In basilica-ai/basilica-r-client: Deep Feature Extraction for Images and Natural Language Text

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Basilica provides 2 functions for working with images:

• embed_sentence
• embed_sentences

The embed_sentence function takes a single characther vector (a string) and returns a vector of features. The embed_sentences functions takes a list of character vectors returns a matrix with a feature vector on every row.

embed_sentece

sentences <- c(
  "This is a sentence!",
  "This is a similar sentence!",
  "I don't think this sentence is very similar at all..."
)

library('basilica')
conn <- connect("SLOW_DEMO_KEY")

embeddings <- list()
embeddings[[1]] <- embed_sentence(sentence[[1]], conn=conn)
embeddings[[2]] <- embed_sentence(sentence[[2]], conn=conn)
embeddings[[3]] <- embed_sentence(sentence[[3]], conn=conn)

print(cor(embeddings[[1]], embeddings[[2]]))
print(cor(embeddings[[1]], embeddings[[3]]))

embed_senteces

sentences <- c(
  "This is a sentence!",
  "This is a similar sentence!",
  "I don't think this sentence is very similar at all..."
)

library('basilica')
conn <- connect("SLOW_DEMO_KEY")

embeddings <- embed_sentences(sentences, conn=conn)

print(cor(embeddings[1,], embeddings[2,]))
print(cor(embeddings[1,], embeddings[3,]))

basilica-ai/basilica-r-client documentation built on June 6, 2019, 3:40 p.m.