macmillancontentscience. To fix this you can add URL: https://jonthegeek.r-universe.dev/tidybert to the package DESCRIPTION file. See also theR-universe documentation.Package: tidybert 0.0.0.9900
tidybert: Tidy BERT-like Models
Implements BERT-like NLP models with a consistent interface for fitting and creating predictions. The models are fully compatible with the tidymodels framework.
Authors:
tidybert_0.0.0.9900.tar.gz
tidybert_0.0.0.9900.zip(r-4.7)tidybert_0.0.0.9900.zip(r-4.6)tidybert_0.0.0.9900.zip(r-4.5)
tidybert_0.0.0.9900.tgz(r-4.6-any)tidybert_0.0.0.9900.tgz(r-4.5-any)
tidybert_0.0.0.9900.tar.gz(r-4.7-any)tidybert_0.0.0.9900.tar.gz(r-4.6-any)
tidybert_0.0.0.9900.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
tidybert/json (API)
| # Install 'tidybert' in R: |
| install.packages('tidybert', repos = c('https://jonthegeek.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/macmillancontentscience/tidybert/issues
Last updated from:e25e191224. Checks:8 ERROR, 1 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | ERROR | 173 | ||
| source / vignettes | ERROR | 229 | ||
| linux-release-x86_64 | ERROR | 150 | ||
| macos-release-arm64 | ERROR | 124 | ||
| macos-oldrel-arm64 | ERROR | 171 | ||
| windows-devel | ERROR | 159 | ||
| windows-release | ERROR | 155 | ||
| windows-oldrel | ERROR | 190 | ||
| wasm-release | OK | 139 |
Exports:%>%bertbert_classificationbert_regressionbert_typemodel_bert_linearn_tokenstidy_bert_output
Dependencies:bitbit64cachemcallrclicorocrayondescdigestdlrdplyrfarverfastmapfastmatchfsgenericsgluehardhathmsjsonlitelabelinglifecycleluzmagrittrmemoisepiecemakerpillarpkgconfigprettyunitsprocessxprogresspspurrrR6rappdirsRColorBrewerRcpprlangsafetensorsscalessparsevctrsstringistringrtibbletidyselecttorchtorchtransformersutf8vctrsviridisLitewithrwordpiecewordpiece.datazeallot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fine-Tune a BERT Model | bert |
| Fit a BERT-style neural network | bert_classification bert_classification.data.frame bert_classification.default bert_classification.formula bert_classification.matrix |
| Fit a BERT-style neural network | bert_regression bert_regression.data.frame bert_regression.default bert_regression.formula bert_regression.matrix |
| Pre-Trained BERT Model | bert_type |
| Pretrained BERT Model with Linear Output | model_bert_linear |
| Number of Tokens | n_tokens |
| Predict from a 'bert_classification' model. | predict.bert_classification |
| Predict from a 'bert_regression' model. | predict.bert_regression |
| Tidy the BERT Output | tidy_bert_output |
