contextualized-topic-models 1.8.0

README

Make sure you read the doc a bit. The cross-lingual topic modeling requires to use a ZeroShot model and it is trained only on ONE language; with the power of multilingual BERT it can then be used to predict the topics of documents in unseen languages. For more details, you can read the two papers mentioned above.

Jump start Tutorial

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). 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.

Overview

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

Contextual neural topic models can be easily instantiated using few parameters (although there is a wide range of parameters you can use to change the behaviour of the neural topic model). When you generate embeddings with BERT remember that there is a maximum length and for documents that are too long some words will be ignored.

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(input_size=len(qt.vocab), bert_input_size=embedding_dimension, n_components=number_of_topics)

CombinedTM:

CombinedTM(input_size=len(qt.vocab), bert_input_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

Mono vs Multilingual Embeddings: Which Embeddings Should I Use?

All the examples below use a multilingual embedding model distiluse-base-multilingual-cased. If you are doing topic modeling in English, you SHOULD use the English sentence-bert model, bert-base-nli-mean-tokens. In that case, it’s really easy to update the code to support monolingual English topic modeling.

qt = TopicModelDataPreparation("bert-base-nli-mean-tokens")

In general, our package should be able to support all the models described in the sentence transformer package and in HuggingFace.

Zero-Shot Cross-Lingual Topic Modeling

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 the paper (https://arxiv.org/pdf/2004.07737v1.pdf)

from contextualized_topic_models.models.ctm import ZeroShotTM
from contextualized_topic_models.utils.data_preparation import QuickText
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.create_training_set(text_for_contextual, text_for_bow)

ctm = ZeroShotTM(input_size=len(qt.vocab), bert_input_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.

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

testing_dataset = qt.create_test_set(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

Showing The Topic Word Cloud

You can also create a word cloud of the topic!

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

Combined Topic Modeling

Here is how you can use the CombinedTM. This is a standard topic model that also uses BERT.

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

qt = TopicModelDataPreparation("bert-base-nli-mean-tokens")

training_dataset = qt.create_training_set(list_of_unpreprocessed_documents, list_of_preprocessed_documents)

ctm = CombinedTM(input_size=len(qt.vocab), bert_input_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).

More Advanced Stuff

training_dataset = qt.create_test_set(testing_text_for_contextual, 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)

qt = TopicModelDataPreparation()

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

self.vectorizer = CountVectorizer() #from sklearn

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

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()

from contextualized_topic_models.utils.preprocessing import WhiteSpacePreprocessing

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

Development Team

• Federico Bianchi <f.bianchi@unibocconi.it> Bocconi University

• Silvia Terragni <s.terragni4@campus.unimib.it> University of Milan-Bicocca

• Dirk Hovy <dirk.hovy@unibocconi.it> Bocconi University

References

If you use this in a research work please cite these papers:

ZeroShotTM

@inproceedings{bianchi2020crosslingual,
    title={Cross-lingual Contextualized Topic Models with Zero-shot Learning},
    author={Federico Bianchi and Silvia Terragni and Dirk Hovy and Debora Nozza and Elisabetta Fersini},
    booktitle={EACL},
    year={2021}
}

CombinedTM

@article{bianchi2020pretraining,
    title={Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence},
    author={Federico Bianchi and Silvia Terragni and Dirk Hovy},
    year={2020},
   journal={arXiv preprint arXiv:2004.03974},
}

ZeroShot Topic Model

Combined Topic Model

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template. To ease the use of the library we have also included the rbo package, all the rights reserved to the author of that package.

Note

Remember that this is a research tool :)

1.8.0 (2021-01-11)

• novel way to handle text, we now allow for an easy usage of training and testing data

• better visualization of the training progress and of the sampling process

• removed old stuff from the documentation

1.7.1 (2020-12-17)

• some minor updates to the documentation

• adding a new method to visualize the topic using a wordcloud

• save and load will now generate a warning since the feature has not been tested

1.7.0 (2020-12-10)

• adding a new and much simpler way to handle text for topic modeling

1.6.0 (2020-11-03)

• introducing the two different classes for ZeroShotTM and CombinedTM

• depracating CTM class in favor of ZeroShotTM and CombinedTM

1.5.3 (2020-11-03)

• adding support for Windows encoding by defaulting file load to UTF-8

1.5.2 (2020-11-03)

• updated sentence-transformers version to 0.3.6

• beta support for model saving and loading

• new evaluation metrics based on coherence

1.5.0 (2020-09-14)

• Introduced a method to predict the topics for a set of documents (supports multiple sampling to reduce variation)

• Adding some features to bert embeddings creation like increased batch size and progress bar

• Supporting training directly from lists without the need to deal with files

• Adding a simple quick preprocessing pipeline

1.4.3 (2020-09-03)

• Updating sentence-transformers package to avoid errors

1.4.2 (2020-08-04)

• Changed the encoding on file load for the SBERT embedding function

1.4.1 (2020-08-04)

• Fixed bug over sparse matrices

1.4.0 (2020-08-01)

• New feature handling sparse bow for optimized processing

• New method to return topic distributions for words

1.0.0 (2020-04-05)

• Released models with the main features implemented

0.1.0 (2020-04-04)

• First release on PyPI.