USAGE.md

Using DeliChess

Load from the Hugging Face Hub

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

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

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:

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

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.