| --- |
| license: apache-2.0 |
| task_categories: |
| - text-retrieval |
| language: |
| - en |
| tags: |
| - information-retrieval |
| - benchmark |
| - clinical-trials |
| - code-search |
| - legal-qa |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: clinical_trial |
| data_files: |
| - split: queries |
| path: "clinical_trial/queries.jsonl" |
| - split: documents |
| path: "clinical_trial/documents.jsonl" |
| - split: qrels |
| path: "clinical_trial/qrels.jsonl" |
| - config_name: code_retrieval |
| data_files: |
| - split: queries |
| path: "code_retrieval/queries.jsonl" |
| - split: documents |
| path: "code_retrieval/documents.jsonl" |
| - split: qrels |
| path: "code_retrieval/qrels.jsonl" |
| - config_name: legal_qa |
| data_files: |
| - split: queries |
| path: "legal_qa/queries.jsonl" |
| - split: documents |
| path: "legal_qa/documents.jsonl" |
| - split: qrels |
| path: "legal_qa/qrels.jsonl" |
| - config_name: paper_retrieval |
| data_files: |
| - split: queries |
| path: "paper_retrieval/queries.jsonl" |
| - split: documents |
| path: "paper_retrieval/documents.jsonl" |
| - split: qrels |
| path: "paper_retrieval/qrels.jsonl" |
| - config_name: set_operation_entity_retrieval |
| data_files: |
| - split: queries |
| path: "set_operation_entity_retrieval/queries.jsonl" |
| - split: documents |
| path: "set_operation_entity_retrieval/documents.jsonl" |
| - split: qrels |
| path: "set_operation_entity_retrieval/qrels.jsonl" |
| - config_name: stack_exchange |
| data_files: |
| - split: queries |
| path: "stack_exchange/queries.jsonl" |
| - split: documents |
| path: "stack_exchange/documents.jsonl" |
| - split: qrels |
| path: "stack_exchange/qrels.jsonl" |
| - config_name: theorem_retrieval |
| data_files: |
| - split: queries |
| path: "theorem_retrieval/queries.jsonl" |
| - split: documents |
| path: "theorem_retrieval/documents.jsonl" |
| - split: qrels |
| path: "theorem_retrieval/qrels.jsonl" |
| - config_name: tip_of_the_tongue |
| data_files: |
| - split: queries |
| path: "tip_of_the_tongue/queries.jsonl" |
| - split: documents |
| path: "tip_of_the_tongue/documents.jsonl" |
| - split: qrels |
| path: "tip_of_the_tongue/qrels.jsonl" |
| --- |
| |
| # NanoCrumb Dataset |
|
|
| A curated subset of the [Crumb](https://huggingface.co/datasets/jfkback/crumb) retrieval dataset, designed for rapid experimentation and evaluation of information retrieval systems. |
|
|
| ## Dataset Summary |
|
|
| **NanoCrumb** distills the large Crumb dataset (10.5 GB, 6.36M rows) into a manageable benchmark while maintaining task diversity across 8 different retrieval domains. |
|
|
| - **Total Size**: ~125 MB (JSONL format) |
| - **Queries**: 400 (50 per task split) |
| - **Documents**: 30,040 unique passages |
| - **Query-Document Pairs**: 31,754 |
| - **Configs**: 8 task-specific configs |
|
|
| ## Configs (Task Splits) |
|
|
| Each config represents a different retrieval domain: |
|
|
| | Config Name | Queries | Documents | Docs/Query (avg) | Description | |
| |------------|---------|-----------|------------------|-------------| |
| | `clinical_trial` | 50 | 22,251 | 464 | Match patients to clinical trials | |
| | `paper_retrieval` | 50 | 4,402 | 102 | Find relevant academic papers | |
| | `set_operation_entity_retrieval` | 50 | 1,533 | 31 | Entity-based retrieval | |
| | `code_retrieval` | 50 | 1,206 | 24 | Find relevant code snippets | |
| | `tip_of_the_tongue` | 50 | 363 | 7 | Recall items from vague descriptions | |
| | `stack_exchange` | 50 | 125 | 3 | Find relevant Q&A posts | |
| | `legal_qa` | 50 | 86 | 2 | Legal question answering | |
| | `theorem_retrieval` | 50 | 74 | 2 | Find mathematical theorems | |
|
|
| ## Dataset Structure |
|
|
| Each config contains three splits: |
|
|
| ### `queries` |
| - `query_id`: Unique query identifier (string) |
| - `query_content`: The query text (string) |
| - `instruction`: Task-specific instructions (string) |
| - `passage_qrels`: List of relevant passages with graded relevance scores (list) |
| - `task_split`: Task domain name (string) |
| - `metadata`: Additional task-specific information (string) |
| - `use_max_p`: Boolean flag for MaxP aggregation (bool) |
|
|
| ### `documents` |
| - `document_id`: Unique document identifier (string) |
| - `document_content`: The passage text (string) |
| - `parent_id`: Links passages to source documents (string) |
| - `task_split`: Task domain name (string) |
| - `metadata`: Document metadata (string) |
|
|
| ### `qrels` |
| - `query_id`: Query identifier (string) |
| - `document_id`: Document identifier (string) |
| - `relevance_score`: Graded relevance 0.0-2.0 (float) |
| - `binary_relevance`: Binary relevance 0 or 1 (int) |
| - `task_split`: Task domain name (string) |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load a specific config (task split) |
| clinical_data = load_dataset("YOUR_USERNAME/nanocrumb", "clinical_trial") |
| |
| # Access the splits |
| queries = clinical_data['queries'] |
| documents = clinical_data['documents'] |
| qrels = clinical_data['qrels'] |
| |
| # Load all configs |
| all_configs = [ |
| "clinical_trial", "code_retrieval", "legal_qa", "paper_retrieval", |
| "set_operation_entity_retrieval", "stack_exchange", |
| "theorem_retrieval", "tip_of_the_tongue" |
| ] |
| |
| for config_name in all_configs: |
| data = load_dataset("YOUR_USERNAME/nanocrumb", config_name) |
| print(f"{config_name}: {len(data['queries'])} queries") |
| ``` |
|
|
| ## Sampling Methodology |
|
|
| For each task split: |
| 1. **Query Selection**: Randomly sampled 50 queries from evaluation set (seed=42) |
| 2. **Document Selection**: |
| - Include ALL positive documents (binary_relevance=1) |
| - Fill remainder with hard negatives (relevance=0) to reach ~100 docs per query |
| - Target: ~5,000 documents per task split |
| 3. **Deduplication**: Documents shared across queries are deduplicated within each config |
| |
| ## Use Cases |
| |
| - 🚀 **Rapid prototyping** of retrieval models |
| - 🧪 **Quick benchmarking** without downloading large datasets |
| - 📚 **Educational purposes** for learning IR techniques |
| - 🔬 **Ablation studies** across diverse domains |
| |
| ## Citation |
| |
| If you use NanoCrumb, please cite the original Crumb dataset: |
| |
| ```bibtex |
| @misc{crumb2024, |
| title={Crumb: A Comprehensive Retrieval Benchmark}, |
| author={[Original Crumb Authors]}, |
| year={2024}, |
| url={https://huggingface.co/datasets/jfkback/crumb} |
| } |
| ``` |
| |
| ## License |
| |
| This dataset inherits the license from the original [Crumb dataset](https://huggingface.co/datasets/jfkback/crumb). |
| |
