document_id string | document_text string | document_filename string | document_metadata dict | document_summary string | summarization_model string | chunks list | multihop_chunks list |
|---|---|---|---|---|---|---|---|
0dcba7dc-044a-49a8-bdc7-1320398a22f3 | "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
} | Qwen/Qwen3-4B-Instruct-2507 | [{"chunk_id":"0dcba7dc-044a-49a8-bdc7-1320398a22f3_0","chunk_text":"5\n2\n0\n2\n\nr\np\nA\n2\n\n]\nL(...TRUNCATED) | [{"chunk_ids":["0dcba7dc-044a-49a8-bdc7-1320398a22f3_1","0dcba7dc-044a-49a8-bdc7-1320398a22f3_4"],"c(...TRUNCATED) |
Test Custom Schema Simple
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
- 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: test-custom-schema-simple
hf_organization: $HF_ORGANISATION
push_to_hub: true
model_list:
- model_name: Qwen/Qwen3-4B-Instruct-2507
pipeline:
ingestion:
source_documents_dir: example/default_example/data
output_dir: data/custom_schema_simple_processed
supported_file_extensions:
- .md
- .txt
- .pdf
chunking: {}
single_shot_question_generation:
chunk_sampling:
enable: false
num_samples: 100
strategy: random
random_seed: 42
use_structured_outputs: false
structured_fallback: true
custom_schema_path: .ai/comms/example_pydantic.py
custom_schema_class: Question
custom_schema_auto_batch: true
debug: true
(This dataset card was automatically generated by YourBench)
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