document_id string | document_text string | document_filename string | document_metadata dict | document_summary string | summarization_model string | chunks list | multihop_chunks list |
|---|---|---|---|---|---|---|---|
4bfcf635-23d2-46b0-b7dc-11bea2025104 | "5\n2\n0\n2\n\nr\np\nA\n2\n\n]\nL\nC\n.\ns\nc\n[\n\n1\nv\n3\n3\n8\n1\n0\n.\n4\n0\n5\n2\n:\nv\ni\nX\n(...TRUNCATED) | yourbench_arxiv_paper.md | {
"file_size": 133539
} | zai-org/GLM-4.5 | [{"chunk_id":"4bfcf635-23d2-46b0-b7dc-11bea2025104_0","chunk_text":"5\n2\n0\n2\n\nr\np\nA\n2\n\n]\nL(...TRUNCATED) | [{"chunk_ids":["4bfcf635-23d2-46b0-b7dc-11bea2025104_2","4bfcf635-23d2-46b0-b7dc-11bea2025104_4"],"c(...TRUNCATED) |
Yourbench Simple Example
This dataset was generated using YourBench (v0.6.0), an open-source framework for generating domain-specific benchmarks from document collections.
Pipeline Steps
- ingestion: Read raw source documents, convert them to normalized markdown and save for downstream steps
- summarization: Perform hierarchical summarization: chunk-level LLM summaries followed by combine-stage reduction
- chunking: Split texts into token-based single-hop and multi-hop chunks
- single_shot_question_generation: Generate standalone question-answer pairs per chunk using LLM
Reproducibility
To reproduce this dataset, use YourBench v0.6.0 with the following configuration:
hf_configuration:
hf_dataset_name: yourbench-simple-example
hf_organization: $HF_ORGANISATION
hf_token: $HF_TOKEN
model_list:
- model_name: zai-org/GLM-4.5
api_key: $API_KEY
provider: auto
pipeline:
ingestion:
source_documents_dir: example/default_example/data
output_dir: example/default_example/processed
summarization: {}
chunking: {}
single_shot_question_generation: {}
prepare_lighteval: {}
(This dataset card was automatically generated by YourBench)
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