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tokenization_barthez.py
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tokenization_barthez.py
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# coding=utf-8
# Copyright 2020 Ecole Polytechnique and 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 the BARThez model."""
import
os
from
shutil
import
copyfile
from
typing
import
List
,
Optional
,
Tuple
import
sentencepiece
as
spm
from
...tokenization_utils
import
AddedToken
,
PreTrainedTokenizer
from
...utils
import
logging
logger
=
logging
.
get_logger
(
__name__
)
VOCAB_FILES_NAMES
=
{
"vocab_file"
:
"sentencepiece.bpe.model"
}
PRETRAINED_VOCAB_FILES_MAP
=
{
"vocab_file"
:
{
"moussaKam/mbarthez"
:
"https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model"
,
"moussaKam/barthez"
:
"https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model"
,
"moussaKam/barthez-orangesum-title"
:
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
,
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
=
{
"moussaKam/mbarthez"
:
1024
,
"moussaKam/barthez"
:
1024
,
"moussaKam/barthez-orangesum-title"
:
1024
,
}
SPIECE_UNDERLINE
=
"▁"
class
BarthezTokenizer
(
PreTrainedTokenizer
):
"""
Adapted from :class:`~transformers.CamembertTokenizer` and :class:`~transformers.BartTokenizer`. Construct a
BARThez tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
`SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
contains the vocabulary necessary to instantiate a tokenizer.
bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the :obj:`cls_token`.
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end of
sequence. The token used is the :obj:`sep_token`.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
additional_special_tokens (:obj:`List[str]`, `optional`, defaults to :obj:`["<s>NOTUSED", "</s>NOTUSED"]`):
Additional special tokens used by the tokenizer.
Attributes:
sp_model (:obj:`SentencePieceProcessor`):
The `SentencePiece` processor that is used for every conversion (string, tokens and IDs).
"""
vocab_files_names
=
VOCAB_FILES_NAMES
pretrained_vocab_files_map
=
PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes
=
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names
=
[
"input_ids"
,
"attention_mask"
]
def
__init__
(
self
,
vocab_file
,
bos_token
=
"<s>"
,
eos_token
=
"</s>"
,
sep_token
=
"</s>"
,
cls_token
=
"<s>"
,
unk_token
=
"<unk>"
,
pad_token
=
"<pad>"
,
mask_token
=
"<mask>"
,
**
kwargs
):
# Mask token behave like a normal word, i.e. include the space before it
mask_token
=
AddedToken
(
mask_token
,
lstrip
=
True
,
rstrip
=
False
)
if
isinstance
(
mask_token
,
str
)
else
mask_token
super
()
.
__init__
(
bos_token
=
bos_token
,
eos_token
=
eos_token
,
unk_token
=
unk_token
,
sep_token
=
sep_token
,
cls_token
=
cls_token
,
pad_token
=
pad_token
,
mask_token
=
mask_token
,
**
kwargs
,
)
self
.
vocab_file
=
vocab_file
self
.
sp_model
=
spm
.
SentencePieceProcessor
()
self
.
sp_model
.
Load
(
str
(
vocab_file
))
self
.
fairseq_tokens_to_ids
=
{
"<s>"
:
0
,
"<pad>"
:
1
,
"</s>"
:
2
,
"<unk>"
:
3
}
self
.
fairseq_tokens_to_ids
[
"<mask>"
]
=
len
(
self
.
sp_model
)
-
1
self
.
fairseq_ids_to_tokens
=
{
v
:
k
for
k
,
v
in
self
.
fairseq_tokens_to_ids
.
items
()}
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 BARThez sequence has the following format:
- single sequence: ``<s> X </s>``
- pair of sequences: ``<s> A </s></s> B </s>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#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
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 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
:obj:`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
:
if
token_ids_1
is
not
None
:
raise
ValueError
(
"You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model."
)
return
list
(
map
(
lambda
x
:
1
if
x
in
[
self
.
sep_token_id
,
self
.
cls_token_id
]
else
0
,
token_ids_0
))
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
]
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.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`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
]
@property
def
vocab_size
(
self
):
return
len
(
self
.
sp_model
)
def
get_vocab
(
self
):
vocab
=
{
self
.
convert_ids_to_tokens
(
i
):
i
for
i
in
range
(
self
.
vocab_size
)}
vocab
.
update
(
self
.
added_tokens_encoder
)
return
vocab
def
_tokenize
(
self
,
text
):
return
self
.
sp_model
.
EncodeAsPieces
(
text
)
def
_convert_token_to_id
(
self
,
token
):
""" Converts a token (str) in an id using the vocab. """
if
token
in
self
.
fairseq_tokens_to_ids
:
return
self
.
fairseq_tokens_to_ids
[
token
]
spm_id
=
self
.
sp_model
.
PieceToId
(
token
)
return
spm_id
if
spm_id
else
self
.
unk_token_id
def
_convert_id_to_token
(
self
,
index
):
"""Converts an index (integer) in a token (str) using the vocab."""
if
index
in
self
.
fairseq_ids_to_tokens
:
return
self
.
fairseq_ids_to_tokens
[
index
]
return
self
.
sp_model
.
IdToPiece
(
index
)
def
__getstate__
(
self
):
state
=
self
.
__dict__
.
copy
()
state
[
"sp_model"
]
=
None
return
state
def
__setstate__
(
self
,
d
):
self
.
__dict__
=
d
self
.
sp_model
=
spm
.
SentencePieceProcessor
()
self
.
sp_model
.
Load
(
self
.
vocab_file
)
def
convert_tokens_to_string
(
self
,
tokens
):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string
=
""
.
join
(
tokens
)
.
replace
(
SPIECE_UNDERLINE
,
" "
)
.
strip
()
return
out_string
def
save_vocabulary
(
self
,
save_directory
:
str
,
filename_prefix
:
Optional
[
str
]
=
None
)
->
Tuple
[
str
]:
if
not
os
.
path
.
isdir
(
save_directory
):
logger
.
error
(
"Vocabulary path ({}) should be a directory"
.
format
(
save_directory
))
return
out_vocab_file
=
os
.
path
.
join
(
save_directory
,
(
filename_prefix
+
"-"
if
filename_prefix
else
""
)
+
VOCAB_FILES_NAMES
[
"vocab_file"
]
)
if
os
.
path
.
abspath
(
self
.
vocab_file
)
!=
os
.
path
.
abspath
(
out_vocab_file
):
copyfile
(
self
.
vocab_file
,
out_vocab_file
)
return
(
out_vocab_file
,)
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