Quick NLP Labelling with Bulk
An NLP POC in an afternoon.
So you have an internal free-text dataset, but you don’t have labels, and you don’t know how easy it will be to make them, enter bulk.
A great way to estimate how difficult a Machine Learning project will be, is to see if the clusters align with how you’d want to break down the classes. Or for a binary problem, if the class boundary you’d be interested in already at least partially aligns with a single cluster.
Bulk stands out for the quality of its EDA (exploratory data analysis), the scatter chart is made with bokeh and enables arbitrary zooming in and out. To go from a highly granular view, to the dataset ‘from 1 million miles out’. You can also colour by any set of keywords.
To colour by keywords you specify them from the terminal when running the program:
python -m bulk text ready.csv --keywords "F1,hair,sun,fight,rain"It differentiates itself from equivalent tools by the ability to then apply quick labels. Taking you out of EDA and into action. It is un-opinionated, meaning you can apply your own choice of dimensionality reduction (umap, t-sne, take your pick).
This design choice also makes it much more performant than some equivalent tools like the tensorboard embedding projector. The browser only has to receive 2 dimensions worth of data (x and y coordinates), instead of the several thousand of the original vector, greatly reducing the required data transfer from server to client/browser.
After highlighting a section of the chart, you can export a named csv (named with the label) store of those records, ready to use to train a model.
The Dataset
I’ve chosen an open-source twitter dataset (from around 2009). The 30k sample (post dimensionality reduction, with raw tweets) used in the demo is available at:
https://github.com/GeorgePearse/bulk-demo/blob/main/ready (1).csv
The biggest advantage of this dataset is that there’s a natural limit to the size of the free text in tweets. Short snippets are where the simple UI of bulk really shines.
The Code
The below code uses the SentenceTransformer API which is written on top of Hugging Face. I find Hugging Face unreasonably awkward to use, but this thin API is great for NLP POCs.
The Pre-Trained Model
Plucked from the sbert website, which will have plugged it from hugging-face.
Huge credit to Vincent Warmerdam for creating bulk and a set of other well-defined, clever tools. Check out his GitHub at https://github.com/koaning.
Please clap and follow if this content interests you. I’ll be trying to focus on longer form content and projects over the next few months. Closing the gap between hobby project and production application.
