Fine Tuning Latent Dirichlet Allocation for Transfer Learning | Contribution | tLDA | The Model | Selecting the prior weight | The tidylda Implementation of tLDA | Aligning Vocabulary | Adding New Topics | Initializing $\boldsymbol{Cd}^{(t)}$ and $\boldsymbol{Cv}^{(t)}$ | Partitioning the Posterior to Collapse $K$ Weights ($\omega_k$) Into One, $a$
Package: tidylda 0.0.7.999
tidylda: Latent Dirichlet Allocation Using 'tidyverse' Conventions
Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>, using style conventions from the 'tidyverse', Wickham et al. (2019)<doi:10.21105/joss.01686>, and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning.
Authors:
tidylda_0.0.7.999.tar.gz
tidylda_0.0.7.999.zip(r-4.7)tidylda_0.0.7.999.zip(r-4.6)tidylda_0.0.7.999.zip(r-4.5)
tidylda_0.0.7.999.tgz(r-4.6-x86_64)tidylda_0.0.7.999.tgz(r-4.6-arm64)tidylda_0.0.7.999.tgz(r-4.5-x86_64)tidylda_0.0.7.999.tgz(r-4.5-arm64)
tidylda_0.0.7.999.tar.gz(r-4.7-arm64)tidylda_0.0.7.999.tar.gz(r-4.7-x86_64)tidylda_0.0.7.999.tar.gz(r-4.6-arm64)tidylda_0.0.7.999.tar.gz(r-4.6-x86_64)
tidylda_0.0.7.999.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidylda/json (API)
| # Install 'tidylda' in R: |
| install.packages('tidylda', repos = c('https://tommyjones.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tommyjones/tidylda/issues
Datasets:
- nih_sample - Abstracts and metadata from NIH research grants awarded in 2014
- nih_sample_dtm - Abstracts and metadata from NIH research grants awarded in 2014
Last updated from:7d8780ad8b. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 233 | ||
| linux-devel-x86_64 | OK | 187 | ||
| source / vignettes | OK | 267 | ||
| linux-release-arm64 | OK | 180 | ||
| linux-release-x86_64 | OK | 192 | ||
| macos-release-arm64 | OK | 174 | ||
| macos-release-x86_64 | OK | 552 | ||
| macos-oldrel-arm64 | OK | 169 | ||
| macos-oldrel-x86_64 | OK | 357 | ||
| windows-devel | OK | 179 | ||
| windows-release | OK | 180 | ||
| windows-oldrel | OK | 172 | ||
| wasm-release | OK | 140 |
Exports:augmentcalc_prob_coherenceglanceposteriorrefittidytidylda
Dependencies:clicpp11dplyrgenericsgluegtoolsjaneaustenrlatticelifecyclemagrittrMatrixmvrsquaredpillarpkgconfigpurrrR6RcppRcppArmadilloRcppProgressRcppThreadrlangSnowballCstringistringrtibbletidyrtidyselecttidytexttokenizersutf8vctrswithr
Transfer Learning with LDA (tLDA)
Last update: 2024-07-03
Started: 2022-11-26
Introduction to tidylda
Introduction to the tidylda package | Tidy Methods | Other Notable Features | Discussion | Installation | Example
Last update: 2024-07-03
Started: 2022-11-26
Probabilistic Coherence
Last update: 2023-07-14
Started: 2022-11-26
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Augment method for 'tidylda' objects | augment.tidylda |
| Probabilistic coherence of topics | calc_prob_coherence |
| Glance method for 'tidylda' objects | glance.tidylda |
| Abstracts and metadata from NIH research grants awarded in 2014 | nih nih_sample nih_sample_dtm |
| Draw from the marginal posteriors of a tidylda topic model | posterior posterior.tidylda |
| Get predictions from a Latent Dirichlet Allocation model | predict.tidylda |
| Print Method for tidylda | print.tidylda |
| Update a Latent Dirichlet Allocation topic model | refit.tidylda |
| Tidy a matrix from a 'tidylda' topic model | tidy.matrix tidy.tidylda |
| Fit a Latent Dirichlet Allocation topic model | tidylda |
