textfeatures has been removed from Suggests. (#255)
step_textfeatures() no longer returns a politeness feature. (#254)
step_untokenize() and step_normalization() now returns factors instead of strings. (#247) # textrecipes 1.0.4step_clean_names() now throw an informative error if needed non-standard role columns are missing during bake(). (#235)
The keep_original_cols argument has been added to step_tokenmerge. This change should mean that every step that produces new columns has the keep_original_cols argument. (#242)
Many internal changes to improve consistency and slight speed increases.
Fixed bug where step_dummy_hash() and step_texthash() would add new columns before old columns. (#235)
Fixed bug where vocabulary_size wasn’t tunable in step_tokenize_bpe(). (#239)
Steps with tunable arguments now have those arguments listed in the documentation.
All steps that add new columns will now informatively error if name collision occurs.
step_tf() wasn’t tunable for weight argument.Setting token = "tweets" in step_tokenize() have been deprecated due to tokenizers::tokenize_tweets() being deprecated. (#209)
step_sequence_onehot(), step_dummy_hash(), step_dummy_texthash() now return integers. step_tf() returns integer when weight_scheme is "binary" or "raw count".
All steps now have required_pkgs() methods.
if (require(...)) code.Remove use of okc_text in vignette
Fix bug in printing of tokenlists
step_tfidf() now correctly saves the idf values and applies them to the testing data set.
tidy.step_tfidf() now returns calculated IDF weights.
step_dummy_hash() generates binary indicators (possibly signed) from simple factor or character vectors.
step_tokenize() has gotten a couple of cousin functions step_tokenize_bpe(), step_tokenize_sentencepiece() and step_tokenize_wordpiece() which wraps {tokenizers.bpe}, {sentencepiece} and {wordpiece} respectively (#147).
Added all_tokenized() and all_tokenized_predictors() to more easily select tokenized columns (#132).
Use show_tokens() to more easily debug a recipe involving tokenization.
Reorganize documentation for all recipe step tidy methods (#126).
Steps now have a dedicated subsection detailing what happens when tidy() is applied. (#163)
All recipe steps now officially support empty selections to be more aligned with dplyr and other packages that use tidyselect (#141).
step_ngram() has been given a speed increase to put it in line with other packages performance.
step_tokenize() will now try to error if vocabulary size is too low when using engine = "tokenizers.bpe" (#119).
Warning given by step_tokenfilter() when filtering failed to apply now correctly refers to the right argument name (#137).
step_tf() now returns 0 instead of NaN when there aren’t any tokens present (#118).
step_tokenfilter() now has a new argument filter_fun will takes a function which can be used to filter tokens. (#164)
tidy.step_stem() now correctly shows if custom stemmer was used.
Added keep_original_cols argument to step_lda, step_texthash(), step_tf(), step_tfidf(), step_word_embeddings(), step_dummy_hash(), step_sequence_onehot(), and step_textfeatures() (#139).
prefix argument now creates names according to the pattern prefix_variablename_name/number. (#124)step_tokenfilter() and step_sequence_onehot() that sometimes caused crashes in R 4.1.0.step_lda() now takes a tokenlist instead of a character variable. See readme for more detail.step_sequence_onehot() now takes tokenlists as input.step_tokenize().step_tokenize().step_clean_names() and step_clean_levels(). (#101)step_ngram() gained an argument min_num_tokens to be able to return multiple n-grams together. (#90)step_text_normalization() to perform unicode normalization on character vectors. (#86)step_word_embeddings() got a argument aggregation_default to specify value in cases where no words matches embedding.step_tokenize() got an engine argument to specify packages other then tokenizers to tokenize.spacyr have been added as an engine to step_tokenize().step_lemma() has been added to extract lemma attribute from tokenlists.step_pos_filter() has been added to allow filtering of tokens bases on their pat of speech tags.step_ngram() has been added to generate ngrams from tokenlists.step_stem() not correctly uses the options argument. (Thanks to @grayskripko for finding bug, #64)step_word2vec() have been changed to step_lda() to reflect what is actually happening.step_word_embeddings() has been added. Allows for use of pre-trained word embeddings to convert token columns to vectors in a high-dimensional “meaning” space. (@jonthegeek, #20)step_tfidf() calculations are slightly changed due to flaw in original implementation https://github.com/dselivanov/text2vec/issues/280.step_textfeatures() have been added, allows for multiple numerical features to be pulled from text.step_sequence_onehot() have been added, allows for one hot encoding of sequences of fixed width.step_word2vec() have been added, calculates word2vec dimensions.step_tokenmerge() have been added, combines multiple list columns into one list-columns.step_texthash() now correctly accepts signed argument.step_tf() and step_tfidf().First CRAN version