CoolFace
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GabeA/chatbench-specific-detail

Specific Detail Part of ChatBench: a benchmark for evaluating embedding models on chat/conversational retrieval tasks. Task Description Given a query about a specific detail, find the conversation containing it. Dataset Statistics Split Queries Corpus Documents test 547 1595 Usage from datasets import load_dataset # Load corpus corpus = load_dataset("GabeA/chatbench-specific-detail", "corpus", split="test") # Load… See the full description on the dataset page: https://huggingface.co/datasets/GabeA/chatbench-specific-detail.

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face
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Specific Detail

Part of ChatBench: a benchmark for evaluating embedding models on chat/conversational retrieval tasks.

Task Description

Given a query about a specific detail, find the conversation containing it.

Dataset Statistics

SplitQueriesCorpus Documents
test5471595

Usage

python
from datasets import load_dataset

# Load corpus
corpus = load_dataset("GabeA/chatbench-specific-detail", "corpus", split="test")

# Load queries
queries = load_dataset("GabeA/chatbench-specific-detail", "queries", split="test")

# Load relevance judgments (qrels)
qrels = load_dataset("GabeA/chatbench-specific-detail", split="test")

With MTEB

python
import mteb

task = mteb.get_task("ChatBenchSpecificDetail")
evaluation = mteb.MTEB(tasks=[task])
evaluation.run(model)

Schema

Corpus

ColumnTypeDescription
_idstringDocument ID
textstringDocument text (full conversation or message window)
titlestringConversation title (if available)

Queries

ColumnTypeDescription
_idstringQuery ID
textstringQuery text

Qrels (default config)

ColumnTypeDescription
query-idstringQuery ID
corpus-idstringRelevant document ID
scoreintRelevance score (1 = relevant)

Citation

bibtex
@software{chatbench2026,
  title = {ChatBench: A Benchmark for Conversational Retrieval},
  author = {Abinante, Gabe},
  year = {2026},
  url = {https://github.com/gabinante/chat-bench},
  license = {Apache-2.0}
}