-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
- Loading branch information
There are no files selected for viewing
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -6,9 +6,9 @@ Authors@R: c(person("Alexander", "Christensen", email = "alexpaulchristensen@gma | |
role = "aut", comment = c(ORCID = "0000-0002-9798-7037")), | ||
person("Hudson", "Golino", email = "[email protected]", role = "aut", | ||
comment = c(ORCID = "0000-0002-1601-1447")), | ||
person("Aleksandar", "Tomašević", email = "[email protected]", role = c("aut", "cre"), | ||
person("Aleksandar", "Tomasevic", email = "[email protected]", role = c("aut", "cre"), | ||
comment = c(ORCID = "0000-0003-4863-6051"))) | ||
Maintainer: Aleksandar Tomašević <[email protected]> | ||
Maintainer: Aleksandar Tomasevic <[email protected]> | ||
Description: Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>. | ||
License: GPL (>= 3.0) | ||
Encoding: UTF-8 | ||
|
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -10,7 +10,7 @@ | |
#' @param face_selection The method to select the face in the image. Can be "largest" or "left" or "right". Default is "largest" and will select the largest face in the image. "left" and "right" will select the face on the far left or the far right side of the image. Face_selection method is irrelevant if there is only one face in the image. | ||
#' @return A data frame containing the scores for each class. | ||
#' | ||
#' @author Aleksandar Tomašević <[email protected]> | ||
#' @author Aleksandar Tomasevic <[email protected]> | ||
#' @importFrom reticulate source_python | ||
#' @importFrom reticulate py | ||
#' @export | ||
|
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -21,7 +21,7 @@ conda_check <- function(){ | |
#' @details Installs miniconda using \code{\link[reticulate]{install_miniconda}} and activates the transforEmotion environment using \code{\link[reticulate]{use_condaenv}}. If the transforEmotion environment does not exist, it will be created using \code{\link[reticulate]{conda_create}}. | ||
#' | ||
#' @author Alexander P. Christensen <[email protected]> | ||
#' Aleksandar Tomašević <[email protected]> | ||
#' Aleksandar Tomasevic <[email protected]> | ||
#' | ||
#' @export | ||
#' | ||
|
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -17,7 +17,7 @@ | |
#' | ||
#' @return A result object containing the analyzed video scores. | ||
#' | ||
#' @author Aleksandar Tomašević <[email protected]> | ||
#' @author Aleksandar Tomasevic <[email protected]> | ||
#' @import reticulate | ||
#' | ||
#' @export | ||
|
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,17 +1,18 @@ | ||
citHeader("To cite transforEmotion in publications use:") | ||
|
||
bibentry( | ||
bibtype = "manual", | ||
key = "transforEmotion", | ||
title = "transforEmotion: Sentiment analysis for text, image and video using transformer models", | ||
author = c(person("Christensen,", "Alexander P"), | ||
person("Golino,", "Hudson"), | ||
person("Tomasević","Aleksandar")), | ||
year = "2024", | ||
note = "R package version 0.1.4", | ||
|
||
textVersion = | ||
paste0("Christensen, A. P., Golino, H., Tomašević, A. (2024). ", | ||
"transforEmotion: Sentiment analysis for text, image and video using transformer models.", | ||
"R package version 0.1.4.") | ||
) | ||
bibtype = "Article", | ||
key = "tomasevic2024decodi", | ||
title = "Decoding emotion dynamics in videos using dynamic Exploratory Graph Analysis and zero-shot image classification: A simulation and tutorial using the transforEmotion R package", | ||
author = c( | ||
person("Aleksandar", "Tomasevic"), | ||
person("Hudson", "Golino"), | ||
person("Alexander P", "Christensen") | ||
), | ||
journal = "PsyArXiv", | ||
year = 2024, | ||
url = "https://osf.io/preprints/psyarxiv/hf3g7", | ||
keywords = "at", | ||
doi = "10.31234/osf.io/hf3g7", | ||
language = "en" | ||
) |
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.
Some generated files are not rendered by default. Learn more about how customized files appear on GitHub.