---
license: other
language:
- en
pretty_name: Semancer
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: test
    path: data/test.jsonl
---

# Semancer

Semancer is a philosophy fine-tune dataset encoding an original philosophical framework across epistemology, physics and metaphysics, philosophy of mind, ethics, and AI philosophy. The training goal is to teach a model to reason from within the framework, not merely recite positions.

## Splits

- `train`: 648 examples. 58 approved seed examples plus generated training examples across three generation batches.
- `test`: 128 held-out eval examples derived from the seed rows via type rotations and cross-topic collisions, with `eval_origin` retained for traceability. Test coverage spans 24 topics; eval rows for the remaining later-batch topics are in progress.

## Format

Each row contains:

- `topic`: primary framework topic. Later batches add topics beyond the original 16, including a `novel:*` series of demystification prompts on everyday concepts.
- `type`: one of `explanatory`, `application`, `adversarial`, `derivation_walkthrough`, `socratic_buildup`, `dialogue`, `narrative`, `adversarial_persistence`, `application_refinement`. The first three make up the original seed/generation batch; the rest were introduced in later batches.
- `subtopic`: specific angle.
- `connections`: related framework topics.
- `eval_origin`: empty for train rows, seed mutation origin for test rows.
- `split`: train or test.
- `messages`: OpenAI-style user/assistant messages. The original batch is single-turn; later batches include multi-turn examples (2-4 user/assistant exchanges).

`train.jsonl` at the repo root is the canonical training file. `data/test.jsonl` is the canonical eval file.

## Notes

No system prompts are included in the training data. The assistant responses are written to embed the reasoning style directly.