MarkupLM (original) (raw)

Overview

The MarkupLM model was proposed in MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. MarkupLM is BERT, but applied to HTML pages instead of raw text documents. The model incorporates additional embedding layers to improve performance, similar to LayoutLM.

The model can be used for tasks like question answering on web pages or information extraction from web pages. It obtains state-of-the-art results on 2 important benchmarks:

• WebSRC, a dataset for Web-Based Structural Reading Comprehension (a bit like SQuAD but for web pages)
• SWDE, a dataset for information extraction from web pages (basically named-entity recognition on web pages)

The abstract from the paper is the following:

Multimodal pre-training with text, layout, and image has made significant progress for Visually-rich Document Understanding (VrDU), especially the fixed-layout documents such as scanned document images. While, there are still a large number of digital documents where the layout information is not fixed and needs to be interactively and dynamically rendered for visualization, making existing layout-based pre-training approaches not easy to apply. In this paper, we propose MarkupLM for document understanding tasks with markup languages as the backbone such as HTML/XML-based documents, where text and markup information is jointly pre-trained. Experiment results show that the pre-trained MarkupLM significantly outperforms the existing strong baseline models on several document understanding tasks. The pre-trained model and code will be publicly available.

This model was contributed by nielsr. The original code can be found here.

Usage tips

• In addition to input_ids, forward() expects 2 additional inputs, namely xpath_tags_seq and xpath_subs_seq. These are the XPATH tags and subscripts respectively for each token in the input sequence.
• One can use MarkupLMProcessor to prepare all data for the model. Refer to the usage guide for more info.

MarkupLM architecture. Taken from the original paper.

Usage: MarkupLMProcessor

The easiest way to prepare data for the model is to use MarkupLMProcessor, which internally combines a feature extractor (MarkupLMFeatureExtractor) and a tokenizer (MarkupLMTokenizer or MarkupLMTokenizerFast). The feature extractor is used to extract all nodes and xpaths from the HTML strings, which are then provided to the tokenizer, which turns them into the token-level inputs of the model (input_ids etc.). Note that you can still use the feature extractor and tokenizer separately, if you only want to handle one of the two tasks.

from transformers import MarkupLMFeatureExtractor, MarkupLMTokenizerFast, MarkupLMProcessor

feature_extractor = MarkupLMFeatureExtractor() tokenizer = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base") processor = MarkupLMProcessor(feature_extractor, tokenizer)

In short, one can provide HTML strings (and possibly additional data) to MarkupLMProcessor, and it will create the inputs expected by the model. Internally, the processor first usesMarkupLMFeatureExtractor to get a list of nodes and corresponding xpaths. The nodes and xpaths are then provided to MarkupLMTokenizer or MarkupLMTokenizerFast, which converts them to token-level input_ids, attention_mask, token_type_ids, xpath_subs_seq, xpath_tags_seq. Optionally, one can provide node labels to the processor, which are turned into token-level labels.

MarkupLMFeatureExtractor uses Beautiful Soup, a Python library for pulling data out of HTML and XML files, under the hood. Note that you can still use your own parsing solution of choice, and provide the nodes and xpaths yourself to MarkupLMTokenizer or MarkupLMTokenizerFast.

In total, there are 5 use cases that are supported by the processor. Below, we list them all. Note that each of these use cases work for both batched and non-batched inputs (we illustrate them for non-batched inputs).

Use case 1: web page classification (training, inference) + token classification (inference), parse_html = True

This is the simplest case, in which the processor will use the feature extractor to get all nodes and xpaths from the HTML.

from transformers import MarkupLMProcessor

processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

html_string = """ ... ... ... ... Hello world ... ... ...

Welcome

...

Here is my website.

... ... """

encoding = processor(html_string, return_tensors="pt") print(encoding.keys()) dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Use case 2: web page classification (training, inference) + token classification (inference), parse_html=False

In case one already has obtained all nodes and xpaths, one doesn’t need the feature extractor. In that case, one should provide the nodes and corresponding xpaths themselves to the processor, and make sure to set parse_html to False.

from transformers import MarkupLMProcessor

processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base") processor.parse_html = False

nodes = ["hello", "world", "how", "are"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"] encoding = processor(nodes=nodes, xpaths=xpaths, return_tensors="pt") print(encoding.keys()) dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Use case 3: token classification (training), parse_html=False

For token classification tasks (such as SWDE), one can also provide the corresponding node labels in order to train a model. The processor will then convert these into token-level labels. By default, it will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is theignore_index of PyTorch’s CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can initialize the tokenizer with only_label_first_subword set to False.

from transformers import MarkupLMProcessor

processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base") processor.parse_html = False

nodes = ["hello", "world", "how", "are"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"] node_labels = [1, 2, 2, 1] encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt") print(encoding.keys()) dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq', 'labels'])

Use case 4: web page question answering (inference), parse_html=True

For question answering tasks on web pages, you can provide a question to the processor. By default, the processor will use the feature extractor to get all nodes and xpaths, and create [CLS] question tokens [SEP] word tokens [SEP].

from transformers import MarkupLMProcessor

processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")

html_string = """ ... ... ... ... Hello world ... ... ...

Welcome

...

My name is Niels.

... ... """

question = "What's his name?" encoding = processor(html_string, questions=question, return_tensors="pt") print(encoding.keys()) dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Use case 5: web page question answering (inference), parse_html=False

For question answering tasks (such as WebSRC), you can provide a question to the processor. If you have extracted all nodes and xpaths yourself, you can provide them directly to the processor. Make sure to set parse_html to False.

from transformers import MarkupLMProcessor

processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base") processor.parse_html = False

nodes = ["hello", "world", "how", "are"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"] question = "What's his name?" encoding = processor(nodes=nodes, xpaths=xpaths, questions=question, return_tensors="pt") print(encoding.keys()) dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])

Resources

• Demo notebooks
• Text classification task guide
• Token classification task guide
• Question answering task guide

MarkupLMConfig

class transformers.MarkupLMConfig

< source >

( vocab_size = 30522 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 2 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 0 bos_token_id = 0 eos_token_id = 2 max_xpath_tag_unit_embeddings = 256 max_xpath_subs_unit_embeddings = 1024 tag_pad_id = 216 subs_pad_id = 1001 xpath_unit_hidden_size = 32 max_depth = 50 position_embedding_type = 'absolute' use_cache = True classifier_dropout = None **kwargs )

Parameters

• vocab_size (int, optional, defaults to 30522) — Vocabulary size of the MarkupLM model. Defines the different tokens that can be represented by the_inputs_ids_ passed to the forward method of MarkupLMModel.
• 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 MarkupLMModel.
• 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_tree_id_unit_embeddings (int, optional, defaults to 1024) — The maximum value that the tree id unit embedding might ever use. Typically set this to something large just in case (e.g., 1024).
• max_xpath_tag_unit_embeddings (int, optional, defaults to 256) — The maximum value that the xpath tag unit embedding might ever use. Typically set this to something large just in case (e.g., 256).
• max_xpath_subs_unit_embeddings (int, optional, defaults to 1024) — The maximum value that the xpath subscript unit embedding might ever use. Typically set this to something large just in case (e.g., 1024).
• tag_pad_id (int, optional, defaults to 216) — The id of the padding token in the xpath tags.
• subs_pad_id (int, optional, defaults to 1001) — The id of the padding token in the xpath subscripts.
• xpath_tag_unit_hidden_size (int, optional, defaults to 32) — The hidden size of each tree id unit. One complete tree index will have (50*xpath_tag_unit_hidden_size)-dim.
• max_depth (int, optional, defaults to 50) — The maximum depth in xpath.

This is the configuration class to store the configuration of a MarkupLMModel. It is used to instantiate a MarkupLM 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 MarkupLMmicrosoft/markuplm-base architecture.

Configuration objects inherit from BertConfig and can be used to control the model outputs. Read the documentation from BertConfig for more information.

Examples:

from transformers import MarkupLMModel, MarkupLMConfig

configuration = MarkupLMConfig()

model = MarkupLMModel(configuration)

configuration = model.config

MarkupLMFeatureExtractor

Constructs a MarkupLM feature extractor. This can be used to get a list of nodes and corresponding xpaths from HTML strings.

This feature extractor inherits from PreTrainedFeatureExtractor() which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

( html_strings ) → BatchFeature

Parameters

• html_strings (str, List[str]) — The HTML string or batch of HTML strings from which to extract nodes and corresponding xpaths.

A BatchFeature with the following fields:

• nodes — Nodes.
• xpaths — Corresponding xpaths.

Main method to prepare for the model one or several HTML strings.

Examples:

from transformers import MarkupLMFeatureExtractor

page_name_1 = "page1.html" page_name_2 = "page2.html" page_name_3 = "page3.html"

with open(page_name_1) as f: ... single_html_string = f.read()

feature_extractor = MarkupLMFeatureExtractor()

encoding = feature_extractor(single_html_string) print(encoding.keys())

multi_html_strings = []

with open(page_name_2) as f: ... multi_html_strings.append(f.read()) with open(page_name_3) as f: ... multi_html_strings.append(f.read())

encoding = feature_extractor(multi_html_strings) print(encoding.keys())

MarkupLMTokenizer

class transformers.MarkupLMTokenizer

< source >

( vocab_file merges_file tags_dict errors = 'replace' bos_token = '' eos_token = '' sep_token = '' cls_token = '' unk_token = '' pad_token = '' mask_token = '' add_prefix_space = False max_depth = 50 max_width = 1000 pad_width = 1001 pad_token_label = -100 only_label_first_subword = True **kwargs )

Parameters

• vocab_file (str) — Path to the vocabulary file.
• merges_file (str) — Path to the merges file.
• errors (str, optional, defaults to "replace") — Paradigm to follow when decoding bytes to UTF-8. Seebytes.decode for more information.
• bos_token (str, optional, defaults to "<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.
• eos_token (str, optional, defaults to "</s>") — The end of sequence token.
  When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.
• sep_token (str, optional, defaults to "</s>") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• cls_token (str, optional, defaults to "<s>") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
• unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.
• mask_token (str, optional, defaults to "<mask>") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
• add_prefix_space (bool, optional, defaults to False) — Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space).

Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). MarkupLMTokenizer can be used to turn HTML strings into to token-level input_ids, attention_mask, token_type_ids, xpath_tags_seq andxpath_tags_seq. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

build_inputs_with_special_tokens

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

• token_ids_0 (List[int]) — List of IDs to which the special tokens will be added.
• token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format:

• single sequence: <s> X </s>
• pair of sequences: <s> A </s></s> B </s>

get_special_tokens_mask

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) → List[int]

Parameters

• Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding —
• special tokens using the tokenizer prepare_for_model method. — token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. already_has_special_tokens (bool, optional, defaults to False): Whether or not the token list is already formatted with special tokens for the model.

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

create_token_type_ids_from_sequences

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

• token_ids_0 (List[int]) — List of IDs.
• token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

List of zeros.

Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned.

save_vocabulary

< source >

( save_directory: str filename_prefix: typing.Optional[str] = None )

MarkupLMTokenizerFast

class transformers.MarkupLMTokenizerFast

< source >

( vocab_file merges_file tags_dict tokenizer_file = None errors = 'replace' bos_token = '' eos_token = '' sep_token = '' cls_token = '' unk_token = '' pad_token = '' mask_token = '' add_prefix_space = False max_depth = 50 max_width = 1000 pad_width = 1001 pad_token_label = -100 only_label_first_subword = True trim_offsets = False **kwargs )

Parameters

• vocab_file (str) — Path to the vocabulary file.
• merges_file (str) — Path to the merges file.
• errors (str, optional, defaults to "replace") — Paradigm to follow when decoding bytes to UTF-8. Seebytes.decode for more information.
• bos_token (str, optional, defaults to "<s>") — The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
  When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.
• eos_token (str, optional, defaults to "</s>") — The end of sequence token.
  When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.
• sep_token (str, optional, defaults to "</s>") — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
• cls_token (str, optional, defaults to "<s>") — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
• unk_token (str, optional, defaults to "<unk>") — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
• pad_token (str, optional, defaults to "<pad>") — The token used for padding, for example when batching sequences of different lengths.
• mask_token (str, optional, defaults to "<mask>") — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
• add_prefix_space (bool, optional, defaults to False) — Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space).

Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).

MarkupLMTokenizerFast can be used to turn HTML strings into to token-level input_ids, attention_mask,token_type_ids, xpath_tags_seq and xpath_tags_seq. This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods.

Users should refer to this superclass for more information regarding those methods.

batch_encode_plus

< source >

( batch_text_or_text_pairs: typing.Union[typing.List[str], typing.List[typing.Tuple[str, str]], typing.List[typing.List[str]]] is_pair: typing.Optional[bool] = None xpaths: typing.Optional[typing.List[typing.List[typing.List[int]]]] = None node_labels: typing.Union[typing.List[int], typing.List[typing.List[int]], NoneType] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs )

add_special_tokens (bool, optional, defaults to True): Whether or not to add special tokens when encoding the sequences. This will use the underlyingPretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is useful if you want to add bos or eos tokens automatically. padding (bool, str or PaddingStrategy, optional, defaults to False): Activates and controls padding. Accepts the following values:

• True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
• 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
• False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (bool, str or TruncationStrategy, optional, defaults to False): Activates and controls truncation. Accepts the following values:
• True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
• 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
• 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
• False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (int, optional): Controls the maximum length to use by one of the truncation/padding parameters.

If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (int, optional, defaults to 0): If set to a number along with max_length, the overflowing tokens returned whenreturn_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. is_split_into_words (bool, optional, defaults to False): Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification. pad_to_multiple_of (int, optional): If set will pad the sequence to a multiple of the provided value. Requires padding to be activated. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability>= 7.5 (Volta). padding_side (str, optional): The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name. return_tensors (str or TensorType, optional): If set, will return tensors instead of list of python integers. Acceptable values are:

• 'tf': Return TensorFlow tf.constant objects.
• 'pt': Return PyTorch torch.Tensor objects.
• 'np': Return Numpy np.ndarray objects.

add_special_tokens (bool, optional, defaults to True): Whether or not to encode the sequences with the special tokens relative to their model. padding (bool, str or PaddingStrategy, optional, defaults to False): Activates and controls padding. Accepts the following values:

• True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
• 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
• False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (bool, str or TruncationStrategy, optional, defaults to False): Activates and controls truncation. Accepts the following values:
• True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
• 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
• 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
• False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (int, optional): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set toNone, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. stride (int, optional, defaults to 0): If set to a number along with max_length, the overflowing tokens returned whenreturn_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens. pad_to_multiple_of (int, optional): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (str or TensorType, optional): If set, will return tensors instead of list of python integers. Acceptable values are:
• 'tf': Return TensorFlow tf.constant objects.
• 'pt': Return PyTorch torch.Tensor objects.
• 'np': Return Numpy np.ndarray objects.

build_inputs_with_special_tokens

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

• token_ids_0 (List[int]) — List of IDs to which the special tokens will be added.
• token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format:

• single sequence: <s> X </s>
• pair of sequences: <s> A </s></s> B </s>

create_token_type_ids_from_sequences

< source >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

Parameters

• token_ids_0 (List[int]) — List of IDs.
• token_ids_1 (List[int], optional) — Optional second list of IDs for sequence pairs.

List of zeros.

Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned.

encode_plus

< source >

( text: typing.Union[str, typing.List[str]] text_pair: typing.Optional[typing.List[str]] = None xpaths: typing.Optional[typing.List[typing.List[int]]] = None node_labels: typing.Optional[typing.List[int]] = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None padding_side: typing.Optional[str] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs )

Parameters

• text (str, List[str], List[List[str]]) — The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
• text_pair (List[str] or List[int], optional) — Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a list of list of strings (words of a batch of examples).
• add_special_tokens (bool, optional, defaults to True) — Whether or not to add special tokens when encoding the sequences. This will use the underlyingPretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is useful if you want to add bos or eos tokens automatically.
• padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
  • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
  • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
  • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
• truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
  • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
• max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters.
  If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
• stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned whenreturn_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
• is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
• pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. Requires padding to be activated. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability>= 7.5 (Volta).
• padding_side (str, optional) — The side on which the model should have padding applied. Should be selected between [‘right’, ‘left’]. Default value is picked from the class attribute of the same name.
• return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  • 'tf': Return TensorFlow tf.constant objects.
  • 'pt': Return PyTorch torch.Tensor objects.
  • 'np': Return Numpy np.ndarray objects.
• add_special_tokens (bool, optional, defaults to True) — Whether or not to encode the sequences with the special tokens relative to their model.
• padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:
  • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).
  • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.
  • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).
• truncation (bool, str or TruncationStrategy, optional, defaults to False) — Activates and controls truncation. Accepts the following values:
  • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
  • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).
• max_length (int, optional) — Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set toNone, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.
• stride (int, optional, defaults to 0) — If set to a number along with max_length, the overflowing tokens returned whenreturn_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
• pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
• return_tensors (str or TensorType, optional) — If set, will return tensors instead of list of python integers. Acceptable values are:
  • 'tf': Return TensorFlow tf.constant objects.
  • 'pt': Return PyTorch torch.Tensor objects.
  • 'np': Return Numpy np.ndarray objects.

Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,__call__ should be used instead.

Given the xpath expression of one particular node (like “/html/body/div/li[1]/div/span[2]”), return a list of tag IDs and corresponding subscripts, taking into account max depth.

MarkupLMProcessor

class transformers.MarkupLMProcessor

< source >

( *args **kwargs )

Parameters

• feature_extractor (MarkupLMFeatureExtractor) — An instance of MarkupLMFeatureExtractor. The feature extractor is a required input.
• tokenizer (MarkupLMTokenizer or MarkupLMTokenizerFast) — An instance of MarkupLMTokenizer or MarkupLMTokenizerFast. The tokenizer is a required input.
• parse_html (bool, optional, defaults to True) — Whether or not to use MarkupLMFeatureExtractor to parse HTML strings into nodes and corresponding xpaths.

Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single processor.

MarkupLMProcessor offers all the functionalities you need to prepare data for the model.

It first uses MarkupLMFeatureExtractor to extract nodes and corresponding xpaths from one or more HTML strings. Next, these are provided to MarkupLMTokenizer or MarkupLMTokenizerFast, which turns them into token-levelinput_ids, attention_mask, token_type_ids, xpath_tags_seq and xpath_subs_seq.

__call__

< source >

( html_strings = None nodes = None xpaths = None node_labels = None questions = None add_special_tokens: bool = True padding: typing.Union[bool, str, transformers.utils.generic.PaddingStrategy] = False truncation: typing.Union[bool, str, transformers.tokenization_utils_base.TruncationStrategy] = None max_length: typing.Optional[int] = None stride: int = 0 pad_to_multiple_of: typing.Optional[int] = None return_token_type_ids: typing.Optional[bool] = None return_attention_mask: typing.Optional[bool] = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None **kwargs )

This method first forwards the html_strings argument to call(). Next, it passes the nodes and xpaths along with the additional arguments to __call__() and returns the output.

Optionally, one can also provide a text argument which is passed along as first sequence.

Please refer to the docstring of the above two methods for more information.

MarkupLMModel

class transformers.MarkupLMModel

< source >

( config add_pooling_layer = True )

Parameters

• config (MarkupLMModel) — 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 thefrom_pretrained() method to load the model weights.
• add_pooling_layer (bool, optional, defaults to True) — Whether to add a pooling layer

The bare Markuplm Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None xpath_tags_seq: typing.Optional[torch.LongTensor] = None xpath_subs_seq: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• xpath_tags_seq (torch.LongTensor of shape (batch_size, sequence_length, config.max_depth), optional) — Tag IDs for each token in the input sequence, padded up to config.max_depth.
• xpath_subs_seq (torch.LongTensor of shape (batch_size, sequence_length, config.max_depth), optional) — Subscript IDs for each token in the input sequence, padded up to config.max_depth.
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
  • 1 indicates the head is not masked,
  • 0 indicates the head is masked.
• inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
• output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MarkupLMConfig) 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, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
• cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True 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 (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.
  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally ifconfig.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_valuesinput) to speed up sequential decoding.

The MarkupLMModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

from transformers import AutoProcessor, MarkupLMModel

processor = AutoProcessor.from_pretrained("microsoft/markuplm-base") model = MarkupLMModel.from_pretrained("microsoft/markuplm-base")

html_string = " Page Title "

encoding = processor(html_string, return_tensors="pt")

outputs = model(**encoding) last_hidden_states = outputs.last_hidden_state list(last_hidden_states.shape) [1, 4, 768]

MarkupLMForSequenceClassification

class transformers.MarkupLMForSequenceClassification

< source >

( config )

Parameters

• config (MarkupLMForSequenceClassification) — 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 thefrom_pretrained() method to load the model weights.

MarkupLM 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 PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None xpath_tags_seq: typing.Optional[torch.Tensor] = None xpath_subs_seq: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• xpath_tags_seq (torch.Tensor of shape (batch_size, sequence_length, config.max_depth), optional) — Tag IDs for each token in the input sequence, padded up to config.max_depth.
• xpath_subs_seq (torch.Tensor of shape (batch_size, sequence_length, config.max_depth), optional) — Subscript IDs for each token in the input sequence, padded up to config.max_depth.
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.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.n_positions - 1].
  What are position IDs?
• head_mask (torch.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 (torch.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.
• labels (torch.Tensor 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), Ifconfig.num_labels > 1 a classification loss is computed (Cross-Entropy).
• output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

A transformers.modeling_outputs.SequenceClassifierOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MarkupLMConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
• logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The MarkupLMForSequenceClassification forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

from transformers import AutoProcessor, AutoModelForSequenceClassification import torch

processor = AutoProcessor.from_pretrained("microsoft/markuplm-base") model = AutoModelForSequenceClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)

html_string = " Page Title " encoding = processor(html_string, return_tensors="pt")

with torch.no_grad(): ... outputs = model(**encoding)

loss = outputs.loss logits = outputs.logits

MarkupLMForTokenClassification

class transformers.MarkupLMForTokenClassification

< source >

( config )

Parameters

• config (MarkupLMForTokenClassification) — 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 thefrom_pretrained() method to load the model weights.

MarkupLM Model with a token_classification head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None xpath_tags_seq: typing.Optional[torch.Tensor] = None xpath_subs_seq: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• xpath_tags_seq (torch.Tensor of shape (batch_size, sequence_length, config.max_depth), optional) — Tag IDs for each token in the input sequence, padded up to config.max_depth.
• xpath_subs_seq (torch.Tensor of shape (batch_size, sequence_length, config.max_depth), optional) — Subscript IDs for each token in the input sequence, padded up to config.max_depth.
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.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.n_positions - 1].
  What are position IDs?
• head_mask (torch.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 (torch.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.
• labels (torch.Tensor of shape (batch_size, sequence_length), optional) — Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1].
• output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

A transformers.modeling_outputs.MaskedLMOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MarkupLMConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Masked language modeling (MLM) loss.
• logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The MarkupLMForTokenClassification forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

from transformers import AutoProcessor, AutoModelForTokenClassification import torch

processor = AutoProcessor.from_pretrained("microsoft/markuplm-base") processor.parse_html = False model = AutoModelForTokenClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)

nodes = ["hello", "world"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"] node_labels = [1, 2] encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")

with torch.no_grad(): ... outputs = model(**encoding)

loss = outputs.loss logits = outputs.logits

MarkupLMForQuestionAnswering

class transformers.MarkupLMForQuestionAnswering

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( config )

Parameters

• config (MarkupLMForQuestionAnswering) — 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 thefrom_pretrained() method to load the model weights.

The Markuplm transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

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( input_ids: typing.Optional[torch.Tensor] = None xpath_tags_seq: typing.Optional[torch.Tensor] = None xpath_subs_seq: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None start_positions: typing.Optional[torch.Tensor] = None end_positions: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• xpath_tags_seq (torch.Tensor of shape (batch_size, sequence_length, config.max_depth), optional) — Tag IDs for each token in the input sequence, padded up to config.max_depth.
• xpath_subs_seq (torch.Tensor of shape (batch_size, sequence_length, config.max_depth), optional) — Subscript IDs for each token in the input sequence, padded up to config.max_depth.
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• token_type_ids (torch.Tensor of shape (batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
  • 0 corresponds to a sentence A token,
  • 1 corresponds to a sentence B token.
    What are token type IDs?
• position_ids (torch.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.n_positions - 1].
  What are position IDs?
• head_mask (torch.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 (torch.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.
• start_positions (torch.Tensor of shape (batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
• end_positions (torch.Tensor of shape (batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
• output_attentions (bool, optional) — Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail.
• output_hidden_states (bool, optional) — Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail.
• return_dict (bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MarkupLMConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
• start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
• end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The MarkupLMForQuestionAnswering forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

from transformers import AutoProcessor, MarkupLMForQuestionAnswering import torch

processor = AutoProcessor.from_pretrained("microsoft/markuplm-base-finetuned-websrc") model = MarkupLMForQuestionAnswering.from_pretrained("microsoft/markuplm-base-finetuned-websrc")

html_string = " My name is Niels " question = "What's his name?"

encoding = processor(html_string, questions=question, return_tensors="pt")

with torch.no_grad(): ... outputs = model(**encoding)

answer_start_index = outputs.start_logits.argmax() answer_end_index = outputs.end_logits.argmax()

predict_answer_tokens = encoding.input_ids[0, answer_start_index : answer_end_index + 1] processor.decode(predict_answer_tokens).strip() 'Niels'

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