contextualized-topic-models 2.0.0

Published Papers

CombinedTM has been accepted at ACL2021 and ZeroShotTM has been accepted at EACL2021!

If you want to replicate our results, you can use our code. You will find the W1 dataset in the colab and here: https://github.com/vinid/data, if you need the W2 dataset, send us an email (it is a bit bigger than W1 and we could not upload it on github).

TL;DR

• In CTMs we have two models. CombinedTM and ZeroShotTM, which have different use cases.

• CTMs work better when the size of the bag of words has been restricted to a number of terms that does not go over 2000 elements. This is because we have a neural model that reconstructs the input bag of word, Moreover, in CombinedTM we project the contextualized embedding to the vocab space, the bigger the vocab the more parameters you get, with the training being more difficult and prone to bad fitting. This is NOT a strict limit, however, consider preprocessing your dataset. We have a preprocessing pipeline that can help you in dealing with this.

• Check the contextual model you are using, the multilingual model one used on English data might not give results that are as good as the pure English trained one.

• Preprocessing is key. If you give a contextual model like BERT preprocessed text, it might be difficult to get out a good representation. What we usually do is use the preprocessed text for the bag of word creating and use the NOT preprocessed text for BERT embeddings. Our preprocessing class can take care of this for you.

Installing

Important: If you want to use CUDA you need to install the correct version of the CUDA systems that matches your distribution, see pytorch.

Install the package using pip

pip install -U contextualized_topic_models

An important aspect to take into account is which network you want to use: the one that combines BERT and the BoW or the one that just uses BERT. It’s easy to swap from one to the other:

ZeroShotTM:

ZeroShotTM(bow_size=len(qt.vocab), contextual_size=embedding_dimension, n_components=number_of_topics)

CombinedTM:

CombinedTM(bow_size=len(qt.vocab), contextual_size=embedding_dimension, n_components=number_of_topics)

But remember that you can do zero-shot cross-lingual topic modeling only with the ZeroShotTM model. See cross-lingual-topic-modeling

Multilingual

Some of the examples below use a multilingual embedding model distiluse-base-multilingual-cased. This means that the representations you are going to use are mutlilinguals (16 languages). However you might need a broader coverage of languages. In that case, you can check SBERT to find a model you can use.

English

If you are doing topic modeling in English, you SHOULD use an English sentence-bert model, for example paraphrase-distilroberta-base-v1. In that case, it’s really easy to update the code to support monolingual English topic modeling. If you need other models you can check SBERT for other models.

qt = TopicModelDataPreparation(" paraphrase-distilroberta-base-v1")

Language-Specific

In general, our package should be able to support all the models described in the sentence transformer package and in HuggingFace. You need to take a look at HuggingFace models and find which is the one for your language. For example, for Italian, you can use UmBERTo. How to use this in the model, you ask? well, just use the name of the model you want instead of the english/multilingual one:

qt = TopicModelDataPreparation("Musixmatch/umberto-commoncrawl-cased-v1")

Combined Topic Model

Here is how you can use the CombinedTM. This is a standard topic model that also uses contextualized embeddings. The good thing about CombinedTM is that it makes your topic much more coherent (see the paper https://arxiv.org/abs/2004.03974).

from contextualized_topic_models.models.ctm import CombinedTM
from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
from contextualized_topic_models.utils.data_preparation import bert_embeddings_from_file
from contextualized_topic_models.datasets.dataset import CTMDataset

qt = TopicModelDataPreparation("paraphrase-distilroberta-base-v1")

training_dataset = qt.fit(text_for_contextual=list_of_unpreprocessed_documents, text_for_bow=list_of_preprocessed_documents)

ctm = CombinedTM(bow_size=len(qt.vocab), contextual_size=768, n_components=50)

ctm.fit(training_dataset) # run the model

ctm.get_topics()

Advanced Notes: Combined TM combines the BoW with SBERT, a process that seems to increase the coherence of the predicted topics (https://arxiv.org/pdf/2004.03974.pdf).

Zero-Shot Topic Model

Our ZeroShotTM can be used for zero-shot topic modeling. It can handle words that are not used during the training phase. More interestingly, this model can be used for cross-lingual topic modeling (See next sections)! See the paper (https://www.aclweb.org/anthology/2021.eacl-main.143)

from contextualized_topic_models.models.ctm import ZeroShotTM
from contextualized_topic_models.utils.data_preparation import TopicModelDataPreparation
from contextualized_topic_models.utils.data_preparation import bert_embeddings_from_file
from contextualized_topic_models.datasets.dataset import CTMDataset

text_for_contextual = [
    "hello, this is unpreprocessed text you can give to the model",
    "have fun with our topic model",
]

text_for_bow = [
    "hello unpreprocessed give model",
    "fun topic model",
]

qt = TopicModelDataPreparation("distiluse-base-multilingual-cased")

training_dataset = qt.fit(text_for_contextual=text_for_contextual, text_for_bow=text_for_bow)

ctm = ZeroShotTM(bow_size=len(qt.vocab), contextual_size=512, n_components=50)

ctm.fit(training_dataset) # run the model

ctm.get_topics()

As you can see, the high-level API to handle the text is pretty easy to use; text_for_bert should be used to pass to the model a list of documents that are not preprocessed. Instead, to text_for_bow you should pass the preprocessed text used to build the BoW.

Advanced Notes: in this way, SBERT can use all the information in the text to generate the representations.

Getting The Topics

Once the model is trained, it is very easy to get the topics!

ctm.get_topics()

Predicting Topics For Unseen Documents

The transform method will take care of most things for you, for example the generation of a corresponding BoW by considering only the words that the model has seen in training. However, this comes with some bumps when dealing with the ZeroShotTM, as we will se in the next section.

You can, however, manually load the embeddings if you like (see the Advanced part of this documentation).

testing_dataset = qt.transform(text_for_contextual=testing_text_for_contextual, text_for_bow=testing_text_for_bow)

# n_sample how many times to sample the distribution (see the doc)
ctm.get_doc_topic_distribution(testing_dataset, n_samples=20) # returns a (n_documents, n_topics) matrix with the topic distribution of each document

testing_dataset = qt.transform(text_for_contextual=testing_text_for_contextual)

# n_sample how many times to sample the distribution (see the doc)
ctm.get_doc_topic_distribution(testing_dataset, n_samples=20)

# here we have a Spanish document
testing_text_for_contextual = [
    "hola, bienvenido",
]

# since we are doing multilingual topic modeling, we do not need the BoW in
# ZeroShotTM when doing cross-lingual experiments (it does not make sense, since we trained with an english Bow
# to use the spanish BoW)
testing_dataset = qt.transform(testing_text_for_contextual)

# n_sample how many times to sample the distribution (see the doc)
ctm.get_doc_topic_distribution(testing_dataset, n_samples=20) # returns a (n_documents, n_topics) matrix with the topic distribution of each document

PyLda Visualization

We support pyLDA visualizations we few lines of code!

import pyLDAvis as vis

lda_vis_data = ctm.get_ldavis_data_format(tp.vocab, training_dataset, n_samples=10)

ctm_pd = vis.prepare(**lda_vis_data)
vis.display(ctm_pd)

Showing The Topic Word Cloud

You can also create a word cloud of the topic!

ctm.get_wordcloud(topic_id=47, n_words=15)

Can I load my own embeddings?

Sure, here is a snippet that can help you. You need to create the embeddings (for bow and contextualized) and you also need to have the vocab and an id2token dictionary (maps integers ids to words).

qt = TopicModelDataPreparation()

training_dataset = qt.load(contextualized_embeddings, bow_embeddings, id2token)
ctm = CombinedTM(bow_size=len(vocab), contextual_size=768, n_components=50)
ctm.fit(training_dataset) # run the model
ctm.get_topics()

You can give a look at the code we use in the TopicModelDataPreparation object to get an idea on how to create everything from scratch. For example:

vectorizer = CountVectorizer() #from sklearn

train_bow_embeddings = vectorizer.fit_transform(text_for_bow)
train_contextualized_embeddings = bert_embeddings_from_list(text_for_contextual, "chosen_contextualized_model")
vocab = vectorizer.get_feature_names()
id2token = {k: v for k, v in zip(range(0, len(vocab)), vocab)}

Evaluation

We have also included some of the metrics normally used in the evaluation of topic models, for example you can compute the coherence of your topics using NPMI using our simple and high-level API.

from contextualized_topic_models.evaluation.measures import CoherenceNPMI

with open('preprocessed_documents.txt', "r") as fr:
    texts = [doc.split() for doc in fr.read().splitlines()] # load text for NPMI

npmi = CoherenceNPMI(texts=texts, topics=ctm.get_topic_lists(10))
npmi.score()

Preprocessing

Do you need a quick script to run the preprocessing pipeline? We got you covered! Load your documents and then use our SimplePreprocessing class. It will automatically filter infrequent words and remove documents that are empty after training. The preprocess method will return the preprocessed and the unpreprocessed documents. We generally use the unpreprocessed for BERT and the preprocessed for the Bag Of Word.

from contextualized_topic_models.utils.preprocessing import WhiteSpacePreprocessing

documents = [line.strip() for line in open("unpreprocessed_documents.txt").readlines()]
sp = WhiteSpacePreprocessing(documents, "english")
preprocessed_documents, unpreprocessed_documents, vocab = sp.preprocess()