| license: other | |
| language: | |
| - en | |
| pretty_name: Semancer | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train.parquet | |
| - split: test | |
| path: data/test.parquet | |
| # 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`: 436 examples, approved seed examples plus generated training examples. | |
| - `test`: 116 held-out eval examples derived from type rotations and cross-topic collisions, with `eval_origin` retained for traceability. | |
| ## Format | |
| Each row contains: | |
| - `topic`: primary framework topic. | |
| - `type`: `explanatory`, `application`, or `adversarial`. | |
| - `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 single-turn user/assistant messages. | |
| JSONL copies are included beside the Parquet files for direct inspection and training pipelines that prefer JSONL. | |
| ## Notes | |
| No system prompts are included in the training data. The assistant responses are written to embed the reasoning style directly. | |