/usr/local/lib/python3.6/site-packages/transformers/models/retribert
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__pycache__/-0755rm
configuration_retribert.py51510644editdlrm
modeling_retribert.py92610644editdlrm
tokenization_retribert.py18230644editdlrm
tokenization_retribert_fast.py22030644editdlrm
__init__.py19930644editdlrm
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/retribert/tokenization_retribert.py (1823B)
# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for RetriBERT.""" from ...utils import logging from ..bert.tokenization_bert import BertTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "yjernite/retribert-base-uncased": "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "yjernite/retribert-base-uncased": 512, } PRETRAINED_INIT_CONFIGURATION = { "yjernite/retribert-base-uncased": {"do_lower_case": True}, } class RetriBertTokenizer(BertTokenizer): r""" Constructs a RetriBERT tokenizer. [`RetroBertTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting and wordpiece. Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION model_input_names = ["input_ids", "attention_mask"]