transforEmotion (version 0.1.5)

sentence_similarity: Sentiment Analysis Scores

Description

Uses sentiment analysis pipelines from huggingface to compute probabilities that the text corresponds to the specified classes

Usage

sentence_similarity(
  text,
  comparison_text,
  transformer = c("all_minilm_l6"),
  device = c("auto", "cpu", "cuda"),
  preprocess = FALSE,
  keep_in_env = TRUE,
  envir = 1
)

Value

Returns a n x m similarity matrix where n is length of text and m is the length of comparison_text

Arguments

text

Character vector or list. Text in a vector or list data format

comparison_text

Character vector or list. Text in a vector or list data format

transformer

Character. Specific sentence similarity transformer to be used. Defaults to "all_minilm_l6" (see huggingface)

Also allows any sentence similarity models with a pipeline from huggingface to be used by using the specified name (e.g., "typeform/distilbert-base-uncased-mnli"; see Examples)

device

Character. Whether to use CPU or GPU for inference. Defaults to "auto" which will use GPU over CPU (if CUDA-capable GPU is setup). Set to "cpu" to perform over CPU

preprocess

Boolean. Should basic preprocessing be applied? Includes making lowercase, keeping only alphanumeric characters, removing escape characters, removing repeated characters, and removing white space. Defaults to FALSE. Transformers generally are OK without preprocessing and handle many of these functions internally, so setting to TRUE will not change performance much

keep_in_env

Boolean. Whether the classifier should be kept in your global environment. Defaults to TRUE. By keeping the classifier in your environment, you can skip re-loading the classifier every time you run this function. TRUE is recommended

envir

Numeric. Environment for the classifier to be saved for repeated use. Defaults to the global environment

Author

Alexander P. Christensen <[e-mail]>

Examples

# Load data
data(neo_ipip_extraversion)

# Example text
text <- neo_ipip_extraversion$friendliness[1:5]

if (FALSE) {
# Example with defaults
sentence_similarity(
 text = text, comparison_text = text
)

# Example with model from 'sentence-transformers'
sentence_similarity(
 text = text, comparison_text = text,
 transformer = "sentence-transformers/all-mpnet-base-v2"
)

}