Metadata-Version: 2.1
Name: contextualized-topic-models
Version: 1.5.3
Summary: Contextualized Topic Models
Home-page: https://github.com/MilaNLProc/contextualized-topic-models
Author: Federico Bianchi
Author-email: f.bianchi@unibocconi.it
License: MIT license
Description: ===========================
        Contextualized Topic Models
        ===========================
        
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        .. image:: https://colab.research.google.com/assets/colab-badge.svg
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            :alt: Open In Colab
        
        
        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:
        
        * `Cross-lingual Contextualized Topic Models with Zero-shot Learning` https://arxiv.org/pdf/2004.07737v1.pdf
        * `Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence` https://arxiv.org/pdf/2004.03974.pdf
        
        
        .. image:: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/logo.png
           :align: center
           :width: 200px
        
        README
        ------
        
        Make **sure** you read the doc a bit. The cross-lingual topic modeling requires to use a "contextual" 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
        -------------------
        
        .. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
            :target: https://colab.research.google.com/drive/1V0tkpJL1yhiHZUJ_vwQRu6I7_svjw1wb?usp=sharing
            :alt: Open In Colab
        
        
        +-----------------------------------+---------------------+
        | Name                              | Link                |
        +===================================+=====================+
        | Wikipedia Topic Modeling          | |colab|             |
        +-----------------------------------+---------------------+
        
        Combined Topic Model
        --------------------
        
        .. image:: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/lm_topic_model.png
           :target: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/lm_topic_model.png
           :align: center
           :width: 400px
        
        Fully Contextual Topic Model
        ----------------------------
        
        .. image:: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/lm_topic_model_multilingual.png
           :target: https://raw.githubusercontent.com/MilaNLProc/contextualized-topic-models/master/img/lm_topic_model_multilingual.png
           :align: center
           :width: 400px
        
        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 again this software under the MIT License.
        
        
        
        Features
        --------
        
        * Combines BERT and Neural Variational Topic Models
        * Two different methodologies: combined, where we combine BoW and BERT embeddings and contextual, that uses only BERT embeddings
        * Includes methods to create embedded representations and BoW
        * Includes evaluation metrics
        
        
        Overview
        --------
        
        Install the package using pip
        
        .. code-block:: bash
        
            pip install -U contextualized_topic_models
        
        
        The contextual neural topic model 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:
        
        Combined Topic Model:
        
        .. code-block:: python
        
            CTM(input_size=len(handler.vocab), bert_input_size=512, inference_type="combined", n_components=50)
        
        Fully Contextual Topic Model:
        
        .. code-block:: python
        
            CTM(input_size=len(handler.vocab), bert_input_size=512, inference_type="contextual", n_components=50)
        
        
        
        
        Contextual Topic Modeling
        -------------------------
        
        Here is how you can use the combined topic model. The high level API is pretty easy to use:
        
        .. code-block:: python
        
            from contextualized_topic_models.models.ctm import CTM
            from contextualized_topic_models.utils.data_preparation import TextHandler
            from contextualized_topic_models.utils.data_preparation import bert_embeddings_from_file
            from contextualized_topic_models.datasets.dataset import CTMDataset
        
            handler = TextHandler("documents.txt")
            handler.prepare() # create vocabulary and training data
        
            # generate BERT data
            training_bert = bert_embeddings_from_file("documents.txt", "distiluse-base-multilingual-cased")
        
            training_dataset = CTMDataset(handler.bow, training_bert, handler.idx2token)
        
            ctm = CTM(input_size=len(handler.vocab), bert_input_size=512, inference_type="combined", n_components=50)
        
            ctm.fit(training_dataset) # run the model
        
        See the example notebook in the `contextualized_topic_models/examples` folder.
        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.
        
        .. code-block:: python
        
            from contextualized_topic_models.evaluation.measures import CoherenceNPMI
        
            with open('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()
        
        
        Cross-lingual Topic Modeling
        ----------------------------
        
        The fully contextual topic model can be used for cross-lingual topic modeling! See the paper (https://arxiv.org/pdf/2004.07737v1.pdf)
        
        
        .. code-block:: python
        
            from contextualized_topic_models.models.ctm import CTM
            from contextualized_topic_models.utils.data_preparation import TextHandler
            from contextualized_topic_models.utils.data_preparation import bert_embeddings_from_file
            from contextualized_topic_models.datasets.dataset import CTMDataset
        
            handler = TextHandler("english_documents.txt")
            handler.prepare() # create vocabulary and training data
        
            training_bert = bert_embeddings_from_file("documents.txt", "distiluse-base-multilingual-cased")
        
            training_dataset = CTMDataset(handler.bow, training_bert, handler.idx2token)
        
            ctm = CTM(input_size=len(handler.vocab), bert_input_size=512, inference_type="contextual", n_components=50)
        
            ctm.fit(training_dataset) # run the model
        
        
        Predict Topics for Unseen Documents
        -----------------------------------
        Once you have trained the cross-lingual topic model, you can use this simple pipeline to predict the topics for documents in a different language.
        
        .. code-block:: python
        
        
            test_handler = TextHandler("spanish_documents.txt")
            test_handler.prepare() # create vocabulary and training data
        
            # generate BERT data
            testing_bert = bert_embeddings_from_file("spanish_documents.txt", "distiluse-base-multilingual-cased")
        
            testing_dataset = CTMDataset(test_handler.bow, testing_bert, test_handler.idx2token)
            # n_sample how many times to sample the distribution (see the doc)
            ctm.get_thetas(testing_dataset, n_samples=20)
        
        
        
        Mono vs Cross-lingual
        ---------------------
        All the examples we saw used a multilingual embedding model :code:`distiluse-base-multilingual-cased`.
        However, if you are doing topic modeling in English, you can use the English sentence-bert model. In that case,
        it's really easy to update the code to support mono-lingual english topic modeling.
        
        .. code-block:: python
        
            training_bert = bert_embeddings_from_file("documents.txt", "bert-base-nli-mean-tokens")
            ctm = CTM(input_size=len(handler.vocab), bert_input_size=768, inference_type="combined", n_components=50)
        
        In general, our package should be able to support all the models described in the `sentence transformer package <https://github.com/UKPLab/sentence-transformers>`_.
        
        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 SimplePreprocessing
        
            documents = [line.strip() for line in open("documents.txt").readlines()]
            sp = SimplePreprocessing(documents)
            preprocessed_documents, unpreprocessed_corpus, 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:
        
        Combined Topic Model
        
        ::
        
            @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},
            }
        
        
        Fully Contextual Topic Model
        
        ::
        
            @article{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},
                year={2020},
               journal={arXiv preprint arXiv:2004.07737},
            }
        
        
        
        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 :)
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        .. _`Stephen Carrow` : https://github.com/estebandito22
        .. _`rbo` : https://github.com/dlukes/rbo
        .. _Federico Bianchi: http://vinid.io
        .. _Silvia Terragni: https://silviatti.github.io/
        .. _Dirk Hovy: https://dirkhovy.com/
        
        
        =======
        History
        =======
        
        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.
        
Keywords: contextualized_topic_models
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/x-rst
