Kitty: Human-in-the-loop Classification with Contextualized Topic Models

Kitty is a utility to generate a simple topic classifier from a topic model. It first runs a CTM instance on the data for you and you can then select and label a set of topics of interest. Once this is done, you can apply this selection to a wider range of documents.

Please cite the following papers if you use Kitty:

To simply put it, Kitty makes use of ZeroShotTM to extract topic from your data. Kitty runs some very simple preprocessing on your data to remove words that might not be too useful. We also have a google colab tutorial.

Open In Colab


In this example we use an english embedding model, however you might need langauge-specific models to do this, check out the related section of the documentation

from contextualized_topic_models.models.kitty_classifier import Kitty

# read the training data
training_set = list(map(lambda x : x.strip(), open("train_data").readlines()))

kt = Kitty()
kt.train(training, topics=5, epochs=1, stopwords_list=["stop", "words"], embedding_model="paraphrase-distilroberta-base-v2")


This could probably output topics like these ones:

0   family, plant, types, type, moth
1   district, mi, area, village, west
2   released, series, television, album, film
3   school, station, historic, public, states
4   born, football, team, played, season

Now, you can then use a simple dictionary to assign the topics to some labels. For example, topic 0 seems to be describing nature related things.

kt.assigned_classes = {0 : "nature", 1 : "location",
                       2 : "entertainment", 3 : "shop/offices", 4: "sport"}

kt.predict(["the village of Puza is a very nice village in Italy"])

>> location

kt.predict(["Pussetto is a soccer player that currently plays for Udiense Calcio"])

>> sport

If you are using a jupyter notebook, you can use the widget to fill in the labels.


Cross-Lingual Support

A nice feature of Kitty is that it can be used to filter documents in different languages. Assume you have access to a large corpus of Italian documents and a smaller corpus of English documents. You can run Kitty on the English documents, map the labels and apply Kitty on the Italian documents. It is enough to change the embedding model.

from contextualized_topic_models.models.kitty_classifier import Kitty

# read the training data
training = list(map(lambda x : x.strip(), open("train_data").readlines()))

# define kitty with a multilingual embedding model
kt = Kitty(embedding_model="paraphrase-multilingual-mpnet-base-v2",  contextual_size=768)

kt.train(training, 5, stopwords_list=["stopwords"]) # train a topic model with 5 topics


You can then apply the mapping as we did before and predict in different languages:

kt.predict(["Pussetto è un calciatore che attualmente gioca per l'Udinese Calcio"])

>> sport

You should refer to SBERT Pretrained Models to know if the languages you want to use are supported by SBERT.

Using Custom Embeddings with Kitty

Do you have custom embeddings and want to use them for faster results? Just give them to Kitty!

from contextualized_topic_models.models.kitty_classifier import Kitty
import numpy as np

# read the training data
training_data = list(map(lambda x : x.strip(), open("train_data").readlines()))
customer_embeddings = np.load('customer_embeddings.npy')

kt = Kitty()
kt.train(training_data, custom_embeddings=customer_embeddings, stopwords_list=["stopwords"])


Note: Custom embeddings must be numpy.arrays.

What Makes Kitty Different from Other Topic Models?

Nothing! It just offers a user-friendly utility that makes use of the ZeroShotTM model in the backend.