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1801 lines
86 KiB
1801 lines
86 KiB
# Copyright 2021 Studio Ousia and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License
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"""Tokenization classes for mLUKE."""
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import itertools
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import json
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import os
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from collections.abc import Mapping
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import numpy as np
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from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers
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from tokenizers.models import Unigram
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from ...tokenization_utils_base import (
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ENCODE_KWARGS_DOCSTRING,
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AddedToken,
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BatchEncoding,
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EncodedInput,
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PaddingStrategy,
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TensorType,
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TextInput,
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TextInputPair,
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TruncationStrategy,
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to_py_obj,
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)
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from ...tokenization_utils_tokenizers import TokenizersBackend
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from ...utils import add_end_docstrings, is_torch_tensor, logging
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logger = logging.get_logger(__name__)
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EntitySpan = tuple[int, int]
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EntitySpanInput = list[EntitySpan]
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Entity = str
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EntityInput = list[Entity]
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SPIECE_UNDERLINE = "▁"
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VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "entity_vocab_file": "entity_vocab.json"}
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ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
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return_token_type_ids (`bool`, *optional*):
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Whether to return token type IDs. If left to the default, will return the token type IDs according to
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the specific tokenizer's default, defined by the `return_outputs` attribute.
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[What are token type IDs?](../glossary#token-type-ids)
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return_attention_mask (`bool`, *optional*):
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Whether to return the attention mask. If left to the default, will return the attention mask according
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to the specific tokenizer's default, defined by the `return_outputs` attribute.
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[What are attention masks?](../glossary#attention-mask)
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return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
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of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
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of returning overflowing tokens.
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return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
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Whether or not to return special tokens mask information.
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return_offsets_mapping (`bool`, *optional*, defaults to `False`):
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Whether or not to return `(char_start, char_end)` for each token.
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This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
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Python's tokenizer, this method will raise `NotImplementedError`.
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return_length (`bool`, *optional*, defaults to `False`):
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Whether or not to return the lengths of the encoded inputs.
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verbose (`bool`, *optional*, defaults to `True`):
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Whether or not to print more information and warnings.
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**kwargs: passed to the `self.tokenize()` method
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Return:
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[`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model.
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[What are input IDs?](../glossary#input-ids)
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- **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
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if *"token_type_ids"* is in `self.model_input_names`).
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[What are token type IDs?](../glossary#token-type-ids)
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
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[What are attention masks?](../glossary#attention-mask)
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- **entity_ids** -- List of entity ids to be fed to a model.
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[What are input IDs?](../glossary#input-ids)
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- **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model.
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- **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when
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`return_token_type_ids=True` or if *"entity_token_type_ids"* is in `self.model_input_names`).
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[What are token type IDs?](../glossary#token-type-ids)
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- **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model
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(when `return_attention_mask=True` or if *"entity_attention_mask"* is in `self.model_input_names`).
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[What are attention masks?](../glossary#attention-mask)
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- **entity_start_positions** -- List of the start positions of entities in the word token sequence (when
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`task="entity_span_classification"`).
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- **entity_end_positions** -- List of the end positions of entities in the word token sequence (when
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`task="entity_span_classification"`).
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- **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
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`return_overflowing_tokens=True`).
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- **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
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`return_overflowing_tokens=True`).
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- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
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regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
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- **length** -- The length of the inputs (when `return_length=True`)
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"""
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class MLukeTokenizer(TokenizersBackend):
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"""
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Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on
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[SentencePiece](https://github.com/google/sentencepiece).
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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entity_vocab_file (`str`):
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Path to the entity vocabulary file.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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task (`str`, *optional*):
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Task for which you want to prepare sequences. One of `"entity_classification"`,
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`"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity
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sequence is automatically created based on the given entity span(s).
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max_entity_length (`int`, *optional*, defaults to 32):
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The maximum length of `entity_ids`.
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max_mention_length (`int`, *optional*, defaults to 30):
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The maximum number of tokens inside an entity span.
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entity_token_1 (`str`, *optional*, defaults to `<ent>`):
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The special token used to represent an entity span in a word token sequence. This token is only used when
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`task` is set to `"entity_classification"` or `"entity_pair_classification"`.
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entity_token_2 (`str`, *optional*, defaults to `<ent2>`):
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The special token used to represent an entity span in a word token sequence. This token is only used when
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`task` is set to `"entity_pair_classification"`.
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additional_special_tokens (`list[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
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Additional special tokens used by the tokenizer.
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sp_model_kwargs (`dict`, *optional*):
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Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
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SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
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to set:
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- `enable_sampling`: Enable subword regularization.
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- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
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- `nbest_size = {0,1}`: No sampling is performed.
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- `nbest_size > 1`: samples from the nbest_size results.
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- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
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using forward-filtering-and-backward-sampling algorithm.
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- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
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BPE-dropout.
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Attributes:
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sp_model (`SentencePieceProcessor`):
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The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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task=None,
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max_entity_length=32,
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max_mention_length=30,
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entity_token_1="<ent>",
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entity_token_2="<ent2>",
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entity_unk_token="[UNK]",
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entity_pad_token="[PAD]",
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entity_mask_token="[MASK]",
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entity_mask2_token="[MASK2]",
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vocab: str | dict | list | None = None,
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entity_vocab: str | dict | list | None = None,
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**kwargs,
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) -> None:
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# Mask token behave like a normal word, i.e. include the space before it
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mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
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# we add 2 special tokens for downstream tasks
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entity_token_1 = (
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AddedToken(entity_token_1, lstrip=False, rstrip=False)
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if isinstance(entity_token_1, str)
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else entity_token_1
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)
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entity_token_2 = (
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AddedToken(entity_token_2, lstrip=False, rstrip=False)
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if isinstance(entity_token_2, str)
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else entity_token_2
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)
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# Handle entity vocab file for backward compatibility
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entity_vocab_file = kwargs.pop("entity_vocab_file", None)
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# Check if vocab/entity_vocab are in kwargs
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if vocab is None and "vocab" in kwargs:
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vocab = kwargs.pop("vocab")
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if entity_vocab is None and "entity_vocab" in kwargs:
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entity_vocab = kwargs.pop("entity_vocab")
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# Build vocab from data (list of (token, score) tuples)
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if isinstance(vocab, list):
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# vocab is list of (token, score) tuples from SentencePieceExtractor
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self._vocab = [(token, float(score)) for token, score in vocab]
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self._vocab_size = len(self._vocab)
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elif vocab is not None:
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self._vocab = vocab
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self._vocab_size = 0
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else:
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# Create minimal vocab with <unk> to satisfy Unigram requirements
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self._vocab = [("<unk>", 0.0)]
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self._vocab_size = 0 # Will be updated when real vocab is loaded
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# Build Unigram tokenizer
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self._tokenizer = Tokenizer(Unigram(self._vocab, unk_id=0))
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# Add SentencePiece-style normalization and pre-tokenization
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self._tokenizer.normalizer = normalizers.Sequence(
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[
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normalizers.Replace("``", '"'),
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normalizers.Replace("''", '"'),
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]
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)
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self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always")
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self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always")
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# Original fairseq vocab and spm vocab must be "aligned":
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# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
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# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
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# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
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# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
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# Mimic fairseq token-to-id alignment for the first 4 tokens
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self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
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# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
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self.fairseq_offset = 1
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self.fairseq_tokens_to_ids["<mask>"] = self._vocab_size + self.fairseq_offset
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self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
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# Load entity vocab
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if entity_vocab is not None:
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self.entity_vocab = entity_vocab
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elif entity_vocab_file is not None:
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with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
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self.entity_vocab = json.load(entity_vocab_handle)
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else:
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# Create minimal entity vocab with required special tokens
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self.entity_vocab = {
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entity_unk_token: 0,
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entity_pad_token: 1,
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entity_mask_token: 2,
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entity_mask2_token: 3,
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}
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for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]:
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if entity_special_token not in self.entity_vocab:
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raise ValueError(
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f"Specified entity special token ``{entity_special_token}`` is not found in entity_vocab."
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)
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self.entity_unk_token_id = self.entity_vocab[entity_unk_token]
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self.entity_pad_token_id = self.entity_vocab[entity_pad_token]
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self.entity_mask_token_id = self.entity_vocab[entity_mask_token]
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self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token]
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self.task = task
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if task is None or task == "entity_span_classification":
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self.max_entity_length = max_entity_length
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elif task == "entity_classification":
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self.max_entity_length = 1
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elif task == "entity_pair_classification":
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self.max_entity_length = 2
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else:
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raise ValueError(
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f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification',"
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" 'entity_span_classification'] only."
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)
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self.max_mention_length = max_mention_length
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# Handle extra/legacy special tokens (v4 compat). The fallback load path can pass
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# `additional_special_tokens` and/or `extra_special_tokens`, with entries serialized as dicts.
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extra_tokens: list[AddedToken | str] = []
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for key in ("extra_special_tokens", "additional_special_tokens"):
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tokens = kwargs.pop(key, None)
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if isinstance(tokens, (list, tuple)):
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for token in tokens:
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extra_tokens.append(AddedToken(**token) if isinstance(token, dict) else token)
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# Ensure MLuke entity tokens are present exactly once.
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seen = {str(token) for token in extra_tokens}
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for token in (entity_token_1, entity_token_2):
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token_str = str(token)
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if token_str not in seen:
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extra_tokens.append(token)
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seen.add(token_str)
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# Also register entity masking/padding tokens so they survive save/load cycles.
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for token in (entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token):
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if token not in seen:
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extra_tokens.append(AddedToken(token, lstrip=False, rstrip=False, normalized=False, special=True))
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seen.add(token)
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kwargs["extra_special_tokens"] = extra_tokens
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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task=task,
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max_entity_length=max_entity_length,
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max_mention_length=max_mention_length,
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entity_token_1=str(entity_token_1),
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entity_token_2=str(entity_token_2),
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entity_unk_token=entity_unk_token,
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entity_pad_token=entity_pad_token,
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entity_mask_token=entity_mask_token,
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entity_mask2_token=entity_mask2_token,
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entity_vocab=entity_vocab if entity_vocab_file is None else None, # Only store if passed as data
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**kwargs,
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)
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# Call _post_init for tokenizers created directly (not from_pretrained)
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self._post_init()
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def _post_init(self):
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"""
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Post-initialization to configure the post-processor for MLuke's special token format.
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"""
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super()._post_init()
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# Ensure the Python-side vocab metadata matches the fast tokenizer backend after loading
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self._vocab_size = self._tokenizer.get_vocab_size(with_added_tokens=False)
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self.fairseq_tokens_to_ids["<mask>"] = self._vocab_size + self.fairseq_offset
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self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
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# Configure post processor for XLM-R/MLuke format:
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# single: <s> X </s>
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# pair: <s> A </s></s> B </s>
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from tokenizers import processors
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single=f"{self.cls_token}:0 $A:0 {self.sep_token}:0",
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pair=f"{self.cls_token}:0 $A:0 {self.sep_token}:0 {self.sep_token}:0 $B:1 {self.sep_token}:1",
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special_tokens=[
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(self.cls_token, self.cls_token_id),
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(self.sep_token, self.sep_token_id),
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],
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)
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@property
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def vocab_size(self):
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return self._vocab_size + self.fairseq_offset + 1 # Add the <mask> token
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _convert_token_to_id(self, token):
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|
"""Converts a token (str) in an id using the vocab."""
|
|
if token in self.fairseq_tokens_to_ids:
|
|
return self.fairseq_tokens_to_ids[token]
|
|
|
|
# Look up token in vocab
|
|
token_id = self._tokenizer.token_to_id(token)
|
|
|
|
# Need to return unknown token if not found (token_to_id returns None)
|
|
return token_id + self.fairseq_offset if token_id is not None else self.unk_token_id
|
|
|
|
def _convert_id_to_token(self, index):
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
|
if index in self.fairseq_ids_to_tokens:
|
|
return self.fairseq_ids_to_tokens[index]
|
|
token = self._tokenizer.id_to_token(index - self.fairseq_offset)
|
|
return token if token is not None else self.unk_token
|
|
|
|
def convert_tokens_to_string(self, tokens):
|
|
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
|
|
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
|
|
return out_string
|
|
|
|
def num_special_tokens_to_add(self, pair: bool = False) -> int:
|
|
"""
|
|
Returns the number of added tokens when encoding a sequence with special tokens.
|
|
|
|
Args:
|
|
pair (`bool`, *optional*, defaults to `False`):
|
|
Whether the number of added tokens should be computed in the case of a sequence pair or a single
|
|
sequence.
|
|
|
|
Returns:
|
|
`int`: Number of special tokens added to sequences.
|
|
"""
|
|
return 4 if pair else 2
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def __call__(
|
|
self,
|
|
text: TextInput | list[TextInput],
|
|
text_pair: TextInput | list[TextInput] | None = None,
|
|
entity_spans: EntitySpanInput | list[EntitySpanInput] | None = None,
|
|
entity_spans_pair: EntitySpanInput | list[EntitySpanInput] | None = None,
|
|
entities: EntityInput | list[EntityInput] | None = None,
|
|
entities_pair: EntityInput | list[EntityInput] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding: bool | str | PaddingStrategy = False,
|
|
truncation: bool | str | TruncationStrategy = None,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
stride: int = 0,
|
|
is_split_into_words: bool | None = False,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = 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:
|
|
# Check for seq2seq parameters that are not supported with entity-aware encoding
|
|
if kwargs.get("text_target") is not None or kwargs.get("text_pair_target") is not None:
|
|
if entity_spans is not None or entities is not None or self.task is not None:
|
|
raise NotImplementedError(
|
|
"text_target and text_pair_target are not supported when using entity-aware encoding. "
|
|
"Please use the tokenizer without entities for seq2seq tasks."
|
|
)
|
|
# Delegate to parent for seq2seq encoding
|
|
return super().__call__(
|
|
text=text,
|
|
text_pair=text_pair,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
is_split_into_words=is_split_into_words,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
"""
|
|
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.
|
|
|
|
Args:
|
|
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`.
|
|
"""
|
|
# Input type checking for clearer error
|
|
is_valid_single_text = isinstance(text, str)
|
|
is_valid_batch_text = isinstance(text, (list, tuple)) and (
|
|
len(text) == 0 or isinstance(text[0], (str, list, tuple))
|
|
)
|
|
if not (is_valid_single_text or is_valid_batch_text):
|
|
raise ValueError("text input must be of type `str` (single example) or `list[str]` (batch).")
|
|
|
|
is_valid_single_text_pair = isinstance(text_pair, str)
|
|
is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (
|
|
len(text_pair) == 0 or isinstance(text_pair[0], str)
|
|
)
|
|
if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
|
|
raise ValueError("text_pair input must be of type `str` (single example) or `list[str]` (batch).")
|
|
|
|
is_batched = bool(isinstance(text, (list, tuple)))
|
|
|
|
# Get proper padding and truncation strategies
|
|
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
if is_batched:
|
|
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
|
|
if entities is None:
|
|
batch_entities_or_entities_pairs = None
|
|
else:
|
|
batch_entities_or_entities_pairs = (
|
|
list(zip(entities, entities_pair)) if entities_pair is not None else entities
|
|
)
|
|
|
|
if entity_spans is None:
|
|
batch_entity_spans_or_entity_spans_pairs = None
|
|
else:
|
|
batch_entity_spans_or_entity_spans_pairs = (
|
|
list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans
|
|
)
|
|
|
|
return self._batch_encode_plus(
|
|
batch_text_or_text_pairs=batch_text_or_text_pairs,
|
|
batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs,
|
|
batch_entities_or_entities_pairs=batch_entities_or_entities_pairs,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
stride=stride,
|
|
is_split_into_words=is_split_into_words,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
else:
|
|
return self._encode_plus(
|
|
text=text,
|
|
text_pair=text_pair,
|
|
entity_spans=entity_spans,
|
|
entity_spans_pair=entity_spans_pair,
|
|
entities=entities,
|
|
entities_pair=entities_pair,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
stride=stride,
|
|
is_split_into_words=is_split_into_words,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
def _encode_plus(
|
|
self,
|
|
text: TextInput,
|
|
text_pair: TextInput | None = None,
|
|
entity_spans: EntitySpanInput | None = None,
|
|
entity_spans_pair: EntitySpanInput | None = None,
|
|
entities: EntityInput | None = None,
|
|
entities_pair: EntityInput | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
stride: int = 0,
|
|
is_split_into_words: bool | None = False,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = 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:
|
|
if (
|
|
entity_spans is None
|
|
and entity_spans_pair is None
|
|
and entities is None
|
|
and entities_pair is None
|
|
and self.task is None
|
|
):
|
|
return super()._encode_plus(
|
|
text=text,
|
|
text_pair=text_pair,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
is_split_into_words=is_split_into_words,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
if return_offsets_mapping:
|
|
raise NotImplementedError(
|
|
"return_offset_mapping is not available when using Python tokenizers. "
|
|
"To use this feature, change your tokenizer to one deriving from "
|
|
"transformers.PreTrainedTokenizerFast. "
|
|
"More information on available tokenizers at "
|
|
"https://github.com/huggingface/transformers/pull/2674"
|
|
)
|
|
|
|
if is_split_into_words:
|
|
raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
|
|
|
|
(
|
|
first_ids,
|
|
second_ids,
|
|
first_entity_ids,
|
|
second_entity_ids,
|
|
first_entity_token_spans,
|
|
second_entity_token_spans,
|
|
) = self._create_input_sequence(
|
|
text=text,
|
|
text_pair=text_pair,
|
|
entities=entities,
|
|
entities_pair=entities_pair,
|
|
entity_spans=entity_spans,
|
|
entity_spans_pair=entity_spans_pair,
|
|
**kwargs,
|
|
)
|
|
|
|
# prepare_for_model will create the attention_mask and token_type_ids
|
|
return self.prepare_for_model(
|
|
first_ids,
|
|
pair_ids=second_ids,
|
|
entity_ids=first_entity_ids,
|
|
pair_entity_ids=second_entity_ids,
|
|
entity_token_spans=first_entity_token_spans,
|
|
pair_entity_token_spans=second_entity_token_spans,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=padding_strategy.value,
|
|
truncation=truncation_strategy.value,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_tensors=return_tensors,
|
|
prepend_batch_axis=True,
|
|
return_attention_mask=return_attention_mask,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
)
|
|
|
|
def _batch_encode_plus(
|
|
self,
|
|
batch_text_or_text_pairs: list[TextInput] | list[TextInputPair],
|
|
batch_entity_spans_or_entity_spans_pairs: list[EntitySpanInput]
|
|
| list[tuple[EntitySpanInput, EntitySpanInput]]
|
|
| None = None,
|
|
batch_entities_or_entities_pairs: list[EntityInput] | list[tuple[EntityInput, EntityInput]] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
stride: int = 0,
|
|
is_split_into_words: bool | None = False,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = 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:
|
|
if (
|
|
batch_entity_spans_or_entity_spans_pairs is None
|
|
and batch_entities_or_entities_pairs is None
|
|
and self.task is None
|
|
):
|
|
if batch_text_or_text_pairs and isinstance(batch_text_or_text_pairs[0], (tuple, list)):
|
|
texts, text_pairs = zip(*batch_text_or_text_pairs)
|
|
texts = list(texts)
|
|
text_pairs = list(text_pairs)
|
|
else:
|
|
texts = batch_text_or_text_pairs
|
|
text_pairs = None
|
|
|
|
return super()._encode_plus(
|
|
text=texts,
|
|
text_pair=text_pairs,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
stride=stride,
|
|
is_split_into_words=is_split_into_words,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_tensors=return_tensors,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_attention_mask=return_attention_mask,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_offsets_mapping=return_offsets_mapping,
|
|
return_length=return_length,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
if return_offsets_mapping:
|
|
raise NotImplementedError(
|
|
"return_offset_mapping is not available when using Python tokenizers. "
|
|
"To use this feature, change your tokenizer to one deriving from "
|
|
"transformers.PreTrainedTokenizerFast."
|
|
)
|
|
|
|
if is_split_into_words:
|
|
raise NotImplementedError("is_split_into_words is not supported in this tokenizer.")
|
|
|
|
# input_ids is a list of tuples (one for each example in the batch)
|
|
input_ids = []
|
|
entity_ids = []
|
|
entity_token_spans = []
|
|
for index, text_or_text_pair in enumerate(batch_text_or_text_pairs):
|
|
if not isinstance(text_or_text_pair, (list, tuple)):
|
|
text, text_pair = text_or_text_pair, None
|
|
else:
|
|
text, text_pair = text_or_text_pair
|
|
|
|
entities, entities_pair = None, None
|
|
if batch_entities_or_entities_pairs is not None:
|
|
entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
|
|
if entities_or_entities_pairs:
|
|
if isinstance(entities_or_entities_pairs[0], str):
|
|
entities, entities_pair = entities_or_entities_pairs, None
|
|
else:
|
|
entities, entities_pair = entities_or_entities_pairs
|
|
|
|
entity_spans, entity_spans_pair = None, None
|
|
if batch_entity_spans_or_entity_spans_pairs is not None:
|
|
entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
|
|
if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(
|
|
entity_spans_or_entity_spans_pairs[0], list
|
|
):
|
|
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
|
|
else:
|
|
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None
|
|
|
|
(
|
|
first_ids,
|
|
second_ids,
|
|
first_entity_ids,
|
|
second_entity_ids,
|
|
first_entity_token_spans,
|
|
second_entity_token_spans,
|
|
) = self._create_input_sequence(
|
|
text=text,
|
|
text_pair=text_pair,
|
|
entities=entities,
|
|
entities_pair=entities_pair,
|
|
entity_spans=entity_spans,
|
|
entity_spans_pair=entity_spans_pair,
|
|
**kwargs,
|
|
)
|
|
input_ids.append((first_ids, second_ids))
|
|
entity_ids.append((first_entity_ids, second_entity_ids))
|
|
entity_token_spans.append((first_entity_token_spans, second_entity_token_spans))
|
|
|
|
batch_outputs = self._batch_prepare_for_model(
|
|
input_ids,
|
|
batch_entity_ids_pairs=entity_ids,
|
|
batch_entity_token_spans_pairs=entity_token_spans,
|
|
add_special_tokens=add_special_tokens,
|
|
padding_strategy=padding_strategy,
|
|
truncation_strategy=truncation_strategy,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_attention_mask=return_attention_mask,
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_length=return_length,
|
|
return_tensors=return_tensors,
|
|
verbose=verbose,
|
|
)
|
|
|
|
return BatchEncoding(batch_outputs)
|
|
|
|
def _check_entity_input_format(self, entities: EntityInput | None, entity_spans: EntitySpanInput | None):
|
|
if not isinstance(entity_spans, list):
|
|
raise TypeError("entity_spans should be given as a list")
|
|
elif len(entity_spans) > 0 and not isinstance(entity_spans[0], tuple):
|
|
raise ValueError(
|
|
"entity_spans should be given as a list of tuples containing the start and end character indices"
|
|
)
|
|
|
|
if entities is not None:
|
|
if not isinstance(entities, list):
|
|
raise ValueError("If you specify entities, they should be given as a list")
|
|
|
|
if len(entities) > 0 and not isinstance(entities[0], str):
|
|
raise ValueError("If you specify entities, they should be given as a list of entity names")
|
|
|
|
if len(entities) != len(entity_spans):
|
|
raise ValueError("If you specify entities, entities and entity_spans must be the same length")
|
|
|
|
def _create_input_sequence(
|
|
self,
|
|
text: TextInput,
|
|
text_pair: TextInput | None = None,
|
|
entities: EntityInput | None = None,
|
|
entities_pair: EntityInput | None = None,
|
|
entity_spans: EntitySpanInput | None = None,
|
|
entity_spans_pair: EntitySpanInput | None = None,
|
|
**kwargs,
|
|
) -> tuple[list, list, list, list, list, list]:
|
|
def get_input_ids(text):
|
|
# Use the underlying tokenizer directly to avoid infinite recursion
|
|
# Then convert to fairseq-aligned IDs
|
|
tokens = self._tokenizer.encode(text, add_special_tokens=False).tokens
|
|
return self.convert_tokens_to_ids(tokens)
|
|
|
|
def get_input_ids_and_entity_token_spans(text, entity_spans):
|
|
if entity_spans is None:
|
|
return get_input_ids(text), None
|
|
|
|
cur = 0
|
|
input_ids = []
|
|
entity_token_spans = [None] * len(entity_spans)
|
|
|
|
split_char_positions = sorted(frozenset(itertools.chain(*entity_spans)))
|
|
char_pos2token_pos = {}
|
|
|
|
for split_char_position in split_char_positions:
|
|
orig_split_char_position = split_char_position
|
|
if (
|
|
split_char_position > 0 and text[split_char_position - 1] == " "
|
|
): # whitespace should be prepended to the following token
|
|
split_char_position -= 1
|
|
if cur != split_char_position:
|
|
input_ids += get_input_ids(text[cur:split_char_position])
|
|
cur = split_char_position
|
|
char_pos2token_pos[orig_split_char_position] = len(input_ids)
|
|
|
|
input_ids += get_input_ids(text[cur:])
|
|
|
|
entity_token_spans = [
|
|
(char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans
|
|
]
|
|
|
|
return input_ids, entity_token_spans
|
|
|
|
first_ids, second_ids = None, None
|
|
first_entity_ids, second_entity_ids = None, None
|
|
first_entity_token_spans, second_entity_token_spans = None, None
|
|
|
|
if self.task is None:
|
|
if entity_spans is None:
|
|
first_ids = get_input_ids(text)
|
|
else:
|
|
self._check_entity_input_format(entities, entity_spans)
|
|
|
|
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
|
|
if entities is None:
|
|
first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
|
|
else:
|
|
first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities]
|
|
|
|
if text_pair is not None:
|
|
if entity_spans_pair is None:
|
|
second_ids = get_input_ids(text_pair)
|
|
else:
|
|
self._check_entity_input_format(entities_pair, entity_spans_pair)
|
|
|
|
second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans(
|
|
text_pair, entity_spans_pair
|
|
)
|
|
if entities_pair is None:
|
|
second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair)
|
|
else:
|
|
second_entity_ids = [
|
|
self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair
|
|
]
|
|
|
|
elif self.task == "entity_classification":
|
|
if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)):
|
|
raise ValueError(
|
|
"Entity spans should be a list containing a single tuple "
|
|
"containing the start and end character indices of an entity"
|
|
)
|
|
first_entity_ids = [self.entity_mask_token_id]
|
|
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
|
|
|
|
# add special tokens to input ids
|
|
entity_token_start, entity_token_end = first_entity_token_spans[0]
|
|
first_ids = (
|
|
first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:]
|
|
)
|
|
first_ids = (
|
|
first_ids[:entity_token_start]
|
|
+ [self.additional_special_tokens_ids[0]]
|
|
+ first_ids[entity_token_start:]
|
|
)
|
|
first_entity_token_spans = [(entity_token_start, entity_token_end + 2)]
|
|
|
|
elif self.task == "entity_pair_classification":
|
|
if not (
|
|
isinstance(entity_spans, list)
|
|
and len(entity_spans) == 2
|
|
and isinstance(entity_spans[0], tuple)
|
|
and isinstance(entity_spans[1], tuple)
|
|
):
|
|
raise ValueError(
|
|
"Entity spans should be provided as a list of two tuples, "
|
|
"each tuple containing the start and end character indices of an entity"
|
|
)
|
|
|
|
head_span, tail_span = entity_spans
|
|
first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id]
|
|
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
|
|
|
|
head_token_span, tail_token_span = first_entity_token_spans
|
|
token_span_with_special_token_ids = [
|
|
(head_token_span, self.additional_special_tokens_ids[0]),
|
|
(tail_token_span, self.additional_special_tokens_ids[1]),
|
|
]
|
|
if head_token_span[0] < tail_token_span[0]:
|
|
first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2)
|
|
first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4)
|
|
token_span_with_special_token_ids.reverse()
|
|
else:
|
|
first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4)
|
|
first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2)
|
|
|
|
for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids:
|
|
first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:]
|
|
first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:]
|
|
|
|
elif self.task == "entity_span_classification":
|
|
if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)):
|
|
raise ValueError(
|
|
"Entity spans should be provided as a list of tuples, "
|
|
"each tuple containing the start and end character indices of an entity"
|
|
)
|
|
|
|
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
|
|
first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
|
|
|
|
else:
|
|
raise ValueError(f"Task {self.task} not supported")
|
|
|
|
return (
|
|
first_ids,
|
|
second_ids,
|
|
first_entity_ids,
|
|
second_entity_ids,
|
|
first_entity_token_spans,
|
|
second_entity_token_spans,
|
|
)
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def _batch_prepare_for_model(
|
|
self,
|
|
batch_ids_pairs: list[tuple[list[int], None]],
|
|
batch_entity_ids_pairs: list[tuple[list[int] | None, list[int] | None]],
|
|
batch_entity_token_spans_pairs: list[tuple[list[tuple[int, int]] | None, list[tuple[int, int]] | None]],
|
|
add_special_tokens: bool = True,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
) -> BatchEncoding:
|
|
"""
|
|
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
|
|
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
|
|
manages a moving window (with user defined stride) for overflowing tokens
|
|
|
|
|
|
Args:
|
|
batch_ids_pairs: list of tokenized input ids or input ids pairs
|
|
batch_entity_ids_pairs: list of entity ids or entity ids pairs
|
|
batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
|
|
max_entity_length: The maximum length of the entity sequence.
|
|
"""
|
|
|
|
batch_outputs = {}
|
|
for input_ids, entity_ids, entity_token_span_pairs in zip(
|
|
batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
|
|
):
|
|
first_ids, second_ids = input_ids
|
|
first_entity_ids, second_entity_ids = entity_ids
|
|
first_entity_token_spans, second_entity_token_spans = entity_token_span_pairs
|
|
outputs = self.prepare_for_model(
|
|
first_ids,
|
|
second_ids,
|
|
entity_ids=first_entity_ids,
|
|
pair_entity_ids=second_entity_ids,
|
|
entity_token_spans=first_entity_token_spans,
|
|
pair_entity_token_spans=second_entity_token_spans,
|
|
add_special_tokens=add_special_tokens,
|
|
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
|
|
truncation=truncation_strategy.value,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
stride=stride,
|
|
pad_to_multiple_of=None, # we pad in batch afterward
|
|
padding_side=None, # we pad in batch afterward
|
|
return_attention_mask=False, # we pad in batch afterward
|
|
return_token_type_ids=return_token_type_ids,
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
|
return_length=return_length,
|
|
return_tensors=None, # We convert the whole batch to tensors at the end
|
|
prepend_batch_axis=False,
|
|
verbose=verbose,
|
|
)
|
|
|
|
for key, value in outputs.items():
|
|
if key not in batch_outputs:
|
|
batch_outputs[key] = []
|
|
batch_outputs[key].append(value)
|
|
|
|
batch_outputs = self.pad(
|
|
batch_outputs,
|
|
padding=padding_strategy.value,
|
|
max_length=max_length,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_attention_mask=return_attention_mask,
|
|
)
|
|
|
|
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
|
|
|
|
return batch_outputs
|
|
|
|
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
|
|
def prepare_for_model(
|
|
self,
|
|
ids: list[int],
|
|
pair_ids: list[int] | None = None,
|
|
entity_ids: list[int] | None = None,
|
|
pair_entity_ids: list[int] | None = None,
|
|
entity_token_spans: list[tuple[int, int]] | None = None,
|
|
pair_entity_token_spans: list[tuple[int, int]] | None = None,
|
|
add_special_tokens: bool = True,
|
|
padding: bool | str | PaddingStrategy = False,
|
|
truncation: bool | str | TruncationStrategy = None,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
stride: int = 0,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
return_token_type_ids: bool | None = None,
|
|
return_attention_mask: bool | None = None,
|
|
return_overflowing_tokens: bool = False,
|
|
return_special_tokens_mask: bool = False,
|
|
return_offsets_mapping: bool = False,
|
|
return_length: bool = False,
|
|
verbose: bool = True,
|
|
prepend_batch_axis: bool = False,
|
|
**kwargs,
|
|
) -> BatchEncoding:
|
|
"""
|
|
Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
|
|
entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing
|
|
while taking into account the special tokens and manages a moving window (with user defined stride) for
|
|
overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first*
|
|
or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an
|
|
error.
|
|
|
|
Args:
|
|
ids (`list[int]`):
|
|
Tokenized input ids of the first sequence.
|
|
pair_ids (`list[int]`, *optional*):
|
|
Tokenized input ids of the second sequence.
|
|
entity_ids (`list[int]`, *optional*):
|
|
Entity ids of the first sequence.
|
|
pair_entity_ids (`list[int]`, *optional*):
|
|
Entity ids of the second sequence.
|
|
entity_token_spans (`list[tuple[int, int]]`, *optional*):
|
|
Entity spans of the first sequence.
|
|
pair_entity_token_spans (`list[tuple[int, int]]`, *optional*):
|
|
Entity spans of the second sequence.
|
|
max_entity_length (`int`, *optional*):
|
|
The maximum length of the entity sequence.
|
|
"""
|
|
|
|
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
|
|
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
|
|
padding=padding,
|
|
truncation=truncation,
|
|
max_length=max_length,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
verbose=verbose,
|
|
**kwargs,
|
|
)
|
|
|
|
# Compute lengths
|
|
pair = bool(pair_ids is not None)
|
|
len_ids = len(ids)
|
|
len_pair_ids = len(pair_ids) if pair else 0
|
|
|
|
if return_token_type_ids and not add_special_tokens:
|
|
raise ValueError(
|
|
"Asking to return token_type_ids while setting add_special_tokens to False "
|
|
"results in an undefined behavior. Please set add_special_tokens to True or "
|
|
"set return_token_type_ids to None."
|
|
)
|
|
if (
|
|
return_overflowing_tokens
|
|
and truncation_strategy == TruncationStrategy.LONGEST_FIRST
|
|
and pair_ids is not None
|
|
):
|
|
raise ValueError(
|
|
"Not possible to return overflowing tokens for pair of sequences with the "
|
|
"`longest_first`. Please select another truncation strategy than `longest_first`, "
|
|
"for instance `only_second` or `only_first`."
|
|
)
|
|
|
|
# Load from model defaults
|
|
if return_token_type_ids is None:
|
|
return_token_type_ids = "token_type_ids" in self.model_input_names
|
|
if return_attention_mask is None:
|
|
return_attention_mask = "attention_mask" in self.model_input_names
|
|
|
|
encoded_inputs = {}
|
|
|
|
# Compute the total size of the returned word encodings
|
|
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
|
|
|
|
# Truncation: Handle max sequence length and max_entity_length
|
|
overflowing_tokens = []
|
|
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
|
|
# truncate words up to max_length
|
|
ids, pair_ids, overflowing_tokens = self.truncate_sequences(
|
|
ids,
|
|
pair_ids=pair_ids,
|
|
num_tokens_to_remove=total_len - max_length,
|
|
truncation_strategy=truncation_strategy,
|
|
stride=stride,
|
|
)
|
|
|
|
if return_overflowing_tokens:
|
|
encoded_inputs["overflowing_tokens"] = overflowing_tokens
|
|
encoded_inputs["num_truncated_tokens"] = total_len - max_length
|
|
|
|
# Add special tokens
|
|
if add_special_tokens:
|
|
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
|
|
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
|
|
entity_token_offset = 1 # 1 * <s> token
|
|
pair_entity_token_offset = len(ids) + 3 # 1 * <s> token & 2 * <sep> tokens
|
|
else:
|
|
sequence = ids + pair_ids if pair else ids
|
|
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
|
|
entity_token_offset = 0
|
|
pair_entity_token_offset = len(ids)
|
|
|
|
# Build output dictionary
|
|
encoded_inputs["input_ids"] = sequence
|
|
if return_token_type_ids:
|
|
encoded_inputs["token_type_ids"] = token_type_ids
|
|
if return_special_tokens_mask:
|
|
if add_special_tokens:
|
|
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
|
|
else:
|
|
encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
|
|
|
|
# Set max entity length
|
|
if not max_entity_length:
|
|
max_entity_length = self.max_entity_length
|
|
|
|
if entity_ids is not None:
|
|
total_entity_len = 0
|
|
num_invalid_entities = 0
|
|
valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)]
|
|
valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)]
|
|
|
|
total_entity_len += len(valid_entity_ids)
|
|
num_invalid_entities += len(entity_ids) - len(valid_entity_ids)
|
|
|
|
valid_pair_entity_ids, valid_pair_entity_token_spans = None, None
|
|
if pair_entity_ids is not None:
|
|
valid_pair_entity_ids = [
|
|
ent_id
|
|
for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans)
|
|
if span[1] <= len(pair_ids)
|
|
]
|
|
valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)]
|
|
total_entity_len += len(valid_pair_entity_ids)
|
|
num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids)
|
|
|
|
if num_invalid_entities != 0:
|
|
logger.warning(
|
|
f"{num_invalid_entities} entities are ignored because their entity spans are invalid due to the"
|
|
" truncation of input tokens"
|
|
)
|
|
|
|
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length:
|
|
# truncate entities up to max_entity_length
|
|
valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences(
|
|
valid_entity_ids,
|
|
pair_ids=valid_pair_entity_ids,
|
|
num_tokens_to_remove=total_entity_len - max_entity_length,
|
|
truncation_strategy=truncation_strategy,
|
|
stride=stride,
|
|
)
|
|
valid_entity_token_spans = valid_entity_token_spans[: len(valid_entity_ids)]
|
|
if valid_pair_entity_token_spans is not None:
|
|
valid_pair_entity_token_spans = valid_pair_entity_token_spans[: len(valid_pair_entity_ids)]
|
|
|
|
if return_overflowing_tokens:
|
|
encoded_inputs["overflowing_entities"] = overflowing_entities
|
|
encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length
|
|
|
|
final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids
|
|
encoded_inputs["entity_ids"] = list(final_entity_ids)
|
|
entity_position_ids = []
|
|
entity_start_positions = []
|
|
entity_end_positions = []
|
|
for token_spans, offset in (
|
|
(valid_entity_token_spans, entity_token_offset),
|
|
(valid_pair_entity_token_spans, pair_entity_token_offset),
|
|
):
|
|
if token_spans is not None:
|
|
for start, end in token_spans:
|
|
start += offset
|
|
end += offset
|
|
position_ids = list(range(start, end))[: self.max_mention_length]
|
|
position_ids += [-1] * (self.max_mention_length - end + start)
|
|
entity_position_ids.append(position_ids)
|
|
entity_start_positions.append(start)
|
|
entity_end_positions.append(end - 1)
|
|
|
|
encoded_inputs["entity_position_ids"] = entity_position_ids
|
|
if self.task == "entity_span_classification":
|
|
encoded_inputs["entity_start_positions"] = entity_start_positions
|
|
encoded_inputs["entity_end_positions"] = entity_end_positions
|
|
|
|
if return_token_type_ids:
|
|
encoded_inputs["entity_token_type_ids"] = [0] * len(encoded_inputs["entity_ids"])
|
|
|
|
# Check lengths
|
|
self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
|
|
|
|
# Padding
|
|
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
|
|
encoded_inputs = self.pad(
|
|
encoded_inputs,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
padding=padding_strategy.value,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_attention_mask=return_attention_mask,
|
|
)
|
|
|
|
if return_length:
|
|
encoded_inputs["length"] = len(encoded_inputs["input_ids"])
|
|
|
|
batch_outputs = BatchEncoding(
|
|
encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
|
|
)
|
|
|
|
return batch_outputs
|
|
|
|
def pad(
|
|
self,
|
|
encoded_inputs: BatchEncoding
|
|
| list[BatchEncoding]
|
|
| dict[str, EncodedInput]
|
|
| dict[str, list[EncodedInput]]
|
|
| list[dict[str, EncodedInput]],
|
|
padding: bool | str | PaddingStrategy = True,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_attention_mask: bool | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
verbose: bool = True,
|
|
) -> BatchEncoding:
|
|
"""
|
|
Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
|
|
in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with
|
|
`self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
|
|
are dictionary of numpy arrays or PyTorch tensors the result will use the same type unless
|
|
you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the
|
|
specific device of your tensors however.
|
|
|
|
Args:
|
|
encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `dict[str, list[int]]`, `dict[str, list[list[int]]` or `list[dict[str, list[int]]]`):
|
|
Tokenized inputs. Can represent one input ([`BatchEncoding`] or `dict[str, list[int]]`) or a batch of
|
|
tokenized inputs (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
|
|
list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
|
|
collate function. Instead of `list[int]` you can have tensors (numpy arrays, or PyTorch tensors),
|
|
see the note above for the return type.
|
|
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
|
|
Select a strategy to pad the returned sequences (according to the model's padding side and padding
|
|
index) among:
|
|
|
|
- `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).
|
|
max_length (`int`, *optional*):
|
|
Maximum length of the returned list and optionally padding length (see above).
|
|
max_entity_length (`int`, *optional*):
|
|
The maximum length of the entity sequence.
|
|
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).
|
|
padding_side:
|
|
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_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?](../glossary#attention-mask)
|
|
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
|
If set, will return tensors instead of list of python integers. Acceptable values are:
|
|
|
|
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
|
- `'np'`: Return Numpy `np.ndarray` objects.
|
|
verbose (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to print more information and warnings.
|
|
"""
|
|
# If we have a list of dicts, let's convert it in a dict of lists
|
|
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
|
|
if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):
|
|
# Call .keys() explicitly for compatibility with TensorDict and other Mapping subclasses
|
|
encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}
|
|
|
|
# The model's main input name, usually `input_ids`, has be passed for padding
|
|
if self.model_input_names[0] not in encoded_inputs:
|
|
raise ValueError(
|
|
"You should supply an encoding or a list of encodings to this method "
|
|
f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
|
|
)
|
|
|
|
required_input = encoded_inputs[self.model_input_names[0]]
|
|
|
|
if not required_input:
|
|
if return_attention_mask:
|
|
encoded_inputs["attention_mask"] = []
|
|
return encoded_inputs
|
|
|
|
# If we have PyTorch/NumPy tensors/arrays as inputs, we cast them as python objects
|
|
# and rebuild them afterwards if no return_tensors is specified
|
|
# Note that we lose the specific device the tensor may be on for PyTorch
|
|
|
|
first_element = required_input[0]
|
|
if isinstance(first_element, (list, tuple)):
|
|
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
|
|
index = 0
|
|
while len(required_input[index]) == 0:
|
|
index += 1
|
|
if index < len(required_input):
|
|
first_element = required_input[index][0]
|
|
# At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
|
|
if not isinstance(first_element, (int, list, tuple)):
|
|
if is_torch_tensor(first_element):
|
|
return_tensors = "pt" if return_tensors is None else return_tensors
|
|
elif isinstance(first_element, np.ndarray):
|
|
return_tensors = "np" if return_tensors is None else return_tensors
|
|
else:
|
|
raise ValueError(
|
|
f"type of {first_element} unknown: {type(first_element)}. "
|
|
"Should be one of a python, numpy, or pytorch object."
|
|
)
|
|
|
|
for key, value in encoded_inputs.items():
|
|
encoded_inputs[key] = to_py_obj(value)
|
|
|
|
# Convert padding_strategy in PaddingStrategy
|
|
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
|
|
padding=padding, max_length=max_length, verbose=verbose
|
|
)
|
|
|
|
if max_entity_length is None:
|
|
max_entity_length = self.max_entity_length
|
|
|
|
required_input = encoded_inputs[self.model_input_names[0]]
|
|
if required_input and not isinstance(required_input[0], (list, tuple)):
|
|
encoded_inputs = self._pad(
|
|
encoded_inputs,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
padding_strategy=padding_strategy,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_attention_mask=return_attention_mask,
|
|
)
|
|
return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
|
|
|
|
batch_size = len(required_input)
|
|
if any(len(v) != batch_size for v in encoded_inputs.values()):
|
|
raise ValueError("Some items in the output dictionary have a different batch size than others.")
|
|
|
|
if padding_strategy == PaddingStrategy.LONGEST:
|
|
max_length = max(len(inputs) for inputs in required_input)
|
|
max_entity_length = (
|
|
max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0
|
|
)
|
|
padding_strategy = PaddingStrategy.MAX_LENGTH
|
|
|
|
batch_outputs = {}
|
|
for i in range(batch_size):
|
|
inputs = {k: v[i] for k, v in encoded_inputs.items()}
|
|
outputs = self._pad(
|
|
inputs,
|
|
max_length=max_length,
|
|
max_entity_length=max_entity_length,
|
|
padding_strategy=padding_strategy,
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
|
padding_side=padding_side,
|
|
return_attention_mask=return_attention_mask,
|
|
)
|
|
|
|
for key, value in outputs.items():
|
|
if key not in batch_outputs:
|
|
batch_outputs[key] = []
|
|
batch_outputs[key].append(value)
|
|
|
|
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
|
|
|
|
def _pad(
|
|
self,
|
|
encoded_inputs: dict[str, EncodedInput] | BatchEncoding,
|
|
max_length: int | None = None,
|
|
max_entity_length: int | None = None,
|
|
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
|
|
pad_to_multiple_of: int | None = None,
|
|
padding_side: str | None = None,
|
|
return_attention_mask: bool | None = None,
|
|
) -> dict:
|
|
"""
|
|
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
|
|
|
|
|
|
Args:
|
|
encoded_inputs:
|
|
Dictionary of tokenized inputs (`list[int]`) or batch of tokenized inputs (`list[list[int]]`).
|
|
max_length: maximum length of the returned list and optionally padding length (see below).
|
|
Will truncate by taking into account the special tokens.
|
|
max_entity_length: The maximum length of the entity sequence.
|
|
padding_strategy: PaddingStrategy to use for padding.
|
|
|
|
|
|
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
|
|
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
|
|
- PaddingStrategy.DO_NOT_PAD: Do not pad
|
|
The tokenizer padding sides are defined in self.padding_side:
|
|
|
|
|
|
- 'left': pads on the left of the sequences
|
|
- 'right': pads on the right of the sequences
|
|
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
|
|
This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
|
|
`>= 7.5` (Volta).
|
|
padding_side:
|
|
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_attention_mask:
|
|
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
|
|
"""
|
|
entities_provided = bool("entity_ids" in encoded_inputs)
|
|
|
|
# Load from model defaults
|
|
if return_attention_mask is None:
|
|
return_attention_mask = "attention_mask" in self.model_input_names
|
|
|
|
if padding_strategy == PaddingStrategy.LONGEST:
|
|
max_length = len(encoded_inputs["input_ids"])
|
|
if entities_provided:
|
|
max_entity_length = len(encoded_inputs["entity_ids"])
|
|
|
|
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
|
|
max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
|
|
|
|
if (
|
|
entities_provided
|
|
and max_entity_length is not None
|
|
and pad_to_multiple_of is not None
|
|
and (max_entity_length % pad_to_multiple_of != 0)
|
|
):
|
|
max_entity_length = ((max_entity_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
|
|
|
|
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and (
|
|
len(encoded_inputs["input_ids"]) != max_length
|
|
or (entities_provided and len(encoded_inputs["entity_ids"]) != max_entity_length)
|
|
)
|
|
|
|
# Initialize attention mask if not present.
|
|
if return_attention_mask and "attention_mask" not in encoded_inputs:
|
|
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
|
|
if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs:
|
|
encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"])
|
|
|
|
if needs_to_be_padded:
|
|
difference = max_length - len(encoded_inputs["input_ids"])
|
|
padding_side = padding_side if padding_side is not None else self.padding_side
|
|
if entities_provided:
|
|
entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
|
|
if padding_side == "right":
|
|
if return_attention_mask:
|
|
encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
|
|
if entities_provided:
|
|
encoded_inputs["entity_attention_mask"] = (
|
|
encoded_inputs["entity_attention_mask"] + [0] * entity_difference
|
|
)
|
|
if "token_type_ids" in encoded_inputs:
|
|
encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"] + [0] * difference
|
|
if entities_provided:
|
|
encoded_inputs["entity_token_type_ids"] = (
|
|
encoded_inputs["entity_token_type_ids"] + [0] * entity_difference
|
|
)
|
|
if "special_tokens_mask" in encoded_inputs:
|
|
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
|
|
encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [self.pad_token_id] * difference
|
|
if entities_provided:
|
|
encoded_inputs["entity_ids"] = (
|
|
encoded_inputs["entity_ids"] + [self.entity_pad_token_id] * entity_difference
|
|
)
|
|
encoded_inputs["entity_position_ids"] = (
|
|
encoded_inputs["entity_position_ids"] + [[-1] * self.max_mention_length] * entity_difference
|
|
)
|
|
if self.task == "entity_span_classification":
|
|
encoded_inputs["entity_start_positions"] = (
|
|
encoded_inputs["entity_start_positions"] + [0] * entity_difference
|
|
)
|
|
encoded_inputs["entity_end_positions"] = (
|
|
encoded_inputs["entity_end_positions"] + [0] * entity_difference
|
|
)
|
|
|
|
elif padding_side == "left":
|
|
if return_attention_mask:
|
|
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
|
|
if entities_provided:
|
|
encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_inputs[
|
|
"entity_attention_mask"
|
|
]
|
|
if "token_type_ids" in encoded_inputs:
|
|
encoded_inputs["token_type_ids"] = [0] * difference + encoded_inputs["token_type_ids"]
|
|
if entities_provided:
|
|
encoded_inputs["entity_token_type_ids"] = [0] * entity_difference + encoded_inputs[
|
|
"entity_token_type_ids"
|
|
]
|
|
if "special_tokens_mask" in encoded_inputs:
|
|
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
|
|
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
|
|
if entities_provided:
|
|
encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[
|
|
"entity_ids"
|
|
]
|
|
encoded_inputs["entity_position_ids"] = [
|
|
[-1] * self.max_mention_length
|
|
] * entity_difference + encoded_inputs["entity_position_ids"]
|
|
if self.task == "entity_span_classification":
|
|
encoded_inputs["entity_start_positions"] = [0] * entity_difference + encoded_inputs[
|
|
"entity_start_positions"
|
|
]
|
|
encoded_inputs["entity_end_positions"] = [0] * entity_difference + encoded_inputs[
|
|
"entity_end_positions"
|
|
]
|
|
else:
|
|
raise ValueError("Invalid padding strategy:" + str(padding_side))
|
|
|
|
return encoded_inputs
|
|
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]:
|
|
"""
|
|
Save only the entity vocabulary file. The tokenizer.json is saved by the parent TokenizersBackend.
|
|
"""
|
|
if not os.path.isdir(save_directory):
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
|
return ()
|
|
|
|
entity_vocab_file = os.path.join(
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["entity_vocab_file"]
|
|
)
|
|
|
|
with open(entity_vocab_file, "w", encoding="utf-8") as f:
|
|
f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
|
|
|
|
return (entity_vocab_file,)
|
|
|
|
def build_inputs_with_special_tokens(
|
|
self, token_ids_0: list[int], token_ids_1: list[int] | None = None
|
|
) -> list[int]:
|
|
"""
|
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
|
adding special tokens. An XLM-RoBERTa sequence has the following format:
|
|
|
|
- single sequence: `<s> X </s>`
|
|
- pair of sequences: `<s> A </s></s> B </s>`
|
|
|
|
Args:
|
|
token_ids_0 (`list[int]`):
|
|
List of IDs to which the special tokens will be added.
|
|
token_ids_1 (`list[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
|
|
Returns:
|
|
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
|
"""
|
|
|
|
if token_ids_1 is None:
|
|
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
|
|
cls = [self.cls_token_id]
|
|
sep = [self.sep_token_id]
|
|
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
|
|
|
|
def get_special_tokens_mask(
|
|
self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False
|
|
) -> list[int]:
|
|
"""
|
|
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.
|
|
|
|
Args:
|
|
token_ids_0 (`list[int]`):
|
|
List of IDs.
|
|
token_ids_1 (`list[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
|
Whether or not the token list is already formatted with special tokens for the model.
|
|
|
|
Returns:
|
|
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
|
"""
|
|
|
|
if already_has_special_tokens:
|
|
return super().get_special_tokens_mask(
|
|
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
|
)
|
|
|
|
if token_ids_1 is None:
|
|
return [1] + ([0] * len(token_ids_0)) + [1]
|
|
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
|
|
|
def create_token_type_ids_from_sequences(
|
|
self, token_ids_0: list[int], token_ids_1: list[int] | None = None
|
|
) -> list[int]:
|
|
"""
|
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
|
|
not make use of token type ids, therefore a list of zeros is returned.
|
|
|
|
Args:
|
|
token_ids_0 (`list[int]`):
|
|
List of IDs.
|
|
token_ids_1 (`list[int]`, *optional*):
|
|
Optional second list of IDs for sequence pairs.
|
|
|
|
Returns:
|
|
`list[int]`: List of zeros.
|
|
|
|
"""
|
|
|
|
sep = [self.sep_token_id]
|
|
cls = [self.cls_token_id]
|
|
|
|
if token_ids_1 is None:
|
|
return len(cls + token_ids_0 + sep) * [0]
|
|
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
|
|
|
|
|
|
__all__ = ["MLukeTokenizer"]
|