embedding-explorer 0.5.0

Tools for interactive visual inspection of semantic embeddings.

embedding-explorer

Tools for interactive visual exploration of semantic embeddings.

Installation

Install embedding-explorer from PyPI:

pip install embedding-explorer

Explorer

embedding-explorer comes with a web application built for exploring semantic relations in a corpus with the help of embeddings. In this section I will show a couple of examples of running the app with different embedding models and corpora.

Static Word Embeddings

Let's say that you would like to explore semantic relations by investigating word embeddings generated with Word2Vec. You can do this by passing the vocabulary of the model and the embedding matrix to embedding-explorer.

For this example I will use Gensim, which can be installed from PyPI:

pip install gensim

We will download GloVe Twitter 25 from gensim's repositories.

from gensim import downloader
from embedding_explorer import show_explorer

model = downloader.load("glove-twitter-25")
vocabulary = model.index_to_key
embeddings = model.vectors
show_explorer(corpus=vocabulary, embeddings=embeddings)

This will open a new browser window with the Explorer, where you can enter seed words and set the number of associations that you would like to see on the screen.

Dynamic Embedding Models

If you want to explore relations in a corpus using let's say a sentence transformer, which creates contextually aware embeddings, you can do so by specifying a scikit-learn compatible vectorizer model instead of passing along an embedding matrix.

One clear advantage here is that you can input arbitrary sequences as seeds instead of a predetermined set of texts.

We are going to use the package embetter for embedding documents.

pip install embetter[sentence-trf]

I decided to examine four-grams in the 20newsgroups dataset. We will limit the number of four-grams to 4000 so we only see the most relevant ones.

from embetter.text import SentenceEncoder
from embedding_explorer import show_explorer
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

corpus = fetch_20newsgroups(
    remove=("headers", "footers", "quotes"),
).data
# We will use CountVectorizer for obtaining the possible n-grams
four_grams = (
    CountVectorizer(
        stop_words="english", ngram_range=(4, 4), max_features=4000
    )
    .fit(corpus)
    .get_feature_names_out()
)

model = SentenceEncoder()
show_explorer(corpus=four_grams, vectorizer=model)

Dashboard

If you have multiple models to examine the same corpus or multiple corpora, that you want to examine with the same model, then you can create a dashboard containing all of these options, that users will be able to click on and that takes them to the appropriate explorer page.

For this we will have to assemble these options into a list of Card object, that contain the information about certain pages. Cards have the same parameters as the show_explorer function.

In the following example I will set up two different sentence transformers with the same corpus from the previous example.

from embetter.text import SentenceEncoder
from embedding_explorer import show_dashboard, Card

cards = [
    Card("MiniLM", corpus=four_grams, vectorizer=SentenceEncoder("all-MiniLM-L12-v2")),
    Card("MPNET", corpus=four_grams, vectorizer=SentenceEncoder("all-mpnet-base-v2")),
]
show_dashboard(cards)

API Reference

embedding_explorer.show_explorer(corpus, vectorizer=None, embeddings=None, port=8050, fuzzy_search=False)

Visually inspect word embedding model with the Explorer.

Parameters

Parameter	Type	Description	Default
corpus	Iterable[str]	Texts you intend to search in with the semantic explorer.	-
vectorizer	Optional[BaseEstimator]	Model to vectorize texts with. If not supplied, the model is assumed to be a static word embedding model, and the embeddings parameter has to be supplied.	None
embeddings	Optional[ndarray]	Embeddings of the texts in the corpus. If not supplied, embeddings will be calculated using the vectorizer.	None
port	int	Port for the app to run on.	8050
fuzzy_search	bool	Specifies whether you want to enable fuzzy search in the vocabulary. Recommended for production use, but indexing takes time, resulting in longer startup time.	False

Returns

Type	Description
Thread or None	If the app runs in a Jupyter notebook, work goes on in a background thread, and this thread is returned.

embedding_explorer.show_dashboard(cards)

Launch a dashboard with the given model cards.

Parameters

Parameter	Type	Description	Default
cards	list[Card]	Contains descriptions of model cards that should appear in the dashboard.	-
port	int	Port for the app to run on.	8050

Returns

Type	Description
Thread or None	If the app runs in a Jupyter notebook, work goes on in a background thread, and this thread is returned.

class embedding_explorer.Card(corpus, vectorizer=None, embeddings=None, fuzzy_search=False)

Contains information about an embedding model card that should be displayed on the dashboard.

Parameters

Parameter	Type	Description	Default
corpus	Iterable[str]	Texts you intend to search in with the semantic explorer.	-
vectorizer	Optional[BaseEstimator]	Model to vectorize texts with. If not supplied, the model is assumed to be a static word embedding model, and the embeddings parameter has to be supplied.	None
embeddings	Optional[ndarray]	Embeddings of the texts in the corpus. If not supplied, embeddings will be calculated using the vectorizer.	None
fuzzy_search	bool	Specifies whether you want to enable fuzzy search in the vocabulary. Recommended for production use, but indexing takes time, resulting in longer startup time.	False