NLP News Cypher | 04.12.20

Original article can be found here (source): Artificial Intelligence on Medium

I called it RABBIT. My demo is finito. We built an app for those who are interested in streaming APIs, online inference, and transformers in production.

The web app, (of which I’ve shown a glimpse in the past) attempts to do a very difficult balancing act. One of the hardest bottlenecks in deep learning today is leveraging state of the art models in NLP (transformers, RAM expensive) and being able to deploy them in production without making your server or bank account explode. I think I may have figured it out, at least for this app 😎.

What is it?

RABBIT streams tweets from dozens of financial news sources (the usual suspects: Bloomberg, CNBC, WSJ and more) and runs 2 classifiers over them in real-time!

What am I classifying? 1st model classifies 21 topics in finance:


The 2nd model classifies whether the tweet is either bullish, bearish or neutral in stance. What does this mean? It means that if you are an investor/trader holding gold, and the tweet mentions that the price of gold is up, this would be labeled bullish, the inverse bearish, and if you don’t care either way, it’s neutral. In fact, this app is supposed to be personalized to an individual user. Because what you will see is a demo for a general audience, I tried to generalize as much as possible with this classifying schema.

As a result, this assumes first-order logic in classification. Meaning, my logic is not assuming n-order effects. For example, if you hold oil, and oil goes up in price, this is considered bullish, (even though it is possible that the reason oil went up is because of some geo-political conflict which could have negative impact on the market (bearish), this is a hypothetical n-order effect).

What does it run on?

I’ve architected the back-end with the option of expanding both compute and connections if required. The transformers are the distilled version of RoBERTa that were fed over 10K tweets from a custom dataset. Currently, I’m leveraging message queues and an asynchronous framework to help me push tweets out to the user. Shout-out to Adam King for sparking the idea during one of our digital fireside chats. (FYI, you can check out his infamous GPT-2 model here:

RABBIT uses a web-socket connection for the streaming capabilities and is run only on 4 CPU cores. While this compute may seem small, when married with this architecture, it’s actually lightning fast (even while doing online inference with 2 transformers!). Since the web-sockets are connected to the browser and data serving is uni-directional, scaling to the client-side is fairly robust.


Very recently there’s been some domain shift due to the coronavirus altering the news cycle (which has decreased the accuracy of the models). I will continually add more data to mitigate this, even though for now, it performs reasonably well.


Will officially release it tomorrow, April 13th. Check my Twitter for the update. FYI, the app is best experienced during weekly trading hours when the stock market is open so you can see it stream really fast (even though technically you can check it out anytime you want).

Proud of this work. It’s cheap, it’s powerful and it’s fast.

Possible future approaches will be to create a language-model from scratch, and then fine-tune it on the custom dataset I mentioned above. Additionally, would be nice to add more data in a dashboard with a live stock market stream.

How was your week? 😎