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>.">

transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models (original) (raw)

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>.

Version: 0.1.6
Depends: R (≥ 3.5.0)
Imports: dplyr, googledrive, LSAfun, Matrix, methods, pbapply, progress, remotes, reticulate
Suggests: knitr, markdown, rmarkdown, rstudioapi, testthat (≥ 3.0.0)
Published: 2025-05-15
DOI: 10.32614/CRAN.package.transforEmotion
Author: Alexander ChristensenORCID iD [aut], Hudson Golino ORCID iD [aut], Aleksandar TomaševićORCID iD [aut, cre]
Maintainer: Aleksandar Tomašević
License: GPL (≥ 3.0)
NeedsCompilation: no
Citation: transforEmotion citation info
Materials: README,
CRAN checks: transforEmotion results

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