1706 lines
82 KiB
Python
1706 lines
82 KiB
Python
# coding=utf-8
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# Copyright Studio-Ouisa and The HuggingFace Inc. team. All rights reserved.
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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 LUKE."""
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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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from functools import lru_cache
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from typing import Dict, List, Optional, Tuple, Union
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import numpy as np
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import regex as re
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from ...tokenization_utils import PreTrainedTokenizer
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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 ...utils import add_end_docstrings, is_tf_tensor, 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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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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"entity_vocab_file": "entity_vocab.json",
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}
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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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@lru_cache()
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# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
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def bytes_to_unicode():
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"""
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Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
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characters the bpe code barfs on.
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The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
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if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
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decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
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tables between utf-8 bytes and unicode strings.
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"""
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bs = (
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list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
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)
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cs = bs[:]
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n = 0
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for b in range(2**8):
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if b not in bs:
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bs.append(b)
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cs.append(2**8 + n)
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n += 1
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cs = [chr(n) for n in cs]
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return dict(zip(bs, cs))
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# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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return pairs
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class LukeTokenizer(PreTrainedTokenizer):
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"""
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Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
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This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
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be encoded differently whether it is at the beginning of the sentence (without space) or not:
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```python
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>>> from transformers import LukeTokenizer
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>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
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>>> tokenizer("Hello world")["input_ids"]
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[0, 31414, 232, 2]
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>>> tokenizer(" Hello world")["input_ids"]
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[0, 20920, 232, 2]
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```
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You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
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call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
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<Tip>
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When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
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</Tip>
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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. It also creates entity sequences, namely
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`entity_ids`, `entity_attention_mask`, `entity_token_type_ids`, and `entity_position_ids` to be used by the LUKE
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model.
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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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merges_file (`str`):
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Path to the merges file.
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entity_vocab_file (`str`):
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Path to the entity vocabulary file.
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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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errors (`str`, *optional*, defaults to `"replace"`):
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Paradigm to follow when decoding bytes to UTF-8. See
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[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
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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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add_prefix_space (`bool`, *optional*, defaults to `False`):
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Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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other word. (LUKE tokenizer detect beginning of words by the preceding space).
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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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vocab_file,
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merges_file,
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entity_vocab_file,
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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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errors="replace",
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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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add_prefix_space=False,
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**kwargs,
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):
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bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
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eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
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sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
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cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
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unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
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pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
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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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with open(vocab_file, encoding="utf-8") as vocab_handle:
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self.encoder = json.load(vocab_handle)
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self.decoder = {v: k for k, v in self.encoder.items()}
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self.errors = errors # how to handle errors in decoding
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self.byte_encoder = bytes_to_unicode()
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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bpe_merges = merges_handle.read().split("\n")[1:-1]
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bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
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self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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self.cache = {}
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self.add_prefix_space = add_prefix_space
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# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
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self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
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# we add 2 special tokens for downstream tasks
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# for more information about lstrip and rstrip, see https://github.com/huggingface/transformers/pull/2778
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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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kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
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kwargs["additional_special_tokens"] += [entity_token_1, entity_token_2]
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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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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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f"Probably an incorrect entity vocab file is loaded: {entity_vocab_file}."
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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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super().__init__(
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errors=errors,
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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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add_prefix_space=add_prefix_space,
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task=task,
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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=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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**kwargs,
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)
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@property
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# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Luke, RoBERTa->LUKE
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def vocab_size(self):
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return len(self.encoder)
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# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Luke, RoBERTa->LUKE
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def get_vocab(self):
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vocab = dict(self.encoder).copy()
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vocab.update(self.added_tokens_encoder)
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return vocab
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# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe with Roberta->Luke, RoBERTa->LUKE
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
|
|
word = " ".join(word)
|
|
self.cache[token] = word
|
|
return word
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Luke, RoBERTa->LUKE
|
|
def _tokenize(self, text):
|
|
"""Tokenize a string."""
|
|
bpe_tokens = []
|
|
for token in re.findall(self.pat, text):
|
|
token = "".join(
|
|
self.byte_encoder[b] for b in token.encode("utf-8")
|
|
) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
|
|
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
|
|
return bpe_tokens
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id with Roberta->Luke, RoBERTa->LUKE
|
|
def _convert_token_to_id(self, token):
|
|
"""Converts a token (str) in an id using the vocab."""
|
|
return self.encoder.get(token, self.encoder.get(self.unk_token))
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Luke, RoBERTa->LUKE
|
|
def _convert_id_to_token(self, index):
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
|
return self.decoder.get(index)
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string with Roberta->Luke, RoBERTa->LUKE
|
|
def convert_tokens_to_string(self, tokens):
|
|
"""Converts a sequence of tokens (string) in a single string."""
|
|
text = "".join(tokens)
|
|
text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
|
|
return text
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.build_inputs_with_special_tokens with Roberta->Luke, RoBERTa->LUKE
|
|
def build_inputs_with_special_tokens(
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
|
) -> List[int]:
|
|
"""
|
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
|
adding special tokens. A LUKE 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
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask with Roberta->Luke, RoBERTa->LUKE
|
|
def get_special_tokens_mask(
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = 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]
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Luke, RoBERTa->LUKE
|
|
def create_token_type_ids_from_sequences(
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
|
) -> List[int]:
|
|
"""
|
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. LUKE 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]
|
|
|
|
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Luke, RoBERTa->LUKE
|
|
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
|
|
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
|
|
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
|
|
text = " " + text
|
|
return (text, kwargs)
|
|
|
|
@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] = None,
|
|
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 (`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)))
|
|
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)))
|
|
|
|
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,
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
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 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,
|
|
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: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
|
|
if not isinstance(entity_spans, list):
|
|
raise ValueError("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: 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:
|
|
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 = 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":
|
|
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[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] = None,
|
|
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 (`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,
|
|
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 ([`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, PyTorch tensors or
|
|
TensorFlow 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).
|
|
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:
|
|
|
|
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
|
- `'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):
|
|
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_tensor(first_element):
|
|
return_tensors = "tf" if return_tensors is None else return_tensors
|
|
elif 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, 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)
|
|
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,
|
|
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)
|
|
)
|
|
|
|
# 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"])
|
|
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"] = 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 self.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(self.padding_side))
|
|
|
|
return encoded_inputs
|
|
|
|
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
|
if not os.path.isdir(save_directory):
|
|
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
|
return
|
|
vocab_file = os.path.join(
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
|
)
|
|
merge_file = os.path.join(
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
|
|
)
|
|
|
|
with open(vocab_file, "w", encoding="utf-8") as f:
|
|
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
|
|
|
|
index = 0
|
|
with open(merge_file, "w", encoding="utf-8") as writer:
|
|
writer.write("#version: 0.2\n")
|
|
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
|
|
if index != token_index:
|
|
logger.warning(
|
|
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
|
|
" Please check that the tokenizer is not corrupted!"
|
|
)
|
|
index = token_index
|
|
writer.write(" ".join(bpe_tokens) + "\n")
|
|
index += 1
|
|
|
|
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 vocab_file, merge_file, entity_vocab_file
|