Package: top2vec
Updates:New pre-trained transformer models availableAbility to use any embedding model by passing callable toembedding_modelDocument chunking options for long documentsPhrases in topics by settingngram_vocab=TrueTop2VecTop2Vec is an algorithm fortopic modelingandsemantic search. It automatically detects topics present in text
and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model
you can:Get number of detected topics.Get topics.Get topic sizes.Get hierarchichal topics.Search topics by keywords.Search documents by topic.Search documents by keywords.Find similar words.Find similar documents.Expose model withRESTful-Top2VecSee thepaperfor more details on how it works.BenefitsAutomatically finds number of topics.No stop word lists required.No need for stemming/lemmatization.Works on short text.Creates jointly embedded topic, document, and word vectors.Has search functions built in.How does it work?The assumption the algorithm makes is that many semantically similar documents
are indicative of an underlying topic. The first step is to create a joint embedding of
document and word vectors. Once documents and words are embedded in a vector
space the goal of the algorithm is to find dense clusters of documents, then identify which
words attracted those documents together. Each dense area is a topic and the words that
attracted the documents to the dense area are the topic words.The Algorithm:1. Create jointly embedded document and word vectors usingDoc2VecorUniversal Sentence EncoderorBERT Sentence Transformer.Documents will be placed close to other similar documents and close to the most distinguishing words.2. Create lower dimensional embedding of document vectors usingUMAP.Document vectors in high dimensional space are very sparse, dimension reduction helps for finding dense areas. Each point is a document vector.3. Find dense areas of documents usingHDBSCAN.The colored areas are the dense areas of documents. Red points are outliers that do not belong to a specific cluster.4. For each dense area calculate the centroid of document vectors in original dimension, this is the topic vector.The red points are outlier documents and do not get used for calculating the topic vector. The purple points are the document vectors that belong to a dense area, from which the topic vector is calculated.5. Find n-closest word vectors to the resulting topic vector.The closest word vectors in order of proximity become the topic words.InstallationThe easy way to install Top2Vec is:pip install top2vecTo install pre-trained universal sentence encoder options:pip install top2vec[sentence_encoders]To install pre-trained BERT sentence transformer options:pip install top2vec[sentence_transformers]To install indexing options:pip install top2vec[indexing]Usagefromtop2vecimportTop2Vecmodel=Top2Vec(documents)Important parameters:documents: Input corpus, should be a list of strings.speed: This parameter will determine how fast the model takes to train.
The 'fast-learn' option is the fastest and will generate the lowest quality
vectors. The 'learn' option will learn better quality vectors but take a longer
time to train. The 'deep-learn' option will learn the best quality vectors but
will take significant time to train.workers: The amount of worker threads to be used in training the model. Larger
amount will lead to faster training.Trained models can be saved and loaded.model.save("filename")model=Top2Vec.load("filename")For more information view theAPI guide.Pretrained Embedding ModelsDoc2Vec will be used by default to generate the joint word and document embeddings. However there are also pretrainedembedding_modeloptions for generating joint word and document embeddings:universal-sentence-encoderuniversal-sentence-encoder-multilingualdistiluse-base-multilingual-casedfromtop2vecimportTop2Vecmodel=Top2Vec(documents,embedding_model='universal-sentence-encoder')For large data sets and data sets with very unique vocabulary doc2vec could
produce better results. This will train a doc2vec model from scratch. This method
is language agnostic. However multiple languages will not be aligned.Using the universal sentence encoder options will be much faster since those are
pre-trained and efficient models. The universal sentence encoder options are
suggested for smaller data sets. They are also good options for large data sets
that are in English or in languages covered by the multilingual model. It is also
suggested for data sets that are multilingual.The distiluse-base-multilingual-cased pre-trained sentence transformer is suggested
for multilingual datasets and languages that are not covered by the multilingual
universal sentence encoder. The transformer is significantly slower than
the universal sentence encoder options.More information onuniversal-sentence-encoder,universal-sentence-encoder-multilingual, anddistiluse-base-multilingual-cased.CitationIf you would like to cite Top2Vec in your work this is the current reference:@article{angelov2020top2vec,title={Top2Vec: Distributed Representations of Topics},author={Dimo Angelov},year={2020},eprint={2008.09470},archivePrefix={arXiv},primaryClass={cs.CL}}ExampleTrain ModelTrain a Top2Vec model on the 20newsgroups dataset.fromtop2vecimportTop2Vecfromsklearn.datasetsimportfetch_20newsgroupsnewsgroups=fetch_20newsgroups(subset='all',remove=('headers','footers','quotes'))model=Top2Vec(documents=newsgroups.data,speed="learn",workers=8)Get Number of TopicsThis will return the number of topics that Top2Vec has found in the data.>>>model.get_num_topics()77Get Topic SizesThis will return the number of documents most similar to each topic. Topics are
in decreasing order of size.topic_sizes,topic_nums=model.get_topic_sizes()Returns:topic_sizes: The number of documents most similar to each topic.topic_nums: The unique index of every topic will be returned.Get TopicsThis will return the topics in decreasing size.topic_words,word_scores,topic_nums=model.get_topics(77)Returns:topic_words: For each topic the top 50 words are returned, in order
of semantic similarity to topic.word_scores: For each topic the cosine similarity scores of the
top 50 words to the topic are returned.topic_nums: The unique index of every topic will be returned.Search TopicsWe are going to search for topics most similar tomedicine.topic_words,word_scores,topic_scores,topic_nums=model.search_topics(keywords=["medicine"],num_topics=5)Returns:topic_words: For each topic the top 50 words are returned, in order
of semantic similarity to topic.word_scores: For each topic the cosine similarity scores of the
top 50 words to the topic are returned.topic_scores: For each topic the cosine similarity to the search keywords will be returned.topic_nums: The unique index of every topic will be returned.>>>topic_nums[21,29,9,61,48]>>>topic_scores[0.4468,0.381,0.2779,0.2566,0.2515]Topic 21 was the most similar topic to "medicine" with a cosine similarity of 0.4468. (Values can be from least similar 0, to most similar 1)Generate Word CloudsUsing a topic number you can generate a word cloud. We are going to generate word clouds for the top 5 most similar topics to ourmedicinetopic search from above.topic_words,word_scores,topic_scores,topic_nums=model.search_topics(keywords=["medicine"],num_topics=5)fortopicintopic_nums:model.generate_topic_wordcloud(topic)Search Documents by TopicWe are going to search bytopic 48, a topic that appears to be aboutscience.documents,document_scores,document_ids=model.search_documents_by_topic(topic_num=48,num_docs=5)Returns:documents: The documents in a list, the most similar are first.doc_scores: Semantic similarity of document to topic. The cosine similarity of the
document and topic vector.doc_ids: Unique ids of documents. If ids were not given, the index of document
in the original corpus.For each of the returned documents we are going to print its content, score and document number.documents,document_scores,document_ids=model.search_documents_by_topic(topic_num=48,num_docs=5)fordoc,score,doc_idinzip(documents,document_scores,document_ids):print(f"Document:{doc_id}, Score:{score}")print("-----------")print(doc)print("-----------")print()Document: 15227, Score: 0.6322
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  Evolution is both fact and theory.  The THEORY of evolution represents the
scientific attempt to explain the FACT of evolution.  The theory of evolution
does not provide facts; it explains facts.  It can be safely assumed that ALL
scientific theories neither provide nor become facts but rather EXPLAIN facts.
I recommend that you do some appropriate reading in general science.  A good
starting point with regard to evolution for the layman would be "Evolution as
Fact and Theory" in "Hen's Teeth and Horse's Toes" [pp 253-262] by Stephen Jay
Gould.  There is a great deal of other useful information in this publication.
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Document: 14515, Score: 0.6186
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Just what are these "scientific facts"?  I have never heard of such a thing.
Science never proves or disproves any theory - history does.

-Tim
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Document: 9433, Score: 0.5997
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The same way that any theory is proven false.  You examine the predicitions
that the theory makes, and try to observe them.  If you don't, or if you
observe things that the theory predicts wouldn't happen, then you have some 
evidence against the theory.  If the theory can't be modified to 
incorporate the new observations, then you say that it is false.

For example, people used to believe that the earth had been created
10,000 years ago.  But, as evidence showed that predictions from this 
theory were not true, it was abandoned.
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Document: 11917, Score: 0.5845
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The point about its being real or not is that one does not waste time with
what reality might be when one wants predictions. The questions if the
atoms are there or if something else is there making measurements indicate
atoms is not necessary in such a system.

And one does not have to write a new theory of existence everytime new
models are used in Physics.
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...Semantic Search Documents by KeywordsSearch documents for content semantically similar tocryptographyandprivacy.documents,document_scores,document_ids=model.search_documents_by_keywords(keywords=["cryptography","privacy"],num_docs=5)fordoc,score,doc_idinzip(documents,document_scores,document_ids):print(f"Document:{doc_id}, Score:{score}")print("-----------")print(doc)print("-----------")print()Document: 16837, Score: 0.6112
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...
Email and account privacy, anonymity, file encryption,  academic 
computer policies, relevant legislation and references, EFF, and 
other privacy and rights issues associated with use of the Internet
and global networks in general.
...

Document: 16254, Score: 0.5722
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...
The President today announced a new initiative that will bring
the Federal Government together with industry in a voluntary
program to improve the security and privacy of telephone
communications while meeting the legitimate needs of law
enforcement.
...
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...Similar KeywordsSearch for similar words tospace.words,word_scores=model.similar_words(keywords=["space"],keywords_neg=[],num_words=20)forword,scoreinzip(words,word_scores):print(f"{word}{score}")space 1.0
nasa 0.6589
shuttle 0.5976
exploration 0.5448
planetary 0.5391
missions 0.5069
launch 0.4941
telescope 0.4821
astro 0.4696
jsc 0.4549
ames 0.4515
satellite 0.446
station 0.4445
orbital 0.4438
solar 0.4386
astronomy 0.4378
observatory 0.4355
facility 0.4325
propulsion 0.4251
aerospace 0.4226