gensim library

This function uses the corpora and models modules from the gensim library to create a random projections model for a set of documents. It first converts the documents into a dictionary representation using the Dictionary object and then into a Bag-of-Words (BoW) representation using the doc2bow method. After that, it trains a RandomProjectionsModel instance, which is used for dimensionality reduction and noise reduction.

The type of code 2024-12-16 12:14:17 24 views

gensim Dictionary

This function uses the LatentSemanticAnalysis model from the gensim library to generate a topic space model based on the given document list and returns the model.

Function 2024-12-16 12:02:22 6 views

gensim (LdaModel

This function uses gensim's LdaModel to analyze text, extract keyphrases that represent each topic, and return the most relevant num_phrases keyphrases.

Text analysis 2024-12-16 12:00:43 5 views

gensim corpora

This function uses the LdaModel from the gensim library to perform topic modeling on a list of documents. It first preprocesses the documents, then creates a dictionary and corpus, and finally trains an LDA model to output the most representative words for each topic.

Text analysis 2024-12-16 11:49:57 3 views

gensim corpora

This function uses the corpora and models modules from the gensim library to train a LDA (Latent Dirichlet Allocation) model. It accepts a list of documents, the number of topics, and the number of passes as inputs, and returns the trained model.

Gensim LDA Model Training 2024-12-16 11:46:00 4 views

gensim

This function uses the corpora and models modules from the gensim library to perform topic modeling on a list of documents. It first preprocesses the documents, then creates a dictionary and corpus, trains an LDA model, and finally outputs the topics of the model.

Machine learning 2024-12-16 11:33:59 3 views

gensim corpora

This function applies a random topic model to a list of documents using gensim's LdaModel. It first converts the documents into a dictionary and a corpus, then trains an LdaModel with random initialization. Finally, it returns the trained model.

Machine learning 2024-12-16 11:28:52 5 views

gensim corpora

This function uses the corpora and models modules from the gensim library to generate a random text corpus. It first generates random documents, then preprocesses each document, and finally creates a dictionary and a bag-of-words model.

Generate random text corpus 2024-12-16 11:28:31 4 views

gensim

This function uses the corpora and models modules from the gensim library to perform topic modeling on input text data. It first creates a dictionary representation of the texts, then converts it to a bag-of-words corpus, and finally trains an LDA (Latent Dirichlet Allocation) model.

Function 2024-12-16 11:23:26 4 views

gensim corpora

This function accepts a text and a dictionary, and then uses the LDA model from the gensim library to calculate the frequency distribution of words in the text. It first cleans the text, then converts it to a dictionary representation, then creates a Bag-of-Words (BoW) model, and finally uses the LDA model to analyze the text.

Text analysis 2024-12-16 11:20:50 3 views