Skip to main content

How I did customize "rasa-nlu-trainer" as my own tool


Check out my implementation here

Background

I wanted to have a tool for human beings to classify intents and extract entities of texts which were obtained from a raw dataset such as Rocket.chat's conversation, Maluuba Frames or here. Then, the output (labeled texts) could be consumed by an NLU tool such as Rasa NLU.

rasa-nlu-trainer was a potential one which I didn't need to build an app from scratch. However, I needed to add more of my own features to fulfill my needs. They were:

1. Loading/displaying raw texts stored by a database such as MongoDB
2. Manually labeling intents and entities for the loaded texts
3. Persisting labeled texts into the database

I firstly did look up what rasa-nlu-trainer's technologies were used in order to see how to implement my mentioned features.

At first glance

rasa-nlu-trainer was bootstrapped with Create React App. Create React App is a tool to create a React app with no build configuration, as it said. This tool is also recommended by the official React.js tutorial. I actually was very new to React.js. I wondered what problem Creat React App tried to solve so that I first wanted to set up a project without Create React App. I followed this post.

Well, let me say if I built a React app from scratch then I needed to do some manual configurations for Webpack and Babel including webpack.config.json and .babelrc

Then, I tried to use Create React App, and I followed this guide. All right, let me say Create React App helped me create a React boilerplate project with hiding Webpack and Babel's configuration so that as a developer I only needed to focus on writing production code.

The nuts and bolts

I went on starting to watch how rasa-nlu-trainer's source code was organized and what its techniques are used.

Folder structure

1. Auto-created stuff by Create React App
   - public/index.html
   - src/index.js
2. Overrides
   - They didn't use "react-script", they created their own way instead! That was the reason why some additional folder/files were added such as server.js, scripts and config.

Used technologies

- Programming language: Javascript
- Babel: ES6 transpiler
- Project bundle: Webpack
- Dependencies management: npm
- Backend: Node.js
- Fronted framework/project-based: React.js
- React model storing: React-Redux
- React UI extension components: Ant Design
- TypeScript

Hooray! Welcome me to Javascript world!

Architecture

- index.js: Provider{store} <---> LocaleProvider ---> App
- How "separation of concerns" works: import and  export
- Global data (store) is immutable and only changed by files actions.js and reducer.js

Proposed changes

Finally, I could start to add my needs to the existing rasa-nlu-trainer app. I listed out what my features:
- Creating an overview page as a custom React component to display all loaded texts
- Customizing the existing page for classifying intents and extracting entities.
- Implementing Node.js methods to load/stores texts (as JSON)  which stored in MongoDB

The rule of thumb was I needed to follow the mentioned architecture for modifying the global data which were managed by React-Redux.

You can take a look at the changes from this pull request.

Conclusion

Sometimes, the best way to learn new techs is to get down into an existing tool and customize it with your own needs. 💪

Comments

Popular posts from this blog

Junit - Test fails on French or German string assertion

In my previous post about building a regex to check a text without special characters but allow German and French . I met a problem that the unit test works fine on my machine using Eclipse, but it was fail when running on Jenkins' build job. Here is my test: @Test public void shouldAllowFrenchAndGermanCharacters(){ String source = "ÄäÖöÜüß áÁàÀâÂéÉèÈêÊîÎçÇ"; assertFalse(SpecialCharactersUtils.isExistSpecialCharater(source)); } Production code: public static boolean isExistNotAllowedCharacters(String source){ Pattern regex = Pattern.compile("^[a-zA-Z_0-9_ÄäÖöÜüß áÁàÀâÂéÉèÈêÊîÎçÇ]*$"); Matcher matcher = regex.matcher(source); return !matcher.matches(); } The result likes the following: Failed tests: SpecialCharactersUtilsTest.shouldAllowFrenchAndGermanCharacters:32 null A guy from stackoverflow.com says: "This is probably due to the default encoding used for your Java source files. The ö in the string literal in the J...

The HelloWorld example of JSF 2.2 with Myfaces

I just did by myself create a very simple app "HelloWorld" of JSF 2.2 with a concrete implementation Myfaces that we can use it later on for our further JSF trying out. I attached the source code link at the end part. Just follow these steps below: 1. Create a Maven project in Eclipse (Kepler) with a simple Java web application archetype "maven-archetype-webapp". Maven should be the best choice for managing the dependencies , so far. JSF is a web framework that is the reason why I chose the mentioned archetype for my example. 2. Import dependencies for JSF implementation - Myfaces (v2.2.10) into file pom.xml . The following code that is easy to find from  http://mvnrepository.com/  with key words "myfaces". <dependency> <groupId>org.apache.myfaces.core</groupId> <artifactId>myfaces-api</artifactId> <version>2.2.10</version> </dependency> <dependency> <groupId>org.apache.myfaces.core<...

How to apply Lean - Kanban for your business

This is the topic of Scrum Breakfast meetup this time, speaker: Ms. Phuong Bui - Technical Project Manager of YOOSE Pte. Ltd. http://www.meetup.com/Scrum-Breakfast-Vietnam-Agile-and-Scrum-Meetup/events/230313727/ Lean comes from Lean manufacturing is a method that focuses on elimination of wastes. In other words, this is a set of principles for archiving the quality, speed and customer alignment. The first time I knew about the term "Lean" is  from the book Software Craftsmanship . Sandro recommends if we want to transform our pet projects into a real business, we should get familiar with Lean Startup concepts. In this talk, Ms. Phuong pointed out some major wastes includes information (ex: unclear requirements), processes (ex: waiting), physical environment and people. Knowing what the problems should be the best way to eliminate them. The difference between  Single item flow and Batch processing is the second main point; and it is the Lean's idea. Batch pr...

Think like a Engineering Manager

Off-boarding Members can leave the company for various reasons, and as a manager, it is important to take action. Hoping for the best is not a strategy. In the case of a low-performing member, I can kindly issue an official warning, set clear objectives for improvement, and re-evaluate the results. If there is a conflict between members, I need to be mindful and go beyond the situation to list our expectations with corresponding actions. Finally, if a member has a big chance to grow at another company, I can have an honest discussion with that member about the trade-offs. Balance at Work As an engineering manager, it is important to balance involvement in meetings and getting your hands dirty on some topics. The goal is to become a companion to teams. Here are my two actions to deal with the situation: Dedicate time for important-but-not-urgent tasks and prioritize them daily. Categorize work into four lines including management, project support, OKRs, and self-study. Management Conduc...

Applying pipeline “tensorflow_embedding” of Rasa NLU

According to this nice article , there was a new pipeline released using a different approach from the standard one ( spacy_sklearn ). I wanted to give it a try to see whether it can help with improving bot’s accuracy. After applying done, I gave an evaluation of “tensorflow_embedding”. It seemed to work better a bit. For example, I defined intents “greet” and “goodbye” with some following messages in my training data. ## intent:greet - Hey! How are you? - Hi! How can I help you? - Good to see you! - Nice to see you! - Hi - Hello - Hi there ## intent:goodbye - Bye - Bye Bye - See you later - Take care - Peace In order to play around with Rasa NLU, I created a project here . You can have a look at this change from this pull request . Yay! When I entered message “hi bot”, then bot with “tensorflow_embedding” could detect intent “greet” with better confidence scores rather than bot with “spacy_sklearn”. The following are responses after executing curl -X POST loc...