gpt2samples

The goal of gpt2samples is to help users explore the various sample texts as generated by Open AI’s new GPT2 transformer based language model.

An original implementation of a smaller version of GPT-2 can be found here, and the original sample text files can be found here.

Data

This package contains the following data, stored as tibbles:

tibble	description
conditional-t07	Conditionally generated samples, with context prompts from WebText test corpus, default settings (temperature 1 and no truncation).
conditional-topk40	Conditionally generated samples, with context prompts from WebText test corpus, with temperature 0.7
conditional	Conditionally generated samples, with context prompts from WebText test corpus, with truncation and top_k 40.
unconditional	Unconditionally generated samples, default settings.
unconditional-t07	Unconditionally generated samples, with temperature 0.7
unconditional-topk40	Unconditionally generated samples, with truncation and top_k 40.

Additionally, all the generated samples (conditional and unconditional) can be explored by calling all_samples().

Installation

You can install the released version of gpt2samples from GitHub with:

# install.packages("gpt2samples")
# install.packages("devtools")
devtools::install_github("kanishkamisra/gpt2samples")

Example

This is a basic example to explore the data using dplyr verbs

library(dplyr)
library(gpt2samples)

conditional %>%
  filter(id == 100)
#> # A tibble: 2 x 4
#>   file         id type     text                                            
#>   <chr>     <int> <chr>    <chr>                                           
#> 1 conditio…   100 sample   the waterbody that you are managing, getting pr…
#> 2 conditio…   100 complet… Permit, WDFW ensures that nonconventional child…

unconditional_t07 %>%
  filter(id == 250)
#> # A tibble: 213 x 3
#>    file              id text                                               
#>    <chr>          <int> <chr>                                              
#>  1 unconditional…   250 This question already has an answer here: How do I…
#>  2 unconditional…   250 ""                                                 
#>  3 unconditional…   250 This is a basic question regarding text editing. T…
#>  4 unconditional…   250 ""                                                 
#>  5 unconditional…   250 (A)                                                
#>  6 unconditional…   250 ""                                                 
#>  7 unconditional…   250 (B)                                                
#>  8 unconditional…   250 ""                                                 
#>  9 unconditional…   250 (A)                                                
#> 10 unconditional…   250 ""                                                 
#> # … with 203 more rows

all_samples() %>%
  filter(file == "conditional") %>%
  tail()
#> # A tibble: 6 x 4
#>   file         id type     text                                            
#>   <chr>     <int> <chr>    <chr>                                           
#> 1 conditio…   500 complet… "BOP will be remembered for it's technically in…
#> 2 conditio…   500 complet… ""                                              
#> 3 conditio…   500 complet… There were literal lap times in running the wat…
#> 4 conditio…   500 complet… ""                                              
#> 5 conditio…   500 complet… ""                                              
#> 6 conditio…   500 complet… I was voiced by legendary actor turns down play…

all_samples() %>%
  group_by(file) %>%
  summarise(total_lines = n())
#> # A tibble: 6 x 2
#>   file                 total_lines
#>   <chr>                      <int>
#> 1 conditional                18067
#> 2 conditional-t07            24081
#> 3 conditional-topk40         20405
#> 4 unconditional              19469
#> 5 unconditional-t07          28841
#> 6 unconditional-topk40       21188

Additional exploration can use Julia Silge and David Robinson’s tidytext package, among others to analyze the generated text as produced by GPT-2.

Contributor Code of Conduct

Please note that the ‘gpt2samples’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.