nesevals provides tooling for evaluating next edit suggestion (NES) scaffolds across different edit history formats, output formats, and models.
Install the package with:
# if needed:
# install.packages("pak")
pak::pak("posit-dev/nesevals")The package ships with a data frame nes_results that summarizes
experimental results:
library(dplyr)
library(nesevals)
glimpse(nes_results)
#> Rows: 20
#> Columns: 11
#> $ model <chr> "claude-haiku-4-5-20251001", "openai/gpt-oss-20b", …
#> $ prompt <chr> "rewrite-region", "rewrite-region", "rewrite-region…
#> $ edit_history <chr> "narrative", "narrative", "narrative", "narrative",…
#> $ output_format <chr> "rewrite_region", "rewrite_region", "rewrite_region…
#> $ n_completions <int> 80, 80, 85, 85, 240, 240, 85, 80, 80, 240, 240, 240…
#> $ n_processable <int> 80, 80, 85, 85, 240, 240, 84, 80, 80, 240, 240, 240…
#> $ n_exact <int> 54, 46, 55, 54, 123, 123, 44, 32, 30, 120, 114, 111…
#> $ mean_score <dbl> 4.49, 4.28, 4.11, 4.07, 4.03, 3.96, 3.75, 3.52, 3.5…
#> $ median_latency_ms <dbl> 1476, 550, 1138, 1034, 144, 144, 1228, 1370, 1576, …
#> $ mean_input_tokens <dbl> 1358, 1050, 1335, 1133, 1181, 1155, 1139, 1128, 112…
#> $ mean_output_tokens <dbl> 140, 169, 128, 109, 108, 107, 111, 95, 119, 219, 21…