827d6d6ef0
* First pass on utility classes and python tokenizers * finishing cleanup pass * style and quality * Fix tests * Updating following @mfuntowicz comment * style and quality * Fix Roberta * fix batch_size/seq_length inBatchEncoding * add alignement methods + tests * Fix OpenAI and Transfo-XL tokenizers * adding trim_offsets=True default for GPT2 et RoBERTa * style and quality * fix tests * add_prefix_space in roberta * bump up tokenizers to rc7 * style * unfortunately tensorfow does like these - removing shape/seq_len for now * Update src/transformers/tokenization_utils.py Co-Authored-By: Stefan Schweter <stefan@schweter.it> * Adding doc and docstrings * making flake8 happy Co-authored-by: Stefan Schweter <stefan@schweter.it>
92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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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 DistilBERT."""
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import logging
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from .tokenization_bert import BertTokenizer, BertTokenizerFast
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
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PRETRAINED_VOCAB_FILES_MAP = {
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"vocab_file": {
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"distilbert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
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"distilbert-base-uncased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
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"distilbert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt",
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"distilbert-base-cased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt",
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"distilbert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-vocab.txt",
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"distilbert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
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}
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}
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
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"distilbert-base-uncased": 512,
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"distilbert-base-uncased-distilled-squad": 512,
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"distilbert-base-cased": 512,
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"distilbert-base-cased-distilled-squad": 512,
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"distilbert-base-german-cased": 512,
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"distilbert-base-multilingual-cased": 512,
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}
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PRETRAINED_INIT_CONFIGURATION = {
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"distilbert-base-uncased": {"do_lower_case": True},
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"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
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"distilbert-base-cased": {"do_lower_case": False},
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"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
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"distilbert-base-german-cased": {"do_lower_case": False},
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"distilbert-base-multilingual-cased": {"do_lower_case": False},
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}
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class DistilBertTokenizer(BertTokenizer):
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r"""
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Constructs a DistilBertTokenizer.
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:class:`~transformers.DistilBertTokenizer is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
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tokenization: punctuation splitting + wordpiece.
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Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
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parameters.
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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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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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model_input_names = ["attention_mask"]
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class DistilBertTokenizerFast(BertTokenizerFast):
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r"""
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Constructs a "Fast" DistilBertTokenizer (backed by HuggingFace's `tokenizers` library).
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:class:`~transformers.DistilBertTokenizerFast` is identical to :class:`~transformers.BertTokenizerFast` and runs end-to-end
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tokenization: punctuation splitting + wordpiece.
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Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
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parameters.
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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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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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model_input_names = ["attention_mask"]
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