pretty_name: RuleFollower Unified Sentence G/F Automatic Annotations
language: en
configs:
- config_name: sentence_labels
default: true
data_files:
- split: train
path: sentence_labels/*.jsonl.gz
- config_name: cots
data_files:
- split: train
path: cots/*.jsonl.gz
RuleFollower unified G/F automatic annotations
This is a training-only, model-annotated subset. It contains 1,983 source IDs from 16 datasets, four CoT generators per source (7,932 CoTs), and 251,870 sentence-label rows. The original 2,000-source sample used dataset-size-proportional quotas across 55,965 source rows.
cots/ contains the task, classification input, complete CoT, and canonical sentence IDs. sentence_labels/ contains sentence-level labels and evidence; join the two by cot_id and use sid within a CoT. source_manifest.csv lists the included source IDs and input-text hashes. metadata.json gives exact counts by generator.
G=1 means the Luna teacher confirmed grounding in the classification input; G=0 means it did not confirm grounding. F=1 means the frozen N1 pipeline confirmed a fallacy; F=0 means it did not confirm one after successfully processing the CoT. Neither zero is a human-verified opposite class. null is a technical abstention: use the separate training_masks.G and training_masks.F fields when computing losses.
Teacher: GPT-5.6-Luna (medium), direct-v3 G and frozen N1-r12 F. Exact input-text matches to the locked 160-source human pool were excluded before packaging (17 source IDs across each generator). Human test labels are not included. Exact duplicate texts may still occur within this training subset; group by input-text hash when splitting or sampling.
The classification inputs derive from RuleFollower's parsed datasets. Review source-dataset redistribution terms before making this subset public.