Model, filtered dataset, code, evaluations and paper for 'Constraining Language Modeling Pretraining Data'
e8fb262 verified |
Download code/JUDGING.md from a-kemeklis/constraining-pretraining-data: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/a-kemeklis/constraining-pretraining-data/resolve/main/code/JUDGING.md
- Command line
-
hf download hf://a-kemeklis/constraining-pretraining-data/code/JUDGING.md
-
curl -L -o JUDGING.md https://huggingface.co/a-kemeklis/constraining-pretraining-data/resolve/main/code/JUDGING.md
1.31 kB
| # How the judging was run | |
| `ts300.py sample` writes `judge_packet_partN.md` files (10 story pairs each, A/B order randomized and balanced, | |
| grading rubric included in every packet) and a separate `judge_key.json`. | |
| Each packet was graded by a fresh Claude Opus 5.5 instance with no other context, run as a Claude Code subagent | |
| with this prompt (paths adjusted per packet). The packets were copied to a folder without the key first. | |
| > You are acting as a blind judge for a story-grading task. Read exactly one file: <packet path>. Follow the | |
| > grading instructions inside it exactly, and grade every item in it yourself; don't write or run any scripts | |
| > to grade. Write your complete reply (the notes for each item, then the CSV block with the exact header the | |
| > file specifies) to <reply path> using the Write tool. Do not open, list, search or run anything else: no | |
| > other files or directories and no shell commands. In particular, never look for or open any answer key. | |
| > When finished, reply with only the word: written | |
| Pasting each packet into a new chat with any capable LLM works the same way. Concatenate the replies in part | |
| order into `judge_replies.txt`, then: | |
| python ts300.py score --key judge_key.json --judge judge_replies.txt | |
| python paired_stats.py judge_key.json judge_replies.txt | |