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gptzeror

Identify Text Written by Large Language Models using GPTZero

https://github.com/christopherkenny/gptzeror

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ✓
    Committers with academic emails
    1 of 1 committers (100.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Identify Text Written by Large Language Models using GPTZero

Basic Info
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 3 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# gptzeror 


[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![R-CMD-check](https://github.com/christopherkenny/GPTZeroR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/christopherkenny/GPTZeroR/actions/workflows/R-CMD-check.yaml)


`gptzeror` provides an R interface to [GPTZero API](https://gptzero.me/). GPTZero predicts if text was generated by "AI" like ChatGPT. It splits documents by paragraph and sentence, allowing for detection when text is partially written by "AI" and partially by humans.

## Installation

You can install the development version of `gptzeror` from [GitHub](https://github.com/) with:

``` r
# install.packages('remotes')
remotes::install_github('christopherkenny/gptzeror')
```

## Example

Below is an example using the abstract of [Kenny, McCartan, Simko, Kuriwaki, and Imai (2023)](https://arxiv.org/abs/2208.06968).

```{r}
abstr <- 'Congressional district lines in many U.S. states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the U.S. House under the enacted plan to those under a set of alternative simulated plans that serve as a non-partisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but most of the electoral bias it creates cancels at the national level, giving Republicans two additional seats on average. Geography and redistricting rules separately contribute a moderate pro-Republican bias. Finally, we find that partisan gerrymandering reduces electoral competition and makes the partisan composition of the U.S. House less responsive to shifts in the national vote.'
```

We can pass text directly via `gptzero_predict_text()`.

```{r example}
library(gptzeror)
gptzero_predict_text(abstr)
```

The API also accepts common file types as uploads, including `.txt`, `.docx`, and `.pdf`. To access this endpoint, use `gptzero_predict_file()`.

```{r}
temp_file <- tempfile(fileext = '.txt')
cat(abstr, file = temp_file)

gptzero_predict_file(temp_file)
```

## Additional Information

Documentation for the [GPTZero API is available here](https://gptzero.me/docs).

Owner

  • Name: Christopher T. Kenny
  • Login: christopherkenny
  • Kind: user
  • Location: Cambridge, MA
  • Company: Harvard University

Redistricting and rstats. Harvard University, PhD Candidate, Department of Government. Cornell '19.

GitHub Events

Total
  • Release event: 1
  • Watch event: 1
  • Push event: 1
  • Create event: 1
Last Year
  • Release event: 1
  • Watch event: 1
  • Push event: 1
  • Create event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 25
  • Total Committers: 1
  • Avg Commits per committer: 25.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Christopher Kenny c****4@c****u 25
Committer Domains (Top 20 + Academic)
cornell.edu: 1

Issues and Pull Requests

Last synced: over 1 year ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 473 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: gptzeror

Identify Text Written by Large Language Models using 'GPTZero'

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 473 Last month
Rankings
Downloads: 19.1%
Forks count: 28.2%
Dependent packages count: 28.3%
Average: 29.5%
Stargazers count: 34.9%
Dependent repos count: 36.9%
Maintainers (1)
christopherkenny@fas.harvard.edu
Last synced: 10 months ago