# Using DeliChess

## Load from the Hugging Face Hub

After the release is published, install `datasets` and load one of the three
configurations:

```python
from datasets import load_dataset

utterances = load_dataset(
    "SpaceHunterInf/DeliChess", "utterances", split="train"
)
events = load_dataset("SpaceHunterInf/DeliChess", "events", split="train")
dialogues = load_dataset("SpaceHunterInf/DeliChess", "dialogues", split="train")
```

`utterances` is the recommended starting point for dialogue and annotation
research. `events` preserves the complete ordered task record, including puzzle
definitions and pre-/post-deliberation submissions. `dialogues` provides one
summary row per dialogue. The name `train` is a storage split, not a recommended
machine-learning partition; split by `dialogue_id` to prevent leakage.

## Load the audit copy locally

```python
from datasets import load_dataset

utterances = load_dataset(
    "csv", data_files="HuggingFace_DeliChess/data/utterances.csv", split="train"
)
```

The files can also be read with pandas:

```python
import pandas as pd

utterances = pd.read_csv("HuggingFace_DeliChess/data/utterances.csv")
```

## Choosing annotations

Columns prefixed with `human_` are the corpus annotations used in the paper.
Columns prefixed with `gemini_` are independent predictions from Gemini 3.5
Flash. They are included for comparison and method development; they are not
gold labels and should not silently replace the human annotations. The four
`*_match` columns compare the two sources.

Read `docs/ANNOTATION_GUIDELINES.md` before interpreting any label. In
particular, `NO_HEDGED` is the historical label name for an unhedged task
stance, and agent usefulness is not a general measure of utterance quality.

## Reconstruct a dialogue

```python
d001 = (
    utterances
    .filter(lambda row: row["dialogue_id"] == "D001")
    .sort("utterance_position")
)
```

For statistical analysis, utterances should not be treated as independent:
they are nested within dialogues and speakers.