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https://github.com/MilaNLProc/contextualized-topic-models

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
https://github.com/MilaNLProc/contextualized-topic-models

bert embeddings multilingual-models multilingual-topic-models neural-topic-models nlp nlp-library nlp-machine-learning text-as-data topic-coherence topic-modeling transformer

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A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.

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README 

        
===========================

Contextualized Topic Models

===========================



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    :alt: Open In Colab



.. image:: https://raw.githubusercontent.com/aleen42/badges/master/src/medium.svg

    :target: https://medium.com/towards-data-science/contextualized-topic-modeling-with-python-eacl2021-eacf6dfa576

    :alt: Medium Blog Post



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        :alt: Video Tutorial



Contextualized Topic Models (CTM) are a family of topic models that use pre-trained representations of language (e.g., BERT) to

support topic modeling. See the papers for details:



* Bianchi, F., Terragni, S., & Hovy, D. (2021). `Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence`. ACL. https://aclanthology.org/2021.acl-short.96/

* Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). `Cross-lingual Contextualized Topic Models with Zero-shot Learning`. EACL. https://www.aclweb.org/anthology/2021.eacl-main.143/



.. image:: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/logo.png

   :align: center

   :width: 200px



Topic Modeling with Contextualized Embeddings

---------------------------------------------



Our new topic modeling family supports many different languages (i.e., the one supported by HuggingFace models) and comes in two versions: **CombinedTM** combines contextual embeddings with the good old bag of words to make more coherent topics; **ZeroShotTM** is the perfect topic model for task in which you might have missing words in the test data and also, if trained with multilingual embeddings, inherits the property of being a multilingual topic model!



The big advantage is that you can use different embeddings for CTMs. Thus, when a new

embedding method comes out you can use it in the code and improve your results. We are not limited

by the BoW anymore.



We also have `Kitty `_! A new submodule that can be used to create a human-in-the-loop

classifier to quickly classify your documents and create named clusters.



.. image:: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/logo_kitty.png

   :align: center

   :width: 200px



Tutorials

---------



You can look at our `medium`_ blog post or start from one of our Colab Tutorials:



.. |colab1_2| image:: https://colab.research.google.com/assets/colab-badge.svg

    :target: https://colab.research.google.com/drive/1fXJjr_rwqvpp1IdNQ4dxqN4Dp88cxO97?usp=sharing

    :alt: Open In Colab



.. |colab2_2| image:: https://colab.research.google.com/assets/colab-badge.svg

    :target: https://colab.research.google.com/drive/1bfWUYEypULFk_4Tfff-Pb_n7-tSjEe9v?usp=sharing

    :alt: Open In Colab



.. |colab3_3| image:: https://colab.research.google.com/assets/colab-badge.svg

    :target: https://colab.research.google.com/drive/1upTRu4zSm1VMbl633n9qkIDA526l22E_?usp=sharing

    :alt: Open In Colab



.. |kitty_colab| image:: https://colab.research.google.com/assets/colab-badge.svg

    :target: https://colab.research.google.com/drive/18mKzaKnmBlBOHb1oiS5MtaTSyq47ys2X?usp=sharing

    :alt: Open In Colab



+--------------------------------------------------------------------------------+------------------+

| Name                                                                           | Link             |

+================================================================================+==================+

| Combined TM on Wikipedia Data (Preproc+Saving+Viz) (stable **v2.3.0**)         | |colab1_2|       |

+--------------------------------------------------------------------------------+------------------+

| Zero-Shot Cross-lingual Topic Modeling (Preproc+Viz) (stable **v2.3.0**)       | |colab2_2|       |

+--------------------------------------------------------------------------------+------------------+

| Kitty: Human in the loop Classifier (High-level usage) (stable **v2.2.0**)     | |kitty_colab|    |

+--------------------------------------------------------------------------------+------------------+

| SuperCTM and  β-CTM (High-level usage) (stable **v2.2.0**)                     | |colab3_3|       |

+--------------------------------------------------------------------------------+------------------+



Overview

--------



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.

+ CTM uses `SBERT`_, you should check it out to better understand how we create embeddings. SBERT allows us to use any embedding model. You might want to check things like `max length `_.



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



.. code-block:: bash



    pip install -U contextualized_topic_models



Models

~~~~~~



An important aspect to take into account is which network you want to use:

the one that combines contextualized embeddings

and the BoW (`CombinedTM `_) or the one that just uses contextualized embeddings (`ZeroShotTM `_)



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



Contextualized Topic Models also support supervision (SuperCTM). You can read more about this on the `documentation `_.



.. image:: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/ctm_both.jpeg

   :align: center

   :width: 800px



We also have `Kitty `_: a utility you can use to do a simpler human in the loop classification of your

documents. This can be very useful to do document filtering. It also works in cross-lingual setting and

thus you might be able to filter documents in a language you don't know!



References

----------



If you find this useful you can cite the following papers :)



**ZeroShotTM**



::



    @inproceedings{bianchi-etal-2021-cross,

        title = "Cross-lingual Contextualized Topic Models with Zero-shot Learning",

        author = "Bianchi, Federico and Terragni, Silvia and Hovy, Dirk  and

          Nozza, Debora and Fersini, Elisabetta",

        booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",

        month = apr,

        year = "2021",

        address = "Online",

        publisher = "Association for Computational Linguistics",

        url = "https://www.aclweb.org/anthology/2021.eacl-main.143",

        pages = "1676--1683",

    }



**CombinedTM**



::



    @inproceedings{bianchi-etal-2021-pre,

        title = "Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence",

        author = "Bianchi, Federico  and

          Terragni, Silvia  and

          Hovy, Dirk",

        booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",

        month = aug,

        year = "2021",

        address = "Online",

        publisher = "Association for Computational Linguistics",

        url = "https://aclanthology.org/2021.acl-short.96",

        doi = "10.18653/v1/2021.acl-short.96",

        pages = "759--766",

    }



Language-Specific and Multilingual

----------------------------------



Some of the examples below use a multilingual embedding model

:code:`paraphrase-multilingual-mpnet-base-v2`.

This means that the representations you are going to use are mutlilingual.

However you might need a broader coverage of languages or just one specific language.

Refer to the page in the documentation to see how to choose a model for another language.

In that case, you can check `SBERT`_ to find the perfect model to use.



Here, you can read more about `language-specific and mulitlingual `_.



Quick Overview

--------------



You should definitely take a look at the `documentation `_

to better understand how these topic models work.



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).

n_components=50 specifies the number of topics.



.. code-block:: python



    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



    qt = TopicModelDataPreparation("all-mpnet-base-v2")



    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) # 50 topics



    ctm.fit(training_dataset) # run the model



    ctm.get_topics(2)



**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)



.. code-block:: python



    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



    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("paraphrase-multilingual-mpnet-base-v2")



    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=768, n_components=50)



    ctm.fit(training_dataset) # run the model



    ctm.get_topics(2)



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.



Using The Topic Models

----------------------



Getting The Topics

~~~~~~~~~~~~~~~~~~



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



.. code-block:: python



    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).



Mono-Lingual Topic Modeling

===========================



If you use **CombinedTM** you need to include the test text for the BOW:



.. code-block:: python



    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



If you use **ZeroShotTM** you do not need to use the `testing_text_for_bow` because if you are using

a different set of test documents, this will create a BoW of a different size. Thus, the best

way to do this is to pass just the text that is going to be given in input to the contexual model:



.. code-block:: python



    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)



Cross-Lingual Topic Modeling

============================



Once you have trained the ZeroShotTM model with multilingual embeddings,

you can use this simple pipeline to predict the topics for documents in a different language (as long as this language

is covered by **paraphrase-multilingual-mpnet-base-v2**).



.. code-block:: python



    # 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



**Advanced Notes:** We do not need to pass the Spanish bag of word: the bag of words of the two languages will not be comparable! We are passing it to the model for compatibility reasons, but you cannot get

the output of the model (i.e., the predicted BoW of the trained language) and compare it with the testing language one.



More Advanced Stuff

-------------------



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.



.. code-block:: python



    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_corpus, vocab, retained_indices = sp.preprocess()



Using Custom Embeddings with Kitty

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



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



.. code-block:: python



    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()))

    custom_embeddings = np.load('custom_embeddings.npy')



    kt = Kitty()

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



    print(kt.pretty_print_word_classes())



Note: Custom embeddings must be numpy.arrays.



Development Team

----------------



* `Federico Bianchi`_  Bocconi University

* `Silvia Terragni`_  University of Milan-Bicocca

* `Dirk Hovy`_  Bocconi University



Software Details

----------------



* Free software: MIT license

* Documentation: https://contextualized-topic-models.readthedocs.io.

* Super big shout-out to `Stephen Carrow`_ for creating the awesome https://github.com/estebandito22/PyTorchAVITM package from which we constructed the foundations of this package. We are happy to redistribute this software again under the MIT License.



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 :)



.. _pytorch: https://pytorch.org/get-started/locally/

.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _preprocessing: https://github.com/MilaNLProc/contextualized-topic-models#preprocessing

.. _cross-lingual-topic-modeling: https://github.com/MilaNLProc/contextualized-topic-models#cross-lingual-topic-modeling

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage

.. _`Stephen Carrow` : https://github.com/estebandito22

.. _`rbo` : https://github.com/dlukes/rbo

.. _Federico Bianchi: https://federicobianchi.io

.. _Silvia Terragni: https://silviatti.github.io/

.. _Dirk Hovy: https://dirkhovy.com/

.. _SBERT: https://www.sbert.net/docs/pretrained_models.html

.. _HuggingFace: https://huggingface.co/models

.. _UmBERTo: https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1

.. _medium: https://fbvinid.medium.com/contextualized-topic-modeling-with-python-eacl2021-eacf6dfa576