Skip to main content


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

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'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 glancerasa-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 ac…
Recent posts

Performance of a Data Structure

Why data structures matter The fact is that programs are all about processing data. Data structures are referred to how data is organized which affects the time of executing a program.
How to measure the performance of a data structure In order to measure "how fast"/efficiency/performance of a data structure, we measure the performance of its operations. There are four basic operations including reading, searching, insertion, and deletion. A pure time consuming is not used for the measuring because it is not reliable depending on the hardware that it is run on. But instead, we use the term time complexity which refers to how many steps an operation takes.
An example of how a single rule can affect efficiency Let's compare two data structures: Array and Set (with N elements).
1. Array - Reading: 1 step (because the computer has the ability to jump to any particular index in the array)
- Searching: N steps (the worst case with linear search)
- Insertion: N + 1 steps (the wo…

My must-have apps for daily work

There is no doubt that cool apps can help us be more productive and enjoyable at work. For the time being, I really love the following apps which are used by me almost every day.
1. A personal Kanban In fact, a personal kanban is the most useful app for me. Why does it matter? It is not just a to-do list, but it keeps me motivated every day because it helps me be able to know what my "big picture" is. I usually set up my plans together with a path to reach them. KanbanFlow is my preferred tool.
2. A terminal Needless to say, a terminal is a must-have app for every developer, especially the ones use macOS/Linux. Due to its importance, I love to decorate and enhance it to be super exciting with various tools such as iTermoh-my-zsh, and thefuck. ;)

3. A documentation "ecosystem" As a developer, I can not remember all things that I have experimented a day. Moreover, a document is really useful for sharing an idea with other people. I use the set of tools for helping…

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 localhost:5000/parse -d '{&qu…

Sharing a virtualenv across several Python projects using Pipenv

There is a standard library for all projects in Python. However, several projects don’t always have the same dependencies all the time. That is where virtual environments come to play.

You can follow this official document to use two separated tools virtualenv and pip to fulfill that need. My preferred alternative is to use pipenv. Pipenv is easy to use and convenient. The following are my steps to make a shared virtualenv for my all projects which requires the same dependencies.

Step 1. Create an isolated virtualenv.
python -m venv my-shared-env Step 2. Create a symbolic link to the created virtualenv.
cd project_1 ln -s ~/.local/share/virtualenvs/my-shared-env .venv I have encountered the following issue at step 1.
FileNotFoundError: [Errno 2] No such file or directory: '{my_project_path}/.venv/bin/pip': '{my_project_path}/.venv/bin/pip' The root cause was I tried to create virtualenv by running pipenv install and renaming the generated virtualenv to a new name but …

"Java & Agile" was renamed "Python & Machine Learning"

I have started blogging since 2014. In fact, I just happened to know a very nice free blogging course of John Sonmez. Time flies!

I currently work at a startup to build an AI chatbot. It was where Python and Machine Learning become my most focus from now on. That also was a reason why I wanted to rename my blog theme. I still keep using Java in my projects though.

Thank you all for reading my blog so far. It motivates a lot to keep me carrying on this habit.

A new topic "Python & Machine Learning" is added to my blog theme today. Yay!

When we don't see the sun, we see other stars

What are your motivations for creativity? - I want to make a change.
- It makes me happy! It is a need of my mind.
How to be creative for a thing? There are two steps: - See the thing as every people see it - Think about a new different thing from it How to think about a new different thing? There are two ways: - Forget all things you have already known. - A whack on the side of your head. ;)
This was what I have learned from the following great book:
Well! A physical whack on the side of your head is needed sometimes but the meaning behind is that you need to break these 9 following locks on your mind. Remove them!
The lock #1: "The correct answer" We all learn from schools that there is only one correct answer to a question. For example, a proposition is only true or false in Algebra. In reality, there are always some answers to a question basing on a point of view. For example, number 6 becomes number 9 if you look it in the opposite.
The lock #2: "That is not logic&…