Created
January 3, 2020 02:29
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import re | |
from typing import Any, Dict, List, Text | |
from rasa.nlu.config import RasaNLUModelConfig | |
from rasa.nlu.tokenizers.tokenizer import Token, Tokenizer | |
from rasa.nlu.training_data import Message, TrainingData | |
from rasa.nlu.constants import ( | |
INTENT_ATTRIBUTE, | |
TEXT_ATTRIBUTE, | |
TOKENS_NAMES, | |
MESSAGE_ATTRIBUTES, | |
) | |
class SudachiTokenizer(Tokenizer): | |
provides = [TOKENS_NAMES[attribute] for attribute in MESSAGE_ATTRIBUTES] | |
defaults = { | |
# Flag to check whether to split intents | |
"intent_tokenization_flag": False, | |
# Symbol on which intent should be split | |
"intent_split_symbol": "_", | |
# add __CLS__ token to the end of the list of tokens | |
"use_cls_token": False, | |
} # default don't load custom dictionary | |
def __init__(self, component_config: Dict[Text, Any] = None) -> None: | |
super().__init__(component_config) | |
from sudachipy import dictionary | |
from sudachipy import tokenizer | |
self.tokenizer_obj = dictionary.Dictionary().create() | |
self.mode = tokenizer.Tokenizer.SplitMode.A | |
@classmethod | |
def required_packages(cls) -> List[Text]: | |
return ["sudachipy"] | |
def train( | |
self, training_data: TrainingData, config: RasaNLUModelConfig, **kwargs: Any | |
) -> None: | |
for example in training_data.training_examples: | |
for attribute in MESSAGE_ATTRIBUTES: | |
if example.get(attribute) is not None: | |
example.set( | |
TOKENS_NAMES[attribute], | |
self.tokenize(example.get(attribute), attribute), | |
) | |
#for example in training_data.training_examples: | |
#example.set("tokens", self.tokenize(self, example.text)) | |
def process(self, message: Message, **kwargs: Any) -> None: | |
message.set( | |
TOKENS_NAMES[TEXT_ATTRIBUTE], self.tokenize(message.text, TEXT_ATTRIBUTE) | |
) | |
#message.set("tokens", self.tokenize(self, message.text)) | |
#def tokenize(self, text: Text) -> List[Token]: | |
def tokenize(self, text: Text, attribute: Text = TEXT_ATTRIBUTE) -> List[Token]: | |
words = [m.surface() for m in self.tokenizer_obj.tokenize(text, self.mode)] | |
running_offset = 0 | |
tokens = [] | |
print(words) | |
for word in words: | |
word_offset = text.index(word, running_offset) | |
word_len = len(word) | |
running_offset = word_offset + word_len | |
tokens.append(Token(word, word_offset)) | |
self.add_cls_token(tokens, attribute) | |
return tokens |
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