Spaces:
Runtime error
Runtime error
File size: 2,475 Bytes
0558aa4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
from typing import Optional
from nemo.collections.common.tokenizers.char_tokenizer import CharTokenizer
__all__ = ['WordTokenizer']
class WordTokenizer(CharTokenizer):
"Tokenizes at word boundary"
def __init__(
self,
vocab_file: str,
mask_token: Optional[str] = None,
bos_token: Optional[str] = None,
eos_token: Optional[str] = None,
pad_token: Optional[str] = None,
sep_token: Optional[str] = None,
cls_token: Optional[str] = None,
unk_token: Optional[str] = None,
):
"""
Args:
vocab_file: path to file with vocabulary which consists
of characters separated by \n
mask_token: mask token
bos_token: the beginning of sequence token
eos_token: the end of sequence token. Usually equal to sep_token
pad_token: token to use for padding
sep_token: token used for separating sequences
cls_token: class token. Usually equal to bos_token
unk_token: token to use for unknown tokens
"""
super().__init__(
vocab_file=vocab_file,
mask_token=mask_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
unk_token=unk_token,
sep_token=sep_token,
cls_token=cls_token,
)
def text_to_tokens(self, text):
token_candidates = text.strip().split()
tokens = []
for token in token_candidates:
if token in self.vocab:
tokens.append(token)
else:
tokens.append(self.unk_token)
return tokens
def ids_to_text(self, ids):
ids_ = [id_ for id_ in ids if id_ not in self.special_tokens]
return " ".join(self.ids_to_tokens(ids_))
|