RAGChain Docs
  • Introduction
  • Quick Start
  • Installation
  • RAGchain Structure
    • File Loader
      • Dataset Loader
        • Ko-Strategy-QA Loader
      • Hwp Loader
      • Rust Hwp Loader
      • Win32 Hwp Loader
      • OCR
        • Nougat Loader
        • Mathpix Markdown Loader
        • Deepdoctection Loader
    • Text Spliter
      • Recursive Text Splitter
      • Markdown Header Splitter
      • HTML Header splitter
      • Code splitter
      • Token splitter
    • Retrieval
      • BM25 Retrieval
      • Hybrid Retrieval
      • Hyde Retrieval
      • VectorDB Retrieval
    • LLM
    • DB
      • MongoDB
      • Pickle DB
    • Reranker
      • BM25 Reranker
      • UPR Reranker
      • TART Reranker
      • MonoT5 Reranker
      • LLM Reranker
    • Benchmark
      • Auto Evaluator
      • Dataset Evaluators
        • Qasper
        • Ko-Strategy-QA
        • Strategy-QA
        • ms-marco
  • Utils
    • Query Decomposition
    • Evidence Extractor
    • Embedding
    • Slim Vector Store
      • Pinecone Slim
      • Chroma Slim
    • File Cache
    • Linker
      • Redis Linker
      • Dynamo Linker
      • Json Linker
    • REDE Search Detector
    • Semantic Clustering
  • Pipeline
    • BasicIngestPipeline
    • BasicRunPipeline
    • RerankRunPipeline
    • ViscondeRunPipeline
  • For Advanced RAG
    • Time-Aware RAG
    • Importance-Aware RAG
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  • Overview
  • Example Use
  1. RAGchain Structure
  2. Benchmark
  3. Dataset Evaluators

Ko-Strategy-QA

Overview

Ko-Strategy-QA is Korean version of Strategy-QA, which is translated by DeepL. Ko-Strategy-QA is the only option for evaluating multi-hop questions in Korean. Also, RAGchain makers made this dataset^^

Example Use

from RAGchain.benchmark.dataset import KoStrategyQAEvaluator

pipeline = <your pipeline>
retrievals = [<your retrieval>]
db = <your db>

evaluator = KoStrategyQAEvaluator(pipeline, evaluate_size=100)
evaluator.ingest(retrievals, db) # This code will ingest whole paragraphs in Ko-Strategy-QA dataset. You only need to run this once.
result = evaluator.evaluate()

# print result summary (mean values)
print(result.results)
# print result DataFrame
print(result.each_results)
PreviousQasperNextStrategy-QA

Last updated 1 year ago