Load a Sentence Transformers model to extract sentence/document embeddings

Load a Sentence Transformers model to extract sentence/document embeddings

Usage

hf_load_sentence_model(model_id, ...)

Arguments

model_id

The id of a sentence-transformers model. Use hf_search_models(author = 'sentence-transformers') to find suitable models.

...

Sent to model call, could include arguments such as use_auth_token = auth_token, device = device etc.

Value

A Huggingface model ready for prediction.

See also

https://huggingface.co/sentence-transformers

Examples

if (FALSE) { # \dontrun{
# Compute sentence embeddings
sentences <- c("Baby turtles are so cute!", "He walks as slowly as a turtle.")
sentences_two <- c("The lake is cold today.", "I enjoy swimming in the lake.")
sentences <- c(sentences, sentences_two)
model <- hf_load_sentence_model('paraphrase-MiniLM-L6-v2')
embeddings <- model$encode(sentences)
embeddings %>% dist() %>% as.matrix() %>% as.data.frame() %>% setNames(sentences)
embddings <- embeddings %>% dplyr::mutate(`sentence 1` = sentences) %>%
tidyr::pivot_longer(cols = -`sentence 1`, names_to = 'sentence 2', values_to = 'distance')
embeddings <- embeddings %>% filter(distance > 0)
# Cluster sentences
embeddings <- embeddings %>%
t() %>% prcomp() %>%
purrr::pluck('rotation') %>%
as.data.frame() %>%
dplyr::mutate(sentence = sentences)
plot <- embedidings %>% ggplot2::ggplot(aes(PC1, PC2)) +
ggplot2::geom_label(ggplot2::aes(PC1, PC2, label = sentence, vjust="inward", hjust="inward")) +
ggplot2::theme_minimal()
} # }