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text2vec 0.6.6 (2025-11-29)

  • fix R CMD check notes in documentation of R6 classes

text2vec 0.6.5 (2023-10-16)

  • fix test discovered with Matrix==1.6-2 release

text2vec 0.6.4 (2023-02-15)

  • update dependency Matrix>=1.5-2, fixes #338

text2vec 0.6.2 (2022-09-11)

  • removed test which is not needed with Matrix package v 1.5

text2vec 0.6

  1. 2019-12-17
    • breaking change - removed construction of a vocabulary in parallel on windows
    • use rsparse package for SVD and GloVe factorizations
    • updated RWMD implementation (hopefully bug free)
  2. 2018-09-10
    • breaking change - changed IDF formula - see #280 for details.
  3. 2018-05-28
    • Added postag_lemma_tokenizer() (wrapper around udpipe::udpipe_annotate). Can be used as a drop-in replacement for more simple tokenizers in text2vec.
  4. 2018-05-25
    • Made combine_vocabularies() part of public API - see #260 for details.
  5. 2018-05-10
    • Added coherence() function for comprehensive coherence metrics. Thanks to Manuel Bickel ( @manuelbickel ) for conrtibution.
  6. 2018-05-02
    • Fixed bug LSA model - document embeddings calculated as left singular vectors multiplied by singular values (not square root of values as before). Thanks to Sloane Simmons ( @singularperturbation )
    • Now fit_transform and transform methods in LDA model produce same results. Thanks to @jiunsiew for reporting. Also now LDA has n_iter_inference parameter. It controls number of the samples from converged distribution for document-topic inference. This leads to more robust document-topic probabilities (reduced variance). Default value is 10.
  7. 2018-01-17
    • more numerically robust PMI, LFMD - thanks to @andland. Also adds iteration number iter to collocation_stat. iter shows iteration number when collocation stats (and counters) were calculated.

text2vec 0.5.1 [2018-01-10]

  1. 2018-01-10
    • removed rank* columns from collocation_stat - were never used internally. Users can easily calculate ranks themselves
  2. 2018-01-09
    • Added Bi-Normal Separation transformation, thanks to Pavel Shashkin ( @pshashk )
    • Added Dunning's log-likelihood ratio for collocations, thanks to Chris Lee ( @Chrisss93 )
    • Early stopping for collocations learning
  3. 2017-12-18
    • fixed several bugs #219 #217 #205
    • decreased number of dependencies - no more magrittr, uuid, tokenizers
    • removed distributed LDA which didn't work correctly
  4. 2017-10-18
    • Now tokenization is based on tokenizers and THE stringi packages.
    • models API follow mlapi package. No API changes on text2vec side - we just put abstract scikit-learn-like classes to a separate package in order to make them more reusable.

text2vec 0.5.0

  1. 2017-06-12
    • Add additional filters to prune_vocabulary - filter by document counts
    • Clean up LSA, fixed transform method. Added option to use randomized SVD algorithm from irlba.
  2. 2017-05-17
  3. 2017-05-17
    • API breaking change - vocabulary format change - now plain data.frame with meta-information in attributes (stopwords, ngram, number of docs, etc).
  4. 2017-03-25
    • No more rely on RcppModules
    • API breaking change - removed lda_c from formats in DTM construction
    • added ifiles_parallel, itoken_parallel high-level functions for parallel computing
    • API breaking change chunks_numer parameter renamed to n_chunks
  5. 2017-01-02
    • API breaking change - removed create_corpus from public API, moved co-occurence related optons to create_tcm from vecorizers
    • add ability to add custom weights for co-occurence statistics calculations
  6. 2016-12-30
    • Noticeable speedup (1.5x) and even more noticeable improvement on memory usage (2x less!) for create_dtm, create_tcm . Now package relies on sparsepp library for underlying hash maps.
  7. 2016-10-30
    • Collocations - detection of multi-word phrases using differend heuristics - PMI, gensim, LFMD.
  8. 2016-10-20
    • Fixed bug in as.lda_c() function

text2vec 0.4.0

2016-10-03. See 0.4 milestone tags.

  1. Now under GPL (>= 2) Licence
  2. "immutable" iterators - no need to reinitialize them
  3. unified models interface
  4. New models: LSA, LDA, GloVe with L1 regularization
  5. Fast similarity and distances calculation: Cosine, Jaccard, Relaxed Word Mover's Distance, Euclidean
  6. Better hadnling UTF-8 strings, thanks to @qinwf
  7. iterators and models rely on R6 package

text2vec 0.3.0

  1. 2016-01-13 fix for #46, thanks to @buhrmann for reporting
  2. 2016-01-16 format of vocabulary changed.
    • do not keep doc_proportions. see #52.
    • add stop_words argument to prune_vocabulary. signature also was changed.
  3. 2016-01-17 fix for #51. if iterator over tokens returns list with names, these names will be:
    • stored as attr(corpus, 'ids')
    • rownames in dtm
    • names for dtm list in lda_c format
  4. 2016-02-02 high level function for corpus and vocabulary construction.
    • construction of vocabulary from list of itoken.
    • construction of dtm from list of itoken.
  5. 2016-02-10 rename transformers
    • now all transformers starts with transform_* - more intuitive + simpler usage with autocompletion
  6. 2016-03-29 (accumulated since 2016-02-10)
    • rename vocabulary to create_vocabulary.
    • new functions create_dtm, create_tcm.
    • All core functions are able to benefit from multicore machines (user have to register parallel backend themselves)
    • Fix for progress bars. Now they are able to reach 100% and ticks increased after computation.
    • ids argument to itoken. Simplifies assignement of ids to rows of DTM
    • create_vocabulary now can handle stopwords
    • see all updates here
  7. 2016-03-30 more robust split_into() util.

text2vec 0.2.0 (2016-01-10)

First CRAN release of text2vec.

  • Fast text vectorization with stable streaming API on arbitrary n-grams.
    • Functions for vocabulary extraction and management
    • Hash vectorizer (based on digest murmurhash3)
    • Vocabulary vectorizer
  • GloVe algorithm word embeddings.
    • Fast term-co-occurence matrix factorization via parallel async AdaGrad.
  • All core functions written in C++.