shivavardhineedi
commited on
Commit
•
2a690bc
1
Parent(s):
e193352
Upload tokenizer
Browse files- added_tokens.json +11 -0
- special_tokens_map.json +47 -0
- tokenization_internlm2.py +235 -0
- tokenizer.model +3 -0
- tokenizer_config.json +179 -0
added_tokens.json
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{
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"</box>": 92552,
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"</img>": 92545,
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"</quad>": 92548,
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"</ref>": 92550,
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"<IMG_CONTEXT>": 92546,
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"<box>": 92551,
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"<img>": 92544,
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"<quad>": 92547,
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"<ref>": 92549
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}
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|action_start|>",
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"<|action_end|>",
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"<|interpreter|>",
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"<|plugin|>",
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"<img>",
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"</img>",
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"<IMG_CONTEXT>",
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"<quad>",
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"</quad>",
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"<ref>",
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"</ref>",
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"<box>",
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"</box>"
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],
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenization_internlm2.py
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# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
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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 InternLM."""
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import os
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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.tokenization_utils import PreTrainedTokenizer
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
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PRETRAINED_VOCAB_FILES_MAP = {}
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# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
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class InternLM2Tokenizer(PreTrainedTokenizer):
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"""
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Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
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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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"""
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vocab_files_names = VOCAB_FILES_NAMES
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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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_auto_class = 'AutoTokenizer'
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def __init__(
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self,
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vocab_file,
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unk_token='<unk>',
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bos_token='<s>',
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eos_token='</s>',
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pad_token='</s>',
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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add_bos_token=True,
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add_eos_token=False,
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decode_with_prefix_space=False,
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clean_up_tokenization_spaces=False,
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**kwargs,
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):
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.vocab_file = vocab_file
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self.add_bos_token = add_bos_token
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self.add_eos_token = add_eos_token
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self.decode_with_prefix_space = decode_with_prefix_space
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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self._no_prefix_space_tokens = None
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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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pad_token=pad_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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@property
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def no_prefix_space_tokens(self):
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if self._no_prefix_space_tokens is None:
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vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
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self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith('▁')}
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return self._no_prefix_space_tokens
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@property
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def vocab_size(self):
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"""Returns vocab size"""
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return self.sp_model.get_piece_size()
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@property
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def bos_token_id(self) -> Optional[int]:
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return self.sp_model.bos_id()
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@property
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def eos_token_id(self) -> Optional[int]:
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return self.sp_model.eos_id()
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def get_vocab(self):
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"""Returns vocab as a dict"""
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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):
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"""Returns a tokenized string."""
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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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return self.sp_model.piece_to_id(token)
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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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token = self.sp_model.IdToPiece(index)
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return token
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def _maybe_add_prefix_space(self, tokens, decoded):
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if tokens and tokens[0] not in self.no_prefix_space_tokens:
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return ' ' + decoded
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else:
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return decoded
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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current_sub_tokens = []
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out_string = ''
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prev_is_special = False
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special:
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out_string += ' '
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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current_sub_tokens = []
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else:
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current_sub_tokens.append(token)
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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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out_string = self.clean_up_tokenization(out_string)
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out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
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return out_string[1:]
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def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
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"""
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Save the vocabulary and special tokens file to a directory.
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Args:
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save_directory (`str`):
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The directory in which to save the vocabulary.
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Returns:
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`Tuple(str)`: Paths to the files saved.
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"""
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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 build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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if self.add_bos_token:
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bos_token_ids = [self.bos_token_id]
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else:
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bos_token_ids = []
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output = bos_token_ids + token_ids_0
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if token_ids_1 is not None:
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output = output + token_ids_1
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if self.add_eos_token:
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output = output + [self.eos_token_id]
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return output
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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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194 |
+
|
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Args:
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196 |
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token_ids_0 (`List[int]`):
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197 |
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List of IDs.
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198 |
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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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200 |
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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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+
|
203 |
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Returns:
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204 |
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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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205 |
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"""
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206 |
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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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210 |
+
|
211 |
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
|
213 |
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
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214 |
+
|
215 |
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def create_token_type_ids_from_sequences(
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216 |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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217 |
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) -> List[int]:
|
218 |
+
"""
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219 |
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
220 |
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use of token type ids, therefore a list of zeros is returned.
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221 |
+
|
222 |
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Args:
|
223 |
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token_ids_0 (`List[int]`):
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224 |
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List of IDs.
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225 |
+
token_ids_1 (`List[int]`, *optional*):
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226 |
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Optional second list of IDs for sequence pairs.
|
227 |
+
|
228 |
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Returns:
|
229 |
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`List[int]`: List of zeros.
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230 |
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"""
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231 |
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eos = [self.eos_token_id]
|
232 |
+
|
233 |
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if token_ids_1 is None:
|
234 |
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return len(token_ids_0 + eos) * [0]
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235 |
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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tokenizer.model
ADDED
@@ -0,0 +1,3 @@
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1 |
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version https://git-lfs.github.com/spec/v1
|
2 |
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oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
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3 |
+
size 1477754
|
tokenizer_config.json
ADDED
@@ -0,0 +1,179 @@
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1 |
+
{
|
2 |
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|
3 |
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|
4 |
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5 |
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10 |
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11 |
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|
12 |
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13 |
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14 |
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|
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|
18 |
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|
19 |
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|
20 |
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21 |
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|
22 |
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23 |
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|
24 |
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|
25 |
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|
26 |
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|
27 |
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|
28 |
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29 |
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|
30 |
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|
31 |
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|
32 |
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|
33 |
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|
34 |
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},
|
35 |
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|
36 |
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|
37 |
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|
38 |
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|
39 |
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|
40 |
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|
41 |
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|
42 |
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|
43 |
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|
44 |
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|
45 |
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46 |
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47 |
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|
48 |
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|
49 |
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50 |
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|
51 |
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|
52 |
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53 |
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54 |
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55 |
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|
56 |
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57 |
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58 |
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59 |
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|
60 |
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61 |
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62 |
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63 |
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64 |
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65 |
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66 |
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67 |
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68 |
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69 |
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70 |
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74 |
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75 |
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76 |
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77 |
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78 |
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86 |
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88 |
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89 |
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90 |
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91 |
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92 |
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93 |
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94 |
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95 |
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96 |
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97 |
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98 |
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99 |
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100 |
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101 |
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102 |
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103 |
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104 |
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105 |
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106 |
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107 |
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108 |
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110 |
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111 |
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113 |
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114 |
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115 |
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116 |
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118 |
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119 |
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120 |
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121 |
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122 |
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123 |
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124 |
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|
126 |
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|
127 |
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|
128 |
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|
129 |
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130 |
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|
131 |
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132 |
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136 |
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137 |
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138 |
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|
139 |
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|
140 |
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141 |
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|
142 |
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143 |
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|
144 |
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145 |
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|
146 |
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|
147 |
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|
148 |
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|
149 |
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|
150 |
+
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|
151 |
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"<|action_start|>",
|
152 |
+
"<|action_end|>",
|
153 |
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"<|interpreter|>",
|
154 |
+
"<|plugin|>",
|
155 |
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"<img>",
|
156 |
+
"</img>",
|
157 |
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"<IMG_CONTEXT>",
|
158 |
+
"<quad>",
|
159 |
+
"</quad>",
|
160 |
+
"<ref>",
|
161 |
+
"</ref>",
|
162 |
+
"<box>",
|
163 |
+
"</box>"
|
164 |
+
],
|
165 |
+
"auto_map": {
|
166 |
+
"AutoTokenizer": [
|
167 |
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"tokenization_internlm2.InternLM2Tokenizer",
|
168 |
+
null
|
169 |
+
]
|
170 |
+
},
|
171 |
+
"bos_token": "<s>",
|
172 |
+
"chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
173 |
+
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|
174 |
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|
175 |
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|
176 |
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|
177 |
+
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|
178 |
+
"unk_token": "<unk>"
|
179 |
+
}
|