archival notice

This was a fun project for a while, but it's become a pain to maintain all the different backends. If you're looking for visualisation tools, check https://github.com/koaning/cluestar and consider https://github.com/koaning/cluestar if you're interested in the embeddings going forward.

whatlies

A library that tries to help you to understand (note the pun).

"What lies in word embeddings?"

This small library offers tools to make visualisation easier of both word embeddings as well as operations on them.

Produced

This project was initiated at Rasa as a by-product of our efforts in the developer advocacy and research teams. The project is maintained by koaning in order to support more use-cases.

Features

This library has tools to help you understand what lies in word embeddings. This includes:

• simple tools to create (interactive) visualisations
• support for many language backends including spaCy, fasttext, tfhub, huggingface and bpemb
• lightweight scikit-learn featurizer support for all these backends

Installation

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 some of the possible installation settings you could go for.

pip install whatlies[spacy]
pip install whatlies[tfhub]
pip install whatlies[transformers]

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.

Getting Started

More in depth getting started guides can be found on the documentation page.

Examples

The idea is that you can load embeddings from a language backend and use mathematical operations on it.

from whatlies import EmbeddingSet
from whatlies.language import SpacyLanguage

lang = SpacyLanguage("en_core_web_md")
words = ["cat", "dog", "fish", "kitten", "man", "woman",
         "king", "queen", "doctor", "nurse"]

emb = EmbeddingSet(*[lang[w] for w in words])
emb.plot_interactive(x_axis=emb["man"], y_axis=emb["woman"])

You can even do fancy operations. Like projecting onto and away from vector embeddings! You can perform these on embeddings as well as sets of embeddings. In the example below we attempt to filter away gender bias using linear algebra operations.

orig_chart = emb.plot_interactive('man', 'woman')

new_ts = emb | (emb['king'] - emb['queen'])
new_chart = new_ts.plot_interactive('man', 'woman')

There's also things like pca and umap.

from whatlies.transformers import Pca, Umap

orig_chart = emb.plot_interactive('man', 'woman')
pca_plot = emb.transform(Pca(2)).plot_interactive()
umap_plot = emb.transform(Umap(2)).plot_interactive()

pca_plot | umap_plot

Scikit-Learn Support

Every language backend in this video is available as a scikit-learn featurizer as well.

import numpy as np
from whatlies.language import BytePairLanguage
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

pipe = Pipeline([
    ("embed", BytePairLanguage("en")),
    ("model", LogisticRegression())
])

X = [
    "i really like this post",
    "thanks for that comment",
    "i enjoy this friendly forum",
    "this is a bad post",
    "i dislike this article",
    "this is not well written"
]

y = np.array([1, 1, 1, 0, 0, 0])

pipe.fit(X, y)

Documentation

To learn more and for a getting started guide, check out the documentation.

Similar Projects

There are some similar projects out and we figured it fair to mention and compare them here.

Local Development

If you want to develop locally you can start by running this command.

make develop

Documentation

This is generated via

make docs

Citation

Please use the following citation when you found whatlies helpful for any of your work (find the whatlies paper here):

@inproceedings{warmerdam-etal-2020-going,
    title = "Going Beyond {T}-{SNE}: Exposing whatlies in Text Embeddings",
    author = "Warmerdam, Vincent  and
      Kober, Thomas  and
      Tatman, Rachael",
    booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.nlposs-1.8",
    doi = "10.18653/v1/2020.nlposs-1.8",
    pages = "52--60",
    abstract = "We introduce whatlies, an open source toolkit for visually inspecting word and sentence embeddings. The project offers a unified and extensible API with current support for a range of popular embedding backends including spaCy, tfhub, huggingface transformers, gensim, fastText and BytePair embeddings. The package combines a domain specific language for vector arithmetic with visualisation tools that make exploring word embeddings more intuitive and concise. It offers support for many popular dimensionality reduction techniques as well as many interactive visualisations that can either be statically exported or shared via Jupyter notebooks. The project documentation is available from https://koaning.github.io/whatlies/.",
}