可以通过点击 官方链接 查看详细信息
官方提供的API列表如下：
interfaces– Core gensim interfaces
utils– Various utility functions
matutils– Math utils
corpora.bleicorpus– Corpus in Blei’s LDA-C format
corpora.csvcorpus– Corpus in CSV format
corpora.dictionary– Construct word<->id mappings
corpora.hashdictionary– Construct word<->id mappings
corpora.indexedcorpus– Random access to corpus documents
corpora.lowcorpus– Corpus in List-of-Words format
corpora.malletcorpus– Corpus in Mallet format of List-Of-Words.
corpora.mmcorpus– Corpus in Matrix Market format
corpora.sharded_corpus– Corpus stored in separate files
corpora.svmlightcorpus– Corpus in SVMlight format
corpora.textcorpus– Building corpora with dictionaries
corpora.ucicorpus– Corpus in UCI bag-of-words format
corpora.wikicorpus– Corpus from a Wikipedia dump
models.ldamodel– Latent Dirichlet Allocation
models.ldamulticore– parallelized Latent Dirichlet Allocation
models.lsimodel– Latent Semantic Indexing
models.ldaseqmodel– Dynamic Topic Modeling in Python
models.tfidfmodel– TF-IDF model
models.rpmodel– Random Projections
models.hdpmodel– Hierarchical Dirichlet Process
models.logentropy_model– LogEntropy model
models.normmodel– Normalization model
models.translation_matrix– Translation Matrix model
models.lsi_dispatcher– Dispatcher for distributed LSI
models.lsi_worker– Worker for distributed LSI
models.lda_dispatcher– Dispatcher for distributed LDA
models.lda_worker– Worker for distributed LDA
models.atmodel– Author-topic models
models.word2vec– Deep learning with word2vec
models.keyedvectors– Store and query word vectors
models.doc2vec– Deep learning with paragraph2vec
models.fasttext– FastText model
models.phrases– Phrase (collocation) detection
models.coherencemodel– Topic coherence pipeline
models.basemodel– Core TM interface
models.callbacks– Callbacks for track and viz LDA train process
models.wrappers.ldamallet– Latent Dirichlet Allocation via Mallet
models.wrappers.dtmmodel– Dynamic Topic Models (DTM) and Dynamic Influence Models (DIM)
models.wrappers.ldavowpalwabbit– Latent Dirichlet Allocation via Vowpal Wabbit
models.wrappers.wordrank– Word Embeddings from WordRank
models.wrappers.varembed– VarEmbed Word Embeddings
models.wrappers.fasttext– FastText Word Embeddings
similarities.docsim– Document similarity queries
How It Works
similarities.index– Fast Approximate Nearest Neighbor Similarity with Annoy package
sklearn_api.atmodel– Scikit learn wrapper for Author-topic model
sklearn_api.d2vmodel– Scikit learn wrapper for paragraph2vec model
sklearn_api.hdp– Scikit learn wrapper for Hierarchical Dirichlet Process model
sklearn_api.ldamodel– Scikit learn wrapper for Latent Dirichlet Allocation
sklearn_api.ldaseqmodel– Scikit learn wrapper for LdaSeq model
sklearn_api.lsimodel– Scikit learn wrapper for Latent Semantic Indexing
sklearn_api.phrases– Scikit learn wrapper for phrase (collocation) detection
sklearn_api.rpmodel– Scikit learn wrapper for Random Projection model
sklearn_api.text2bow– Scikit learn wrapper word<->id mapping
sklearn_api.tfidf– Scikit learn wrapper for TF-IDF model
sklearn_api.w2vmodel– Scikit learn wrapper for word2vec model
topic_coherence.aggregation– Aggregation module
topic_coherence.direct_confirmation_measure– Direct confirmation measure module
topic_coherence.indirect_confirmation_measure– Indirect confirmation measure module
topic_coherence.probability_estimation– Probability estimation module
topic_coherence.segmentation– Segmentation module
topic_coherence.text_analysis– Analyzing the texts of a corpus to accumulate statistical information about word occurrences
scripts.glove2word2vec– Convert glove format to word2vec
scripts.make_wikicorpus– Convert articles from a Wikipedia dump to vectors.
scripts.word2vec_standalone– Train word2vec on text file CORPUS
scripts.make_wiki_online– Convert articles from a Wikipedia dump
scripts.make_wiki_online_lemma– Convert articles from a Wikipedia dump
scripts.make_wiki_online_nodebug– Convert articles from a Wikipedia dump
scripts.word2vec2tensor– Convert the word2vec format to Tensorflow 2D tensor
scripts.segment_wiki– Convert wikipedia dump to json-line format
parsing.porter– Porter Stemming Algorithm
parsing.preprocessing– Functions to preprocess raw text
summarization.bm25– BM25 ranking function
summarization.commons– Common graph functions
summarization.graph– TextRank graph
summarization.keywords– Keywords for TextRank summarization algorithm
summarization.pagerank_weighted– Weighted PageRank algorithm
summarization.summarizer– TextRank Summariser
summarization.syntactic_unit– Syntactic Unit class
summarization.textcleaner– Summarization pre-processing