See table below for files related to each tutorial. I will do my best to keep things up-to-date, but feel free to reach out you have any thoughts or suggestions (see contact info below).
| Task | Datasets Used | Colab Link |
|---|---|---|
| Creating Fixed Sentence Embeddings | train-data.csv -test-data.csv |
 |
| Text Classification with Fixed Embeddings | -aggregate-word-embedding-data.csv -sentence-SBERT-embedding-data.csv -sentence-USE-embedding-data.csv |
 |
| Fine-Tuning Transformers for Text Classification of Big Five Items | -train-data.csv -test-data.csv |
|
| Fine-Tuning Transformers for Big Five Inclusion | -supplemental-item-data.csv |
|
| Few Shot Learning with Transformers | -train-data.csv -test-data.csv |
|
Most example use Python though some R utilities are provided. Data can be found in the data/ and raw-data/ folders of this repository. We recommend using the Colab notebooks (in the tables above) to progress through the examples more easily.
A .zip file of the repository can be by accessing the repository’s
Github page then clicking the clone followed by download zip
Files will be published on Github as a public repository. Those
wishing to download the files used in this research can do so in
command-line using the following commands:
- PC Users should first download Git Bash
- Open Git Bash
- Change your directory to the location you would like to store the repository
$ cd ~/Documents/
- Use
git cloneto create a copy of the entire repository into the current directory
$ git clone https://github.com/Shea-Fyffe/transforming-personality-scales.git
- Mac Users should first download Git
- Open the macOS Terminal App
- Change your directory to the location you would like to store the repository
cd ~/
- Use
git cloneto create a copy of the entire repository into the current directory
git clone https://github.com/Shea-Fyffe/transforming-personality-scales.git
We recommend accessing the software tutorials through Google Colaboratory (i.e., Google Colab), which is a relatively intuitive cloud-based service that allows researchers, practitioners, and students to access high-powered virtual machines at little-to-no cost (Bisong, 2019).
For an overview of Google Colab please see the following notebook
If you have any questions or concerns---or want a copy of the research paper associated with this repository, please reach out:
