Ranks each document's own words and phrases by cosine similarity between their embeddings and the document embedding, computed with the same model, and selects the top `n` by maximal marginal relevance so the keywords are relevant without being redundant (the KeyBERT design).
Usage
keywords(
text,
model = NULL,
n = 10L,
ngrams = 2L,
topic_diversity = 0.3,
stop_words = default_stop_words(),
min_token_length = 3L,
batch_size = 32L
)Arguments
- text
A character vector of documents. Names, when present, are carried into the `document_name` column.
- model
A loaded sbert model, a pinned model name, or `NULL` for the session default.
- n
Maximum keywords returned per document. Default `10`.
- ngrams
Maximum phrase length in tokens. Default `2` (unigrams and bigrams).
- topic_diversity
Maximal-marginal-relevance trade-off in `[0, 1)`: `0` ranks purely by similarity, larger values penalize keywords similar to ones already selected. Default `0.3`.
- stop_words
Words excluded from candidates. Defaults to [stop_words()].
- min_token_length
Minimum character length of a candidate token. Default `3`.
- batch_size
Number of texts encoded per model call. Default `32`.
Value
A base data frame with one row per keyword and columns `document_id`, `document_name`, `rank`, `keyword`, and `topic_similarity` (cosine similarity between the keyword and its document).
Details
Candidates are consecutive-token n-grams (lengths `1` to `ngrams`) drawn from the document after stop-word removal, so a candidate phrase never crosses a removed stop word silently: "analysis of networks" yields the candidates "analysis", "networks", and "analysis networks".
Examples
# Pinned models that can encode the text.
models()
#> model dimensions max_tokens languages
#> 1 all-MiniLM-L6-v2 384 256 English
#> 2 all-MiniLM-L12-v2 384 128 English
#> 3 paraphrase-MiniLM-L3-v2 384 128 English
#> 4 multi-qa-MiniLM-L6-cos-v1 384 512 English
#> 5 paraphrase-multilingual-MiniLM-L12-v2 384 128 50+ languages
#> 6 all-mpnet-base-v2 768 384 English
#> 7 paraphrase-multilingual-mpnet-base-v2 768 128 50+ languages
#> 8 bge-small-en-v1.5 384 512 English
#> 9 bge-base-en-v1.5 768 512 English
#> 10 multilingual-e5-small 384 512 100+ languages
#> 11 nomic-embed-text-v1.5 768 8192 English
#> 12 jina-embeddings-v2-small-en 512 8192 English
#> 13 mxbai-embed-large-v1 1024 512 English
#> 14 potion-base-8M 256 1000000 English
#> size_mb
#> 1 90.9
#> 2 133.6
#> 3 69.5
#> 4 90.9
#> 5 479.4
#> 6 436.3
#> 7 1119.2
#> 8 133.8
#> 9 436.5
#> 10 487.4
#> 11 548.0
#> 12 130.5
#> 13 1337.6
#> 14 30.9
# \donttest{
if (interactive()) {
keywords(
"Transition network analysis models learning event sequences.",
n = 5
)
}
# }