# DADOES Data

DADOES starts from `GoEmotions`-style multi-label mood detection, but the target
domain is an English daily report written by an agent in a simulated world.

Training records should use JSON Lines:

```json
{"text":"I repaired the shelter and felt satisfied before sunset.","labels":["satisfied"]}
{"text":"The wolves followed me back, and I felt anxious and tired.","labels":["anxious","tired"]}
```

The first production dataset should combine:

- GoEmotions-derived public examples mapped into the DADOES label set.
- License-reviewed public loneliness examples.
- Agent daily reports sampled from the world.
- Human or LLM-reviewed labels for those reports.

The public dataset survey is tracked in `DATASETS.md`. The practical decision
from that survey is that `lonely` can be improved with external English text
data, while `bored` and `tired` require project-owned agent-report labels.

Optional external sources are read from `data/raw/external`:

```text
data/raw/external/
  dadoes-domain/train.jsonl
  dadoes-domain/dev.jsonl
  dadoes-domain/test.jsonl
  loneliness_causes/train_data.csv
  loneliness_causes/validation_data.csv
  loneliness_causes/test_data.csv
  text_emotion/text_emotion.csv
  prajwal_text_emotion/final_dataset.csv
  um1neko/train.json
  um1neko/dev.json
  um1neko/test.json
  empathetic_dialogues/train.csv
  empathetic_dialogues/validation.csv
  empathetic_dialogues/test.csv
  fig_loneliness/train.jsonl
  fig_loneliness/dev.jsonl
  fig_loneliness/test.jsonl
```

Run `cargo run --release --features dataset-download --bin
prepare_external_datasets` from the repository root to download the public
raw-file sources.

The Rust trainer can also read locally exported non-commercial sources:

- `empathetic_dialogues/*.csv` must contain `context`, `prompt`, and
  `utterance` columns.
- `fig_loneliness/*.jsonl` must contain `text` and `lonely` fields.

DADOES does not depend on Python or Hugging Face `datasets` to produce those
files. The trainer requires `DADOES_INCLUDE_NON_COMMERCIAL=1` before it will
read them.

The Rust baseline in `src/lib.rs` uses a small in-crate seed set only so the
project has a runnable machine-learning path from the first commit.