (tf.Tensor of shape (batch_size, num_visual_


TFLxmertForPreTraining

features, visual_feat_dim)) —
This input represents spacial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spacial features to be normalized bounding boxes on a scale of 0 to
These are currently not provided by the transformers library.   attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   visual_attention_mask (tf.Tensor of shape (batch_size, sequence_length), optional) —
MMask 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 (tf.Tensor of shape (batch_size, sequence_length), optional) —
Segment token indices to ind


TFLxmertForPreTraining

icate 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?   inputs_embeds (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
mor