dataset_info:
config_name: sep
features:
- name: system_prompt_clean
dtype: string
- name: prompt_instructed
dtype: string
- name: system_prompt_instructed
dtype: string
- name: prompt_clean
dtype: string
- name: witness
dtype: string
- name: id
dtype: int64
- name: info_type
dtype: string
- name: info_subtask
dtype: string
- name: info_subtask_descr
dtype: string
- name: info_appended_task_id
dtype: int64
- name: info_appended_type
dtype: string
- name: info_is_insistent
dtype: bool
splits:
- name: train
num_bytes: 10284323
num_examples: 9160
download_size: 3904497
dataset_size: 10284323
language:
- en
license: other
tags:
- rl
- alignment
- evaluation
size_categories:
- 1K<n<100K
configs:
- config_name: sep
data_files:
- split: train
path: sep/train-*
geodesic-research/sep
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/sep", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.
Configs
| Config | Source | Transform | Splits |
|---|---|---|---|
sep |
GitHub: egozverev/Should-It-Be-Executed-Or-Processed (pinned commit 7606c0696f20) |
map_column → map_column → project |
none |
Provenance
sep
Source: GitHub: egozverev/Should-It-Be-Executed-Or-Processed (pinned commit 7606c0696f20) (see sep.yaml).
Transform: map_column → map_column → project
python -m dataset_builder configs/sep.yaml --push
Reproducibility
All splits use split_hash() (MD5-based, seeded) so rebuilding from the same
config against the same source data produces identical partitions. For an
LLM-generated dataset, a provider's seed parameter is best-effort; pin
consumer loads to a specific HF commit SHA to avoid drift when the builder
re-pushes.
This card is auto-generated by dataset_builder.cards.