# coding=utf-8
# Copyright Studio-Ouisa and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Tokenization classes for LUKE."""
import itertools
import json
import os
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ... import RobertaTokenizer
from ...file_utils import add_end_docstrings, is_tf_available, is_torch_available
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
AddedToken,
BatchEncoding,
EncodedInput,
PaddingStrategy,
TensorType,
TextInput,
TextInputPair,
TruncationStrategy,
_is_tensorflow,
_is_torch,
to_py_obj,
)
from ...utils import logging
logger = logging.get_logger(__name__)
EntitySpan = Tuple[int, int]
EntitySpanInput = List[EntitySpan]
Entity = str
EntityInput = List[Entity]
VOCAB_FILES_NAMES = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"entity_vocab_file": "entity_vocab.json",
}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/vocab.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/vocab.json",
},
"merges_file": {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/merges.txt",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/merges.txt",
},
"entity_vocab_file": {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/entity_vocab.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/entity_vocab.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"studio-ousia/luke-base": 512,
"studio-ousia/luke-large": 512,
}
ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
return_token_type_ids (:obj:`bool`, `optional`):
Whether to return token type IDs. If left to the default, will return the token type IDs according to
the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
`What are token type IDs? <../glossary.html#token-type-ids>`__
return_attention_mask (:obj:`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 :obj:`return_outputs` attribute.
`What are attention masks? <../glossary.html#attention-mask>`__
return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
of pairs) is provided with :obj:`truncation_strategy = longest_first` or :obj:`True`, an error is
raised instead of returning overflowing tokens.
return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return special tokens mask information.
return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return :obj:`(char_start, char_end)` for each token.
This is only available on fast tokenizers inheriting from
:class:`~transformers.PreTrainedTokenizerFast`, if using Python's tokenizer, this method will raise
:obj:`NotImplementedError`.
return_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to return the lengths of the encoded inputs.
verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to print more information and warnings.
**kwargs: passed to the :obj:`self.tokenize()` method
Return:
:class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:
- **input_ids** -- List of token ids to be fed to a model.
`What are input IDs? <../glossary.html#input-ids>`__
- **token_type_ids** -- List of token type ids to be fed to a model (when :obj:`return_token_type_ids=True`
or if `"token_type_ids"` is in :obj:`self.model_input_names`).
`What are token type IDs? <../glossary.html#token-type-ids>`__
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
:obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names`).
`What are attention masks? <../glossary.html#attention-mask>`__
- **entity_ids** -- List of entity ids to be fed to a model.
`What are input IDs? <../glossary.html#input-ids>`__
- **entity_position_ids** -- List of entity positions in the input sequence to be fed to a model.
- **entity_token_type_ids** -- List of entity token type ids to be fed to a model (when
:obj:`return_token_type_ids=True` or if `"entity_token_type_ids"` is in :obj:`self.model_input_names`).
`What are token type IDs? <../glossary.html#token-type-ids>`__
- **entity_attention_mask** -- List of indices specifying which entities should be attended to by the model
(when :obj:`return_attention_mask=True` or if `"entity_attention_mask"` is in
:obj:`self.model_input_names`).
`What are attention masks? <../glossary.html#attention-mask>`__
- **entity_start_positions** -- List of the start positions of entities in the word token sequence (when
:obj:`task="entity_span_classification"`).
- **entity_end_positions** -- List of the end positions of entities in the word token sequence (when
:obj:`task="entity_span_classification"`).
- **overflowing_tokens** -- List of overflowing tokens sequences (when a :obj:`max_length` is specified and
:obj:`return_overflowing_tokens=True`).
- **num_truncated_tokens** -- Number of tokens truncated (when a :obj:`max_length` is specified and
:obj:`return_overflowing_tokens=True`).
- **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
regular sequence tokens (when :obj:`add_special_tokens=True` and :obj:`return_special_tokens_mask=True`).
- **length** -- The length of the inputs (when :obj:`return_length=True`)
"""
[docs]class LukeTokenizer(RobertaTokenizer):
r"""
Construct a LUKE tokenizer.
This tokenizer inherits from :class:`~transformers.RobertaTokenizer` which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods. Compared to
:class:`~transformers.RobertaTokenizer`, :class:`~transformers.LukeTokenizer` also creates entity sequences, namely
:obj:`entity_ids`, :obj:`entity_attention_mask`, :obj:`entity_token_type_ids`, and :obj:`entity_position_ids` to be
used by the LUKE model.
Args:
vocab_file (:obj:`str`):
Path to the vocabulary file.
merges_file (:obj:`str`):
Path to the merges file.
entity_vocab_file (:obj:`str`):
Path to the entity vocabulary file.
task (:obj:`str`, `optional`):
Task for which you want to prepare sequences. One of :obj:`"entity_classification"`,
:obj:`"entity_pair_classification"`, or :obj:`"entity_span_classification"`. If you specify this argument,
the entity sequence is automatically created based on the given entity span(s).
max_entity_length (:obj:`int`, `optional`, defaults to 32):
The maximum length of :obj:`entity_ids`.
max_mention_length (:obj:`int`, `optional`, defaults to 30):
The maximum number of tokens inside an entity span.
entity_token_1 (:obj:`str`, `optional`, defaults to :obj:`<ent>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
``task`` is set to :obj:`"entity_classification"` or :obj:`"entity_pair_classification"`.
entity_token_2 (:obj:`str`, `optional`, defaults to :obj:`<ent2>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
``task`` is set to :obj:`"entity_pair_classification"`.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
merges_file,
entity_vocab_file,
task=None,
max_entity_length=32,
max_mention_length=30,
entity_token_1="<ent>",
entity_token_2="<ent2>",
**kwargs
):
# we add 2 special tokens for downstream tasks
# for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778
entity_token_1 = (
AddedToken(entity_token_1, lstrip=False, rstrip=False)
if isinstance(entity_token_1, str)
else entity_token_1
)
entity_token_2 = (
AddedToken(entity_token_2, lstrip=False, rstrip=False)
if isinstance(entity_token_2, str)
else entity_token_2
)
kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
kwargs["additional_special_tokens"] += [entity_token_1, entity_token_2]
super().__init__(
vocab_file=vocab_file,
merges_file=merges_file,
task=task,
max_entity_length=32,
max_mention_length=30,
entity_token_1="<ent>",
entity_token_2="<ent2>",
**kwargs,
)
with open(entity_vocab_file, encoding="utf-8") as entity_vocab_handle:
self.entity_vocab = json.load(entity_vocab_handle)
self.task = task
if task is None or task == "entity_span_classification":
self.max_entity_length = max_entity_length
elif task == "entity_classification":
self.max_entity_length = 1
elif task == "entity_pair_classification":
self.max_entity_length = 2
else:
raise ValueError(
f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification', 'entity_span_classification'] only."
)
self.max_mention_length = max_mention_length
[docs] @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, List[TextInput]],
text_pair: Optional[Union[TextInput, List[TextInput]]] = None,
entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None,
entity_spans_pair: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None,
entities: Optional[Union[EntityInput, List[EntityInput]]] = None,
entities_pair: Optional[Union[EntityInput, List[EntityInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: Optional[bool] = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
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 (:obj:`str`, :obj:`List[str]`, :obj:`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 (:obj:`str`, :obj:`List[str]`, :obj:`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 (:obj:`List[Tuple[int, int]]`, :obj:`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
:obj:`"entity_classification"` or :obj:`"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 (:obj:`List[Tuple[int, int]]`, :obj:`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 (:obj:`List[str]`, :obj:`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 (:obj:`List[str]`, :obj:`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 (:obj:`int`, `optional`):
The maximum length of :obj:`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)))
assert (
is_valid_single_text or is_valid_batch_text
), "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)
)
assert (
text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair
), "text_pair input must be of type `str` (single example) or `List[str]` (batch)."
is_batched = bool(isinstance(text, (list, tuple)))
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=padding,
truncation=truncation,
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,
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=padding,
truncation=truncation,
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,
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,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def encode_plus(
self,
text: Union[TextInput],
text_pair: Optional[Union[TextInput]] = None,
entity_spans: Optional[EntitySpanInput] = None,
entity_spans_pair: Optional[EntitySpanInput] = None,
entities: Optional[EntityInput] = None,
entities_pair: Optional[EntityInput] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: Optional[bool] = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences.
.. warning:: This method is deprecated, ``__call__`` should be used instead.
Args:
text (:obj:`str`):
The first sequence to be encoded. Each sequence must be a string.
text_pair (:obj:`str`):
The second sequence to be encoded. Each sequence must be a string.
entity_spans (:obj:`List[Tuple[int, int]]`, :obj:`List[List[Tuple[int, int]]]`, `optional`)::
The first sequence of entity spans to be encoded. The sequence consists of tuples each with two
integers denoting character-based start and end positions of entities. If you specify
:obj:`"entity_classification"` or :obj:`"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 the sequence must be equal to the length of ``entities``.
entity_spans_pair (:obj:`List[Tuple[int, int]]`, :obj:`List[List[Tuple[int, int]]]`, `optional`)::
The second sequence of entity spans to be encoded. The 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
the sequence must be equal to the length of ``entities_pair``.
entities (:obj:`List[str]` `optional`)::
The first sequence of entities to be encoded. The 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 the sequence must be
equal to the length of ``entity_spans``. If you specify ``entity_spans`` without specifying this
argument, the entity sequence is automatically constructed by filling it with the [MASK] entity.
entities_pair (:obj:`List[str]`, :obj:`List[List[str]]`, `optional`)::
The second sequence of entities to be encoded. The 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 the sequence must be
equal to the length of ``entity_spans_pair``. If you specify ``entity_spans_pair`` without specifying
this argument, the entity sequence is automatically constructed by filling it with the [MASK] entity.
max_entity_length (:obj:`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,
)
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,
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: Union[TextInput],
text_pair: Optional[Union[TextInput]] = None,
entity_spans: Optional[EntitySpanInput] = None,
entity_spans_pair: Optional[EntitySpanInput] = None,
entities: Optional[EntityInput] = None,
entities_pair: Optional[EntityInput] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: Optional[bool] = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
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,
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,
)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def batch_encode_plus(
self,
batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]],
batch_entity_spans_or_entity_spans_pairs: Optional[
Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]]
] = None,
batch_entities_or_entities_pairs: Optional[
Union[List[EntityInput], List[Tuple[EntityInput, EntityInput]]]
] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: Optional[bool] = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
"""
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
.. warning::
This method is deprecated, ``__call__`` should be used instead.
Args:
batch_text_or_text_pairs (:obj:`List[str]`, :obj:`List[Tuple[str, str]]`):
Batch of sequences or pair of sequences to be encoded. This can be a list of string or a list of pair
of string (see details in ``encode_plus``).
batch_entity_spans_or_entity_spans_pairs (:obj:`List[List[Tuple[int, int]]]`,
:obj:`List[Tuple[List[Tuple[int, int]], List[Tuple[int, int]]]]`, `optional`)::
Batch of entity span sequences or pairs of entity span sequences to be encoded (see details in
``encode_plus``).
batch_entities_or_entities_pairs (:obj:`List[List[str]]`, :obj:`List[Tuple[List[str], List[str]]]`,
`optional`):
Batch of entity sequences or pairs of entity sequences to be encoded (see details in ``encode_plus``).
max_entity_length (:obj:`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,
)
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,
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 _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[List[TextInput], List[TextInputPair]],
batch_entity_spans_or_entity_spans_pairs: Optional[
Union[List[EntitySpanInput], List[Tuple[EntitySpanInput, EntitySpanInput]]]
] = None,
batch_entities_or_entities_pairs: Optional[
Union[List[EntityInput], List[Tuple[EntityInput, EntityInput]]]
] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: Optional[bool] = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
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 entity_spans_or_entity_spans_pairs:
if isinstance(entity_spans_or_entity_spans_pairs[0][0], int):
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs, None
else:
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
(
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,
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 _create_input_sequence(
self,
text: Union[TextInput],
text_pair: Optional[Union[TextInput]] = None,
entities: Optional[EntityInput] = None,
entities_pair: Optional[EntityInput] = None,
entity_spans: Optional[EntitySpanInput] = None,
entity_spans_pair: Optional[EntitySpanInput] = None,
**kwargs
) -> Tuple[list, list, list, list, list, list]:
def get_input_ids(text):
tokens = self.tokenize(text, **kwargs)
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:
unk_entity_id = self.entity_vocab["[UNK]"]
mask_entity_id = self.entity_vocab["[MASK]"]
if entity_spans is None:
first_ids = get_input_ids(text)
else:
assert isinstance(entity_spans, list) and (
len(entity_spans) == 0 or isinstance(entity_spans[0], tuple)
), "entity_spans should be given as a list of tuples containing the start and end character indices"
assert entities is None or (
isinstance(entities, list) and (len(entities) == 0 or isinstance(entities[0], str))
), "If you specify entities, they should be given as a list of entity names"
assert entities is None or len(entities) == len(
entity_spans
), "If you specify entities, entities and entity_spans must be the same length"
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
if entities is None:
first_entity_ids = [mask_entity_id] * len(entity_spans)
else:
first_entity_ids = [self.entity_vocab.get(entity, unk_entity_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:
assert isinstance(entity_spans_pair, list) and (
len(entity_spans_pair) == 0 or isinstance(entity_spans_pair[0], tuple)
), "entity_spans_pair should be given as a list of tuples containing the start and end character indices"
assert entities_pair is None or (
isinstance(entities_pair, list)
and (len(entities_pair) == 0 or isinstance(entities_pair[0], str))
), "If you specify entities_pair, they should be given as a list of entity names"
assert entities_pair is None or len(entities_pair) == len(
entity_spans_pair
), "If you specify entities_pair, entities_pair and entity_spans_pair must be the same length"
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 = [mask_entity_id] * len(entity_spans_pair)
else:
second_entity_ids = [self.entity_vocab.get(entity, unk_entity_id) for entity in entities_pair]
elif self.task == "entity_classification":
assert (
isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)
), "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_vocab["[MASK]"]]
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":
assert (
isinstance(entity_spans, list)
and len(entity_spans) == 2
and isinstance(entity_spans[0], tuple)
and isinstance(entity_spans[1], tuple)
), "Entity spans should be provided as a list of tuples, each tuple containing the start and end character indices of an entity"
head_span, tail_span = entity_spans
first_entity_ids = [self.entity_vocab["[MASK]"], self.entity_vocab["[MASK2]"]]
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 = reversed(token_span_with_special_token_ids)
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":
mask_entity_id = self.entity_vocab["[MASK]"]
assert isinstance(entity_spans, list) and isinstance(
entity_spans[0], tuple
), "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 = [mask_entity_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[Optional[List[int]], Optional[List[int]]]],
batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = 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
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,
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: Optional[List[int]] = None,
entity_ids: Optional[List[int]] = None,
pair_entity_ids: Optional[List[int]] = None,
entity_token_spans: Optional[List[Tuple[int, int]]] = None,
pair_entity_token_spans: Optional[List[Tuple[int, int]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = False,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
stride: int = 0,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
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 (:obj:`List[int]`):
Tokenized input ids of the first sequence.
pair_ids (:obj:`List[int]`, `optional`):
Tokenized input ids of the second sequence.
entity_ids (:obj:`List[int]`, `optional`):
Entity ids of the first sequence.
pair_entity_ids (:obj:`List[int]`, `optional`):
Entity ids of the second sequence.
entity_token_spans (:obj:`List[Tuple[int, int]]`, `optional`):
Entity spans of the first sequence.
pair_entity_token_spans (:obj:`List[Tuple[int, int]]`, `optional`):
Entity spans of the second sequence.
max_entity_length (:obj:`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
# To do: add padding of entities
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,
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: Union[
BatchEncoding,
List[BatchEncoding],
Dict[str, EncodedInput],
Dict[str, List[EncodedInput]],
List[Dict[str, EncodedInput]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
return_tensors: Optional[Union[str, TensorType]] = 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, PyTorch tensors or TensorFlow 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 (:class:`~transformers.BatchEncoding`, list of :class:`~transformers.BatchEncoding`, :obj:`Dict[str, List[int]]`, :obj:`Dict[str, List[List[int]]` or :obj:`List[Dict[str, List[int]]]`):
Tokenized inputs. Can represent one input (:class:`~transformers.BatchEncoding` or :obj:`Dict[str,
List[int]]`) or a batch of tokenized inputs (list of :class:`~transformers.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 :obj:`List[int]` you can have tensors
(numpy arrays, PyTorch tensors or TensorFlow tensors), see the note above for the return type.
padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
single sequence if provided).
* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
maximum acceptable input length for the model if that argument is not provided.
* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
different lengths).
max_length (:obj:`int`, `optional`):
Maximum length of the returned list and optionally padding length (see above).
max_entity_length (:obj:`int`, `optional`):
The maximum length of the entity sequence.
pad_to_multiple_of (:obj:`int`, `optional`):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
return_attention_mask (:obj:`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 :obj:`return_outputs` attribute. `What are
attention masks? <../glossary.html#attention-mask>`__
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
If set, will return tensors instead of list of python integers. Acceptable values are:
* :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
* :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
* :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
verbose (:obj:`bool`, `optional`, defaults to :obj:`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], (dict, BatchEncoding)):
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/TF/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_tf_available() and _is_tensorflow(first_element):
return_tensors = "tf" if return_tensors is None else return_tensors
elif is_torch_available() and _is_torch(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)}. "
f"Should be one of a python, numpy, pytorch or tensorflow 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,
return_attention_mask=return_attention_mask,
)
return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
batch_size = len(required_input)
assert all(
len(v) == batch_size for v in encoded_inputs.values()
), "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 = dict((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,
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: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = 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).
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)
)
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
if entities_provided:
entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
if self.padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"]) + [0] * difference
if entities_provided:
encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"]) + [
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"] + [0] * 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 self.padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + [1] * len(encoded_inputs["input_ids"])
if entities_provided:
encoded_inputs["entity_attention_mask"] = [0] * entity_difference + [1] * len(
encoded_inputs["entity_ids"]
)
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"] = [0] * 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(self.padding_side))
else:
if return_attention_mask:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if entities_provided:
encoded_inputs["entity_attention_mask"] = [1] * len(encoded_inputs["entity_ids"])
return encoded_inputs
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
vocab_file, merge_file = super().save_vocabulary(save_directory, filename_prefix)
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, ensure_ascii=False))
return vocab_file, merge_file, entity_vocab_file