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import os |
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from contextlib import contextmanager |
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from shutil import copyfile |
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from typing import Any, Dict, List, Optional, Tuple |
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import sentencepiece as spm |
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from transformers import AddedToken, PreTrainedTokenizer |
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from transformers import logging |
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logger = logging.get_logger(__name__) |
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SPIECE_UNDERLINE = "▁" |
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VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} |
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PRETRAINED_VOCAB_FILES_MAP = { |
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"vocab_file": { |
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"Formzu/bart-base-japanese": ( |
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"https://huggingface.co/Formzu/bart-base-japanese/resolve/main/sentencepiece.bpe.model" |
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), |
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"Formzu/bart-large-japanese": ( |
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"https://huggingface.co/Formzu/bart-large-japanese/resolve/main/sentencepiece.bpe.model" |
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), |
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} |
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} |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"Formzu/bart-base-japanese": 1024, |
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"Formzu/bart-large-japanese": 1024, |
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} |
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class BartJapaneseTokenizer(PreTrainedTokenizer): |
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""" |
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Construct a BART tokenizer for Japanese text. |
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Adapted from [`RobertaTokenizer`], [`XLNetTokenizer`] and [`MBartTokenizer`]. Based on |
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[SentencePiece](https://github.com/google/sentencepiece). |
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The tokenization method is `<bos> <tokens> <eos>`. |
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Examples: |
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```python |
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>>> from tokenization_bart_japanese import BartJapaneseTokenizer |
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>>> tokenizer = BartJapaneseTokenizer.from_pretrained("Formzu/bart-base-japanese") |
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>>> example_japanese_phrase = "今日は晴れています。" |
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>>> expected_label = "天気" |
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>>> inputs = tokenizer(example_japanese_phrase, return_tensors="pt") |
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>>> labels = tokenizer(expected_label, return_tensors="pt") |
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>>> inputs["labels"] = labels["input_ids"] |
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```""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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model_input_names = ["input_ids", "attention_mask"] |
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prefix_tokens: List[int] = [] |
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suffix_tokens: List[int] = [] |
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def __init__( |
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self, |
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vocab_file, |
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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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tokenizer_file=None, |
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src_lang=None, |
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tgt_lang=None, |
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sp_model_kwargs: Optional[Dict[str, Any]] = None, |
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additional_special_tokens=None, |
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**kwargs |
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): |
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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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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs |
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super().__init__( |
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bos_token=bos_token, |
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eos_token=eos_token, |
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unk_token=unk_token, |
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sep_token=sep_token, |
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cls_token=cls_token, |
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pad_token=pad_token, |
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mask_token=mask_token, |
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tokenizer_file=None, |
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src_lang=src_lang, |
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tgt_lang=tgt_lang, |
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additional_special_tokens=additional_special_tokens, |
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sp_model_kwargs=self.sp_model_kwargs, |
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**kwargs, |
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) |
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.Load(str(vocab_file)) |
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self.vocab_file = vocab_file |
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try: |
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from zenhan import h2z |
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except ModuleNotFoundError as error: |
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raise error.__class__( |
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"You need to install zenhan to use BartJapaneseTokenizer." |
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"See https://pypi.org/project/zenhan/ for installation." |
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) |
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try: |
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from pyknp import Juman |
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except ModuleNotFoundError as error: |
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raise error.__class__( |
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"You need to install pyknp to use BartJapaneseTokenizer." |
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"See https://pypi.org/project/pyknp/ for installation." |
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) |
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self.h2z = h2z |
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self.jumanpp = Juman() |
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self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} |
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self.fairseq_offset = 1 |
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self.sp_model_size = len(self.sp_model) |
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self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset |
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self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} |
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self.set_special_tokens() |
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def __getstate__(self): |
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state = self.__dict__.copy() |
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state["sp_model"] = None |
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state["sp_model_proto"] = self.sp_model.serialized_model_proto() |
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return state |
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def __setstate__(self, d): |
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self.__dict__ = d |
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if not hasattr(self, "sp_model_kwargs"): |
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self.sp_model_kwargs = {} |
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.LoadFromSerializedProto(self.sp_model_proto) |
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@property |
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def vocab_size(self): |
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return len(self.sp_model) + self.fairseq_offset + 1 |
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def get_special_tokens_mask( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False |
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) -> List[int]: |
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""" |
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding |
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special tokens using the tokenizer `prepare_for_model` method. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether or not the token list is already formatted with special tokens for the model. |
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Returns: |
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
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""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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prefix_ones = [1] * len(self.prefix_tokens) |
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suffix_ones = [1] * len(self.suffix_tokens) |
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if token_ids_1 is None: |
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return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones |
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return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones |
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def build_inputs_with_special_tokens( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and |
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adding special tokens. A Japanese BART sequence has the following format, where `X` represents the sequence: |
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- `input_ids` (for encoder) `[bos] X [eos]` |
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- `decoder_input_ids`: (for decoder) `[bos] X [eos]` |
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Pairs of sequences are not the expected use case, but they will be handled without a separator. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs to which the special tokens will be added. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. |
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""" |
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if token_ids_1 is None: |
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return self.prefix_tokens + token_ids_0 + self.suffix_tokens |
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return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens |
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def create_token_type_ids_from_sequences( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. Japanese BART does not |
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make use of token type ids, therefore a list of zeros is returned. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of zeros. |
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""" |
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sep = [self.sep_token_id] |
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cls = [self.cls_token_id] |
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if token_ids_1 is None: |
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return len(cls + token_ids_0 + sep) * [0] |
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] |
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def get_vocab(self): |
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} |
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vocab.update(self.added_tokens_encoder) |
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return vocab |
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def _tokenize(self, text: str) -> List[str]: |
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text = text |
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text = self.h2z(text) |
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text = self.jumanpp.analysis(text) |
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text = ' '.join([mrph.midasi for mrph in text.mrph_list()]) |
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return self.sp_model.encode(text, out_type=str) |
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def _convert_token_to_id(self, token): |
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"""Converts a token (str) in an id using the vocab.""" |
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if token in self.fairseq_tokens_to_ids: |
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return self.fairseq_tokens_to_ids[token] |
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spm_id = self.sp_model.PieceToId(token) |
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return spm_id + self.fairseq_offset if spm_id else self.unk_token_id |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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if index in self.fairseq_ids_to_tokens: |
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return self.fairseq_ids_to_tokens[index] |
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return self.sp_model.IdToPiece(index - self.fairseq_offset) |
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def convert_tokens_to_string(self, tokens): |
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"""Converts a sequence of tokens (strings for sub-words) in a single string.""" |
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out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() |
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return out_string |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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out_vocab_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
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) |
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): |
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copyfile(self.vocab_file, out_vocab_file) |
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elif not os.path.isfile(self.vocab_file): |
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with open(out_vocab_file, "wb") as fi: |
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content_spiece_model = self.sp_model.serialized_model_proto() |
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fi.write(content_spiece_model) |
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return (out_vocab_file,) |
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def set_special_tokens(self) -> None: |
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"""Set prefix=[bos], suffix=[eos].""" |
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self.prefix_tokens = [self.bos_token_id] |
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self.suffix_tokens = [self.eos_token_id] |
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self.add_tokens(self.all_special_tokens_extended, special_tokens=True) |
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