A library that tries help you to understand. "What lies in word embeddings?"
If you prefer a video tutorial before reading the getting started guide watch this;
This project was initiated at Rasa as a fun side project that supports the research and developer advocacy teams at Rasa.
It is maintained by Vincent D. Warmerdam, Research Advocate at Rasa.
What it Does¶
This small library offers tools to make visualisation easier of both word embeddings as well as operations on them. This should be considered an experimental project.
This library will allow you to make visualisations of transformations of word embeddings. Some of these transformations are linear algebra operators.
Note that these charts are fully interactive. Click. Drag. Zoom in. Zoom out.
But we also support other operations. Like pca and umap;
Just like before. Click. Drag. Zoom in. Zoom out.
You can install the package via pip;
pip install whatlies
This will install the base dependencies. Depending on the transformers and language backends that you'll be using you may want to install more. Here's all the possible installation settings you could go for.
pip install whatlies[base] pip install whatlies[tfhub] pip install whatlies[transformers] pip install whatlies[ivis] pip install whatlies[opentsne] pip install whatlies[sense2vec]
If you want it all you can also install via;
pip install whatlies[all]
Note that this will install dependencies but it will not install all the language models you might want to visualise. For example, you might still need to manually download spaCy models if you intend to use that backend.
There are some projects out there who are working on similar tools and we figured it fair to mention and compare them here.
Julia Bazińska & Piotr Migdal Web App¶
The original inspiration for this project came from this web app and this pydata talk. It is a web app that takes a while to load but it is really fun to play with. The goal of this project is to make it easier to make similar charts from jupyter using different language backends.
From google there's the tensorflow projector project. It offers highly interactive 3d visualisations as well as some transformations via tensorboard.
- The tensorflow projector will create projections in tensorboard, which you can also load into jupyter notebook but whatlies makes visualisations directly.
- The tensorflow projector supports interactive 3d visuals, which whatlies currently doesn't.
- Whatlies offers lego bricks that you can chain together to get a visualisation started. This also means that you're more flexible when it comes to transforming data before visualising it.
From Uber AI Labs there's parallax which is described in a paper here. There's a common mindset in the two tools; the goal is to use arbitrary user defined projections to understand embedding spaces better. That said, some differences that are worth to mention.
- It relies on bokeh as a visualisation backend and offers a lot of visualisation types (like radar plots). Whatlies uses altair and tries to stick to simple scatter charts. Altair can export interactive html/svg but it will not scale as well if you've drawing many points at the same time.
- Parallax is meant to be run as a stand-alone app from the command line while Whatlies is meant to be run from the jupyter notebook.
- Parallax gives a full user interface while Whatlies offers lego bricks that you can chain together to get a visualisation started.
- Whatlies relies on language backends (like spaCy, huggingface) to fetch word embeddings. Parallax allows you to instead fetch raw files on disk.
- Parallax has been around for a while, Whatlies is more new and therefore more experimental.
If you want to develop locally you can start by running this command after cloning.