Hugging Face's logo

Transformers documentation

mLUKE

You are viewing v4.40.1 version. A newer version v4.46.3 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

mLUKE

Overview

The mLUKE model was proposed in mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It’s a multilingual extension of the LUKE model trained on the basis of XLM-RoBERTa.

It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks involving reasoning about entities such as named entity recognition, extractive question answering, relation classification, cloze-style knowledge completion.

The abstract from the paper is the following:

Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages with entity representations and show the model consistently outperforms word-based pretrained models in various cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual knowledge more likely than using only word representations.

This model was contributed by ryo0634. The original code can be found here.

Usage tips

One can directly plug in the weights of mLUKE into a LUKE model, like so:

from transformers import LukeModel

model = LukeModel.from_pretrained("studio-ousia/mluke-base")

Note that mLUKE has its own tokenizer, MLukeTokenizer. You can initialize it as follows:

from transformers import MLukeTokenizer

tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base")

As mLUKE’s architecture is equivalent to that of LUKE, one can refer to LUKE’s documentation page for all tips, code examples and notebooks.

MLukeTokenizer

class transformers.MLukeTokenizer

< >

( vocab_file entity_vocab_file bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' task = None max_entity_length = 32 max_mention_length = 30 entity_token_1 = '<ent>' entity_token_2 = '<ent2>' entity_unk_token = '[UNK]' entity_pad_token = '[PAD]' entity_mask_token = '[MASK]' entity_mask2_token = '[MASK2]' sp_model_kwargs: Optional = None **kwargs )

Parameters

  • vocab_file (str) — Path to the vocabulary file.
  • entity_vocab_file (str) — Path to the entity vocabulary file.
  • 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.
  • task (str, optional) — Task for which you want to prepare sequences. One of "entity_classification", "entity_pair_classification", or "entity_span_classification". If you specify this argument, the entity sequence is automatically created based on the given entity span(s).
  • max_entity_length (int, optional, defaults to 32) — The maximum length of entity_ids.
  • max_mention_length (int, optional, defaults to 30) — The maximum number of tokens inside an entity span.
  • entity_token_1 (str, optional, defaults to <ent>) — The special token used to represent an entity span in a word token sequence. This token is only used when task is set to "entity_classification" or "entity_pair_classification".
  • entity_token_2 (str, optional, defaults to <ent2>) — The special token used to represent an entity span in a word token sequence. This token is only used when task is set to "entity_pair_classification".
  • additional_special_tokens (List[str], optional, defaults to ["<s>NOTUSED", "</s>NOTUSED"]) — Additional special tokens used by the tokenizer.
  • sp_model_kwargs (dict, optional) — Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for SentencePiece can be used, among other things, to set:

    • enable_sampling: Enable subword regularization.

    • nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.

      • nbest_size = {0,1}: No sampling is performed.
      • nbest_size > 1: samples from the nbest_size results.
      • nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.

    • alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.
  • sp_model (SentencePieceProcessor) — The SentencePiece processor that is used for every conversion (string, tokens and IDs).

Adapted from XLMRobertaTokenizer and LukeTokenizer. Based on SentencePiece.

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

__call__

< >

( text: Union text_pair: Union = None entity_spans: Union = None entity_spans_pair: Union = None entities: Union = None entities_pair: Union = None add_special_tokens: bool = True padding: Union = False truncation: Union = None max_length: Optional = None max_entity_length: Optional = None stride: int = 0 is_split_into_words: Optional = False pad_to_multiple_of: Optional = None return_tensors: Union = None return_token_type_ids: Optional = None return_attention_mask: Optional = None return_overflowing_tokens: bool = False return_special_tokens_mask: bool = False return_offsets_mapping: bool = False return_length: bool = False verbose: bool = True **kwargs ) → BatchEncoding

Parameters

  • text (str, List[str], List[List[str]]) — The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings.
  • text_pair (str, List[str], List[List[str]]) — The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this tokenizer does not support tokenization based on pretokenized strings.
  • entity_spans (List[Tuple[int, int]], List[List[Tuple[int, int]]], optional) — The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify "entity_classification" or "entity_pair_classification" as the task argument in the constructor, the length of each sequence must be 1 or 2, respectively. If you specify entities, the length of each sequence must be equal to the length of each sequence of entities.
  • entity_spans_pair (List[Tuple[int, int]], List[List[Tuple[int, int]]], optional) — The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each with two integers denoting character-based start and end positions of entities. If you specify the task argument in the constructor, this argument is ignored. If you specify entities_pair, the length of each sequence must be equal to the length of each sequence of entities_pair.
  • entities (List[str], List[List[str]], optional) — The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the task argument in the constructor. The length of each sequence must be equal to the length of each sequence of entity_spans. If you specify entity_spans without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity.
  • entities_pair (List[str], List[List[str]], optional) — The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los Angeles). This argument is ignored if you specify the task argument in the constructor. The length of each sequence must be equal to the length of each sequence of entity_spans_pair. If you specify entity_spans_pair without specifying this argument, the entity sequence or the batch of entity sequences is automatically constructed by filling it with the [MASK] entity.
  • max_entity_length (int, optional) — The maximum length of entity_ids.
  • add_special_tokens (bool, optional, defaults to True) — Whether or not to add special tokens when encoding the sequences. This will use the underlying PretrainedTokenizerBase.build_inputs_with_special_tokens function, which defines which tokens are automatically added to the input ids. This is usefull if you want to add bos or eos tokens automatically.
  • padding (bool, str or PaddingStrategy, optional, defaults to False) — Activates and controls padding. Accepts the following values:

    • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single 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 when return_overflowing_tokens=True will contain some tokens from the end of the truncated sequence returned to provide some overlap between truncated and overflowing sequences. The value of this argument defines the number of overlapping tokens.
  • is_split_into_words (bool, optional, defaults to False) — Whether or not the input is already pre-tokenized (e.g., split into words). If set to True, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification.
  • pad_to_multiple_of (int, optional) — If set will pad the sequence to a multiple of the provided value. Requires padding to be activated. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
  • return_tensors (str or 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.
  • return_token_type_ids (bool, optional) — Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer’s default, defined by the return_outputs attribute.

    What are token type IDs?

  • return_attention_mask (bool, optional) — Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer’s default, defined by the return_outputs attribute.

    What are attention masks?

  • return_overflowing_tokens (bool, optional, defaults to False) — Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with truncation_strategy = longest_first or True, an error is raised instead of returning overflowing tokens.
  • return_special_tokens_mask (bool, optional, defaults to False) — Whether or not to return special tokens mask information.
  • return_offsets_mapping (bool, optional, defaults to False) — Whether or not to return (char_start, char_end) for each token.

    This is only available on fast tokenizers inheriting from PreTrainedTokenizerFast, if using Python’s tokenizer, this method will raise NotImplementedError.

  • return_length (bool, optional, defaults to False) — Whether or not to return the lengths of the encoded inputs.
  • verbose (bool, optional, defaults to True) — Whether or not to print more information and warnings. **kwargs — passed to the self.tokenize() method

Returns

BatchEncoding

A BatchEncoding with the following fields:

  • input_ids — List of token ids to be fed to a model.

    What are input IDs?

  • token_type_ids — List of token type ids to be fed to a model (when return_token_type_ids=True or if “token_type_ids” is in self.model_input_names).

    What are token type IDs?

  • attention_mask — List of indices specifying which tokens should be attended to by the model (when return_attention_mask=True or if “attention_mask” is in self.model_input_names).

    What are attention masks?

  • entity_ids — List of entity ids to be fed to a model.

    What are input IDs?

  • entity_position_ids — List of entity positions in the input sequence to be fed to a model.

  • entity_token_type_ids — List of entity token type ids to be fed to a model (when return_token_type_ids=True or if “entity_token_type_ids” is in self.model_input_names).

    What are token type IDs?

  • entity_attention_mask — List of indices specifying which entities should be attended to by the model (when return_attention_mask=True or if “entity_attention_mask” is in self.model_input_names).

    What are attention masks?

  • entity_start_positions — List of the start positions of entities in the word token sequence (when task="entity_span_classification").

  • entity_end_positions — List of the end positions of entities in the word token sequence (when task="entity_span_classification").

  • overflowing_tokens — List of overflowing tokens sequences (when a max_length is specified and return_overflowing_tokens=True).

  • num_truncated_tokens — Number of tokens truncated (when a max_length is specified and return_overflowing_tokens=True).

  • special_tokens_mask — List of 0s and 1s, with 1 specifying added special tokens and 0 specifying regular sequence tokens (when add_special_tokens=True and return_special_tokens_mask=True).

  • length — The length of the inputs (when return_length=True)

Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences, depending on the task you want to prepare them for.

save_vocabulary

< >

( save_directory: str filename_prefix: Optional = None )

< > Update on GitHub

←Mixtral MobileBERT→