| # SVE-ASCII-Dataset | |
| Training data for ASCII art generation and understanding. From *Unlocking the Latent Canvas: Eliciting and Benchmarking Symbolic Visual Expression in LLMs*. | |
| ## Files | |
| | File | Records | Format | | |
| |------|---------|--------| | |
| | `train/train_dataset.json` | 13,668 | Chat messages | | |
| | `seed/generator-seed.json` | 1,165 | instruction + art | | |
| ## Schemas | |
| **Train** (`train_dataset.json`) – chat format for fine-tuning: | |
| ```json | |
| { | |
| "messages": [ | |
| { "role": "system", "content": "..." }, | |
| { "role": "user", "content": "..." }, | |
| { "role": "assistant", "content": "..." } | |
| ] | |
| } | |
| ``` | |
| Works with standard Chat/instruction tuning (Transformers, TRL, etc.). | |
| **Seed** (`generator-seed.json`) – raw pairs for synthesis: | |
| ```json | |
| { | |
| "instruction": "An ASCII art representation of a milk carton.", | |
| "category": "People", | |
| "art": " _________\n | _______ |\n..." | |
| } | |
| ``` | |
| Used by `dataset-synthesis.py` to produce more training data. | |
| ## Usage | |
| Place this repo under `SVE-ASCII/data/` (or symlink). Point your training script or `dataset-synthesis.py` to the paths above. | |