Supervised Machine Learning for Textual Data Using Transformers
and 'Quanteda'

person("Chung-hong", "Chan", , "chainsawtiney@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6232-7530"))

Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>.

quanteda.textmodels, testthat (>= 3.0.0), withr

jsonlite, lime, quanteda, reticulate, utils, stats

Chung-hong Chan [aut, cre] (<https://orcid.org/0000-0002-6232-7530>)

c(person("David", "Schoch", , "david@schochastics.net", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2952-4812")),
      person("Chung-hong", "Chan", ,"chainsawtiney@gmail.com", role = c("aut"), comment = c(ORCID = "0000-0002-6232-7530")),
      person("Johannes", "Gruber", ,"JohannesB.Gruber@gmail.com", role = c("ctb"), comment = c(ORCID = "0000-0001-9177-1772")))

An implementation of calls designed to collect and organize Mastodon data via its Application Program Interfaces (API), which can be found at the following URL: <https://docs.joinmastodon.org/>.

clipr, curl, dplyr, httr, jsonlite, tibble

knitr, rmarkdown, testthat (>= 3.0.0), rstudioapi, vcr (>=
0.6.0)

Reconstructing Reproducible R Computational Environments with Ease

c(person("Chung-hong", "Chan", , "chainsawtiney@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6232-7530")),
person("David", "Schoch", , "david@schochastics.net", role = "aut",
comment = c(ORCID = "0000-0003-2952-4812")))

Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.