R: Extra 'Recipes' for Text Processing

# Extra 'Recipes' for Text Processing

## Documentation for package ‘textrecipes’ version 1.1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

all_tokenized | Role Selection
all_tokenized_predictors | Role Selection
count_functions | List of all feature counting functions
emoji_samples | Sample sentences with emojis
show_tokens | Show token output of recipe
step_clean_levels | Clean Categorical Levels
step_clean_names | Clean Variable Names
step_dummy_hash | Indicator Variables via Feature Hashing
step_lda | Calculate LDA Dimension Estimates of Tokens
step_lemma | Lemmatization of Token Variables
step_ngram | Generate n-grams From Token Variables
step_pos_filter | Part of Speech Filtering of Token Variables
step_sequence_onehot | Positional One-Hot encoding of Tokens
step_stem | Stemming of Token Variables
step_stopwords | Filtering of Stop Words for Tokens Variables
step_textfeature | Calculate Set of Text Features
step_texthash | Feature Hashing of Tokens
step_text_normalization | Normalization of Character Variables
step_tf | Term frequency of Tokens
step_tfidf | Term Frequency-Inverse Document Frequency of Tokens
step_tokenfilter | Filter Tokens Based on Term Frequency
step_tokenize | Tokenization of Character Variables
step_tokenize_bpe | BPE Tokenization of Character Variables
step_tokenize_sentencepiece | Sentencepiece Tokenization of Character Variables
step_tokenize_wordpiece | Wordpiece Tokenization of Character Variables
step_tokenmerge | Combine Multiple Token Variables Into One
step_untokenize | Untokenization of Token Variables
step_word_embeddings | Pretrained Word Embeddings of Tokens
tidy.step_clean_levels | Clean Categorical Levels
tidy.step_clean_names | Clean Variable Names
tidy.step_dummy_hash | Indicator Variables via Feature Hashing
tidy.step_lda | Calculate LDA Dimension Estimates of Tokens
tidy.step_lemma | Lemmatization of Token Variables
tidy.step_ngram | Generate n-grams From Token Variables
tidy.step_pos_filter | Part of Speech Filtering of Token Variables
tidy.step_sequence_onehot | Positional One-Hot encoding of Tokens
tidy.step_stem | Stemming of Token Variables
tidy.step_stopwords | Filtering of Stop Words for Tokens Variables
tidy.step_textfeature | Calculate Set of Text Features
tidy.step_texthash | Feature Hashing of Tokens
tidy.step_text_normalization | Normalization of Character Variables
tidy.step_tf | Term frequency of Tokens
tidy.step_tfidf | Term Frequency-Inverse Document Frequency of Tokens
tidy.step_tokenfilter | Filter Tokens Based on Term Frequency
tidy.step_tokenize | Tokenization of Character Variables
tidy.step_tokenize_bpe | BPE Tokenization of Character Variables
tidy.step_tokenize_sentencepiece | Sentencepiece Tokenization of Character Variables
tidy.step_tokenize_wordpiece | Wordpiece Tokenization of Character Variables
tidy.step_tokenmerge | Combine Multiple Token Variables Into One
tidy.step_untokenize | Untokenization of Token Variables
tidy.step_word_embeddings | Pretrained Word Embeddings of Tokens
tokenlist | Create Token Object