Rasa_Japanese

Rasa is an open source machine learning framework to automate text-and voice-based conversations.Rasa's primary purpose is to help you build contextual, layered conversations with lots of back-and-forth. To have a real conversation, you need to have some memory and build on things that were said earlier. Rasa lets you do that in a scalable way.

How to use rasa for japanese language?

Installation

Firstly install rasa in your own environment as following:

git clone https://github.com/RasaHQ/rasa.git
cd rasa

As we will add a custom component to tokenize japanese text , we will use mecab library for tokinzing the text.
add mecab-python3 at the end of requirements.txt file. Or install mecab using pip.

pip install mecab-python3

Now install all the requirements using :

pip install -r requirements.txt
pip install -e .

Create Project

First run the following command:
```
 rasa init --no-prompt
 
```
if you find any error like " rasa.core.trackers - Tried to set non existent slot 'name'. Make sure you added all your slots to your domain file.", then please remove all the data inside rasa/data/* directory.
Then add the Japanese language tokenizer in the path "rasa/rasa/nlu/japanese_tokenizer.py". I have added the file in "rasa/rasa/nlu/japanese_tokenizer.py" path.

Add JapaneseTokenizer component class in /rasa/rasa/nlu/registry.py. E.g:

from rasa.nlu.tokenizers.japanese_tokenizer import JapaneseTokenizer

Add class name inside component_classes = [] dictionary. E.g

component_classes = [
# tokenizers
JapaneseTokenizer,
MitieTokenizer,
SpacyTokenizer,
WhitespaceTokenizer,
JiebaTokenizer,
]

Now add JapaneseTokenizer as pipeline in config.yml as follows:

 # Configuration for Rasa NLU.
 # https://rasa.com/docs/rasa/nlu/components/
 language: jp
 pipeline:
   - name: "JapaneseTokenizer"
   - name: "RegexFeaturizer"
   - name: "CRFEntityExtractor"
   - name: "EntitySynonymMapper"
   - name: "CountVectorsFeaturizer"
   - name: "EmbeddingIntentClassifier"
 # Configuration for Rasa Core.
 # https://rasa.com/docs/rasa/core/policies/
 policies:
   - name: MemoizationPolicy
   - name: KerasPolicy
   - name: MappingPolicy

Now Add data as data/nlu.md, stories.md and domail.yml. E.g:

nle.md:

## intent:greet
- ハロー
- もしもし
- 初めまして
- こんにちは
- はじめまして

## intent:icebreak12
- よろしくお願いします
- こちらこそ
- 宜しくお願いします
- 問題ないです

stories.md:

 ## happy path
 * greet
   - utter_greet
 * icebreak12
   - utter_icebreak12

domain.yml:

intents:
- utter_icebreak12
- greet
templates:
  utter_icebreak12:
  - text: 今どこで、何をしていますか？
  utter_greet:
  - text: ご協力いただきありがとうございます。本日は宜しくお願いします
actions:
- utter_greet
- utter_icebreak12

Now train your model using:
```
  rasa train
```
To chat run:
```
  rasa shell
```

hareeshmu/Rasa_Japanese

Rasa_Japanese

How to use rasa for japanese language?

Installation

Create Project