---
local: layoutlm
sections:
- local: overview
  title: Overview
- local: transformers.LayoutLMConfig
  title: LayoutLMConfig
- local: transformers.LayoutLMTokenizer
  title: LayoutLMTokenizer
- local: transformers.LayoutLMTokenizerFast
  title: LayoutLMTokenizerFast
- local: transformers.LayoutLMModel
  title: LayoutLMModel
- local: transformers.LayoutLMForMaskedLM
  title: LayoutLMForMaskedLM
- local: transformers.LayoutLMForSequenceClassification
  title: LayoutLMForSequenceClassification
- local: transformers.LayoutLMForTokenClassification
  title: LayoutLMForTokenClassification
- local: transformers.TFLayoutLMModel
  title: TFLayoutLMModel
- local: transformers.TFLayoutLMForMaskedLM
  title: TFLayoutLMForMaskedLM
- local: transformers.TFLayoutLMForSequenceClassification
  title: TFLayoutLMForSequenceClassification
- local: transformers.TFLayoutLMForTokenClassification
  title: TFLayoutLMForTokenClassification
title: LayoutLM
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
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<h1 id="layoutlm">LayoutLM</h1>

<a id='Overview'></a>

<h2 id="overview">Overview</h2>

The LayoutLM model was proposed in the paper [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results
on several downstream tasks:

- form understanding: the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset (a collection of 199 annotated
  forms comprising more than 30,000 words).
- receipt understanding: the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset (a collection of 626 receipts for
  training and 347 receipts for testing).
- document image classification: the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset (a collection of
  400,000 images belonging to one of 16 classes).

The abstract from the paper is the following:

*Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is
beneficial for a great number of real-world document image understanding tasks such as information extraction from
scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM.
To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for
document-level pretraining. It achieves new state-of-the-art results in several downstream tasks, including form
understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification
(from 93.07 to 94.42).*

Tips:

- In addition to *input_ids*, [forward()](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMModel.forward) also expects the input `bbox`, which are
  the bounding boxes (i.e. 2D-positions) of the input tokens. These can be obtained using an external OCR engine such
  as Google's [Tesseract](https://github.com/tesseract-ocr/tesseract) (there's a [Python wrapper](https://pypi.org/project/pytesseract/) available). Each bounding box should be 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 one first needs to normalize the bounding boxes to be on a 0-1000
  scale. To normalize, you can use the following function:

```python
def normalize_bbox(bbox, width, height):
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]
```

Here, `width` and `height` correspond to the width and height of the original document in which the token
occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:

```python
from PIL import Image

image = Image.open("name_of_your_document - can be a png file, pdf, etc.")

width, height = image.size
```

- For a demo which shows how to fine-tune [LayoutLMForTokenClassification](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification) on the [FUNSD dataset](https://guillaumejaume.github.io/FUNSD/) (a collection of annotated forms), see [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb).
  It includes an inference part, which shows how to use Google's Tesseract on a new document.

This model was contributed by [liminghao1630](https://huggingface.co/liminghao1630). The original code can be found
[here](https://github.com/microsoft/unilm/tree/master/layoutlm).


<h2 id="transformers.LayoutLMConfig">LayoutLMConfig</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMConfig</name><anchor>transformers.LayoutLMConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/configuration_layoutlm.py#L35</source><parameters>[{"name": "vocab_size", "val": " = 30522"}, {"name": "hidden_size", "val": " = 768"}, {"name": "num_hidden_layers", "val": " = 12"}, {"name": "num_attention_heads", "val": " = 12"}, {"name": "intermediate_size", "val": " = 3072"}, {"name": "hidden_act", "val": " = 'gelu'"}, {"name": "hidden_dropout_prob", "val": " = 0.1"}, {"name": "attention_probs_dropout_prob", "val": " = 0.1"}, {"name": "max_position_embeddings", "val": " = 512"}, {"name": "type_vocab_size", "val": " = 2"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-12"}, {"name": "pad_token_id", "val": " = 0"}, {"name": "max_2d_position_embeddings", "val": " = 1024"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_size** (`int`, *optional*, defaults to 30522) --
  Vocabulary size of the LayoutLM model. Defines the different tokens that can be represented by the
  *inputs_ids* passed to the forward method of [LayoutLMModel](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMModel).
- **hidden_size** (`int`, *optional*, defaults to 768) --
  Dimensionality of the encoder layers and the pooler layer.
- **num_hidden_layers** (`int`, *optional*, defaults to 12) --
  Number of hidden layers in the Transformer encoder.
- **num_attention_heads** (`int`, *optional*, defaults to 12) --
  Number of attention heads for each attention layer in the Transformer encoder.
- **intermediate_size** (`int`, *optional*, defaults to 3072) --
  Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
- **hidden_act** (`str` or `function`, *optional*, defaults to `"gelu"`) --
  The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
  `"relu"`, `"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 [LayoutLMModel](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMModel).
- **initializer_range** (`float`, *optional*, defaults to 0.02) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- **layer_norm_eps** (`float`, *optional*, defaults to 1e-12) --
  The epsilon used by the layer normalization layers.
- **max_2d_position_embeddings** (`int`, *optional*, defaults to 1024) --
  The maximum value that the 2D position embedding might ever used. Typically set this to something large
  just in case (e.g., 1024).</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [LayoutLMModel](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMModel). It is used to instantiate a
LayoutLM 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 LayoutLM
[layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) architecture.

Configuration objects inherit from [BertConfig](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertConfig) and can be used to control the model outputs. Read the
documentation from [BertConfig](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertConfig) for more information.




Examples:

```python
>>> from transformers import LayoutLMModel, LayoutLMConfig

>>> # Initializing a LayoutLM configuration
>>> configuration = LayoutLMConfig()

>>> # Initializing a model from the configuration
>>> model = LayoutLMModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

</div>

<h2 id="transformers.LayoutLMTokenizer">LayoutLMTokenizer</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMTokenizer</name><anchor>transformers.LayoutLMTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/tokenization_layoutlm.py#L46</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "do_lower_case", "val": " = True"}, {"name": "do_basic_tokenize", "val": " = True"}, {"name": "never_split", "val": " = None"}, {"name": "unk_token", "val": " = '[UNK]'"}, {"name": "sep_token", "val": " = '[SEP]'"}, {"name": "pad_token", "val": " = '[PAD]'"}, {"name": "cls_token", "val": " = '[CLS]'"}, {"name": "mask_token", "val": " = '[MASK]'"}, {"name": "tokenize_chinese_chars", "val": " = True"}, {"name": "strip_accents", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

Constructs a LayoutLM tokenizer.

[LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer) is identical to [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer) and runs end-to-end tokenization: punctuation splitting +
wordpiece.

Refer to superclass [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer) for usage examples and documentation concerning parameters.


</div>

<h2 id="transformers.LayoutLMTokenizerFast">LayoutLMTokenizerFast</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMTokenizerFast</name><anchor>transformers.LayoutLMTokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/tokenization_layoutlm_fast.py#L51</source><parameters>[{"name": "vocab_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "do_lower_case", "val": " = True"}, {"name": "unk_token", "val": " = '[UNK]'"}, {"name": "sep_token", "val": " = '[SEP]'"}, {"name": "pad_token", "val": " = '[PAD]'"}, {"name": "cls_token", "val": " = '[CLS]'"}, {"name": "mask_token", "val": " = '[MASK]'"}, {"name": "tokenize_chinese_chars", "val": " = True"}, {"name": "strip_accents", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

Constructs a "Fast" LayoutLMTokenizer.

[LayoutLMTokenizerFast](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizerFast) is identical to [BertTokenizerFast](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizerFast) and runs end-to-end tokenization: punctuation
splitting + wordpiece.

Refer to superclass [BertTokenizerFast](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizerFast) for usage examples and documentation concerning parameters.


</div>

<h2 id="transformers.LayoutLMModel">LayoutLMModel</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMModel</name><anchor>transformers.LayoutLMModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L706</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LayoutLMModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L732</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "bbox", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "encoder_hidden_states", "val": " = None"}, {"name": "encoder_attention_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **bbox** (`torch.LongTensor` 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. See [Overview](#Overview) for normalization.
- **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 MASKED tokens.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
  indicates the head is **not masked**, `0` indicates the head is **masked**.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
  returned tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
  for more detail.
- **return_dict** (`bool`, *optional*) --
  If set to `True`, the model will return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) after further processing
  through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
  the classification token after processing through a linear layer and a tanh activation function. The linear
  layer weights are trained from the next sentence prediction (classification) objective during pretraining.
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

  Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if
  `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
  encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
  `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
  input) to speed up sequential decoding.</retdesc></docstring>
The [LayoutLMModel](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMModel) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, LayoutLMModel
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])

>>> outputs = model(
...     input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
... )

>>> last_hidden_states = outputs.last_hidden_state
```

</div></div>

<h2 id="transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMForMaskedLM</name><anchor>transformers.LayoutLMForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L854</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
LayoutLM Model with a `language modeling` head on top.
The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LayoutLMForMaskedLM.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L873</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "bbox", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "encoder_hidden_states", "val": " = None"}, {"name": "encoder_attention_mask", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **bbox** (`torch.LongTensor` 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. See [Overview](#Overview) for normalization.
- **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 MASKED tokens.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
  indicates the head is **not masked**, `0` indicates the head is **masked**.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
  returned tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
  for more detail.
- **return_dict** (`bool`, *optional*) --
  If set to `True`, the model will return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.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]`</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.MaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.MaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Masked language modeling (MLM) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [LayoutLMForMaskedLM](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, LayoutLMForMaskedLM
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])

>>> labels = tokenizer("Hello world", return_tensors="pt")["input_ids"]

>>> outputs = model(
...     input_ids=input_ids,
...     bbox=bbox,
...     attention_mask=attention_mask,
...     token_type_ids=token_type_ids,
...     labels=labels,
... )

>>> loss = outputs.loss
```

</div></div>

<h2 id="transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMForSequenceClassification</name><anchor>transformers.LayoutLMForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L983</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

LayoutLM Model with a sequence classification head on top (a linear layer on top of the pooled output) e.g. for
document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset.

The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LayoutLMForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L997</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "bbox", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **bbox** (`torch.LongTensor` 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. See [Overview](#Overview) for normalization.
- **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 MASKED tokens.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
  indicates the head is **not masked**, `0` indicates the head is **masked**.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
  returned tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
  for more detail.
- **return_dict** (`bool`, *optional*) --
  If set to `True`, the model will return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.SequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [LayoutLMForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, LayoutLMForSequenceClassification
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> sequence_label = torch.tensor([1])

>>> outputs = model(
...     input_ids=input_ids,
...     bbox=bbox,
...     attention_mask=attention_mask,
...     token_type_ids=token_type_ids,
...     labels=sequence_label,
... )

>>> loss = outputs.loss
>>> logits = outputs.logits
```

</div></div>

<h2 id="transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</h2>

<div class="docstring">

<docstring><name>class transformers.LayoutLMForTokenClassification</name><anchor>transformers.LayoutLMForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L1120</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
sequence labeling (information extraction) tasks such as the [FUNSD](https://guillaumejaume.github.io/FUNSD/)
dataset and the [SROIE](https://rrc.cvc.uab.es/?ch=13) dataset.

The LayoutLM model was proposed in [LayoutLM: Pre-training of Text and Layout for Document Image
Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei and
Ming Zhou.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LayoutLMForTokenClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_layoutlm.py#L1134</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "bbox", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **bbox** (`torch.LongTensor` 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. See [Overview](#Overview) for normalization.
- **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 MASKED tokens.

  [What are attention masks?](../glossary#attention-mask)
- **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?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: `1`
  indicates the head is **not masked**, `0` indicates the head is **masked**.
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  If set to `True`, the attentions tensors of all attention layers are returned. See `attentions` under
  returned tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  If set to `True`, the hidden states of all layers are returned. See `hidden_states` under returned tensors
  for more detail.
- **return_dict** (`bool`, *optional*) --
  If set to `True`, the model will return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.TokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.
- **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.</retdesc></docstring>
The [LayoutLMForTokenClassification](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, LayoutLMForTokenClassification
>>> import torch

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="pt")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = torch.tensor([token_boxes])
>>> token_labels = torch.tensor([1, 1, 0, 0]).unsqueeze(0)  # batch size of 1

>>> outputs = model(
...     input_ids=input_ids,
...     bbox=bbox,
...     attention_mask=attention_mask,
...     token_type_ids=token_type_ids,
...     labels=token_labels,
... )

>>> loss = outputs.loss
>>> logits = outputs.logits
```

</div></div>

<h2 id="transformers.TFLayoutLMModel">TFLayoutLMModel</h2>

<div class="docstring">

<docstring><name>class transformers.TFLayoutLMModel</name><anchor>transformers.TFLayoutLMModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L921</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, 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(inputs_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(&amp;lcub;"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLayoutLMModel.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L927</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "bbox", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "encoder_attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  [What are input IDs?](../glossary#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]`.
- **attention_mask** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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.

  [What are token type IDs?](../glossary#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]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.
- **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).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions) 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 ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) and inputs.

- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`tf.Tensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
  Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
  prediction (classification) objective during pretraining.

  This output is usually *not* a good summary of the semantic content of the input, you're often better with
  averaging or pooling the sequence of hidden-states for the whole input sequence.
- **past_key_values** (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
  `past_key_values` input) to speed up sequential decoding.
- **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.
- **cross_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 of the decoder's cross-attention layer, after the attention softmax, used to compute the
  weighted average in the cross-attention heads.</retdesc></docstring>
The [TFLayoutLMModel](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.TFLayoutLMModel) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, TFLayoutLMModel
>>> import tensorflow as tf

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])

>>> outputs = model(
...     input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids
... )

>>> last_hidden_states = outputs.last_hidden_state
```

</div></div>

<h2 id="transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</h2>

<div class="docstring">

<docstring><name>class transformers.TFLayoutLMForMaskedLM</name><anchor>transformers.TFLayoutLMForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L1039</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
LayoutLM Model with a `language modeling` head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, 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(inputs_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(&amp;lcub;"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLayoutLMForMaskedLM.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L1067</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "bbox", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  [What are input IDs?](../glossary#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]`.
- **attention_mask** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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.

  [What are token type IDs?](../glossary#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]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).

- **labels** (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
  config.vocab_size]` (see `input_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]`</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFMaskedLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided) -- Masked language modeling (MLM) loss.
- **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before 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.</retdesc></docstring>
The [TFLayoutLMForMaskedLM](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, TFLayoutLMForMaskedLM
>>> import tensorflow as tf

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "[MASK]"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])

>>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"]

>>> outputs = model(
...     input_ids=input_ids,
...     bbox=bbox,
...     attention_mask=attention_mask,
...     token_type_ids=token_type_ids,
...     labels=labels,
... )

>>> loss = outputs.loss
```

</div></div>

<h2 id="transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</h2>

<div class="docstring">

<docstring><name>class transformers.TFLayoutLMForSequenceClassification</name><anchor>transformers.TFLayoutLMForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L1193</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.


This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, 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(inputs_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(&amp;lcub;"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLayoutLMForSequenceClassification.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L1211</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "bbox", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  [What are input IDs?](../glossary#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]`.
- **attention_mask** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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.

  [What are token type IDs?](../glossary#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]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).

- **labels** (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#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 ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) 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.</retdesc></docstring>
The [TFLayoutLMForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LayoutLMTokenizer, TFLayoutLMForSequenceClassification
>>> import tensorflow as tf

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> sequence_label = tf.convert_to_tensor([1])

>>> outputs = model(
...     input_ids=input_ids,
...     bbox=bbox,
...     attention_mask=attention_mask,
...     token_type_ids=token_type_ids,
...     labels=sequence_label,
... )

>>> loss = outputs.loss
>>> logits = outputs.logits
```

</div></div>

<h2 id="transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</h2>

<div class="docstring">

<docstring><name>class transformers.TFLayoutLMForTokenClassification</name><anchor>transformers.TFLayoutLMForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L1334</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) -- 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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

LayoutLM Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.


This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.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](https://www.tensorflow.org/api_docs/python/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.

<Tip>

TF 2.0 models accepts two formats as inputs:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using `tf.keras.Model.fit` method which currently requires having all the
tensors in the first argument of the model call function: `model(inputs)`.

If you choose this second option, 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(inputs_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(&amp;lcub;"input_ids": input_ids, "token_type_ids": token_type_ids})`

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLayoutLMForTokenClassification.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/layoutlm/modeling_tf_layoutlm.py#L1358</source><parameters>[{"name": "input_ids", "val": ": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[tensorflow.python.keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, tensorflow.python.keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, tensorflow.python.keras.engine.keras_tensor.KerasTensor, NoneType] = None"}, {"name": "bbox", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "attention_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "token_type_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "position_ids", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "head_mask", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "inputs_embeds", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "labels", "val": ": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"}, {"name": "training", "val": ": typing.Optional[bool] = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LayoutLMTokenizer](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) for details.

  [What are input IDs?](../glossary#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]`.
- **attention_mask** (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **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.

  [What are token type IDs?](../glossary#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]`.

  [What are position IDs?](../glossary#position-ids)
- **head_mask** (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) --
  Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).

- **labels** (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_tf_outputs.TFTokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.modeling_tf_outputs.TFTokenClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#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 ([LayoutLMConfig](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.LayoutLMConfig)) 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)`) -- Classification 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.</retdesc></docstring>
The [TFLayoutLMForTokenClassification](/docs/transformers/pr_15592/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> import tensorflow as tf
>>> from transformers import LayoutLMTokenizer, TFLayoutLMForTokenClassification

>>> tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased")

>>> words = ["Hello", "world"]
>>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

>>> token_boxes = []
>>> for word, box in zip(words, normalized_word_boxes):
...     word_tokens = tokenizer.tokenize(word)
...     token_boxes.extend([box] * len(word_tokens))
>>> # add bounding boxes of cls + sep tokens
>>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> token_labels = tf.convert_to_tensor([1, 1, 0, 0])

>>> outputs = model(
...     input_ids=input_ids,
...     bbox=bbox,
...     attention_mask=attention_mask,
...     token_type_ids=token_type_ids,
...     labels=token_labels,
... )

>>> loss = outputs.loss
>>> logits = outputs.logits
```

</div></div>
