Datasets:
File size: 9,839 Bytes
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configs:
- config_name: bm25
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: bm25/NanoCodeRAGLibraryDocumentationSolutions-*
- split: NanoCodeRAGOnlineTutorials
path: bm25/NanoCodeRAGOnlineTutorials-*
- split: NanoCodeRAGProgrammingSolutions
path: bm25/NanoCodeRAGProgrammingSolutions-*
- split: NanoCodeRAGStackoverflowPosts
path: bm25/NanoCodeRAGStackoverflowPosts-*
- config_name: corpus
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: corpus/NanoCodeRAGLibraryDocumentationSolutions-*
- split: NanoCodeRAGOnlineTutorials
path: corpus/NanoCodeRAGOnlineTutorials-*
- split: NanoCodeRAGProgrammingSolutions
path: corpus/NanoCodeRAGProgrammingSolutions-*
- split: NanoCodeRAGStackoverflowPosts
path: corpus/NanoCodeRAGStackoverflowPosts-*
- config_name: harrier_oss_v1_270m
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: harrier_oss_v1_270m/NanoCodeRAGLibraryDocumentationSolutions-*
- split: NanoCodeRAGOnlineTutorials
path: harrier_oss_v1_270m/NanoCodeRAGOnlineTutorials-*
- split: NanoCodeRAGProgrammingSolutions
path: harrier_oss_v1_270m/NanoCodeRAGProgrammingSolutions-*
- split: NanoCodeRAGStackoverflowPosts
path: harrier_oss_v1_270m/NanoCodeRAGStackoverflowPosts-*
- config_name: qrels
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: qrels/NanoCodeRAGLibraryDocumentationSolutions-*
- split: NanoCodeRAGOnlineTutorials
path: qrels/NanoCodeRAGOnlineTutorials-*
- split: NanoCodeRAGProgrammingSolutions
path: qrels/NanoCodeRAGProgrammingSolutions-*
- split: NanoCodeRAGStackoverflowPosts
path: qrels/NanoCodeRAGStackoverflowPosts-*
- config_name: queries
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: NanoCodeRAGLibraryDocumentationSolutions/queries/test.parquet
- split: NanoCodeRAGOnlineTutorials
path: NanoCodeRAGOnlineTutorials/queries/test.parquet
- split: NanoCodeRAGProgrammingSolutions
path: NanoCodeRAGProgrammingSolutions/queries/test.parquet
- split: NanoCodeRAGStackoverflowPosts
path: NanoCodeRAGStackoverflowPosts/queries/test.parquet
language:
- code
tags:
- information-retrieval
- retrieval
- nano
- bm25
dataset_info:
- config_name: bm25
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
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num_examples: 200
- name: NanoCodeRAGOnlineTutorials
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num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 1110671
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
num_bytes: 1188297
num_examples: 200
download_size: 4676749
dataset_size: 4670737
- config_name: corpus
features:
- name: _id
dtype: string
- name: text
dtype: string
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- name: NanoCodeRAGOnlineTutorials
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- name: NanoCodeRAGProgrammingSolutions
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num_examples: 984
- name: NanoCodeRAGStackoverflowPosts
num_bytes: 47533159
num_examples: 10000
download_size: 56258007
dataset_size: 123269205
- config_name: harrier_oss_v1_270m
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
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- name: NanoCodeRAGOnlineTutorials
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- name: NanoCodeRAGProgrammingSolutions
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- name: NanoCodeRAGStackoverflowPosts
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num_examples: 200
download_size: 4678100
dataset_size: 4672021
- config_name: qrels
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
num_bytes: 3407
num_examples: 200
- name: NanoCodeRAGOnlineTutorials
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num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 3547
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
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num_examples: 200
download_size: 13196
dataset_size: 13718
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
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- name: NanoCodeRAGOnlineTutorials
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num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 17812
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
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download_size: 81432
dataset_size: 155960
---
# NanoCodeRAG
This dataset is a Nano-style retrieval dataset. Nano-series evaluation can be run easily with the [HAKARI-Bench](https://github.com/hotchpotch/hakari-bench).
NanoCodeRAG is derived from CodeRAG. It follows the Hugging Face Datasets layout convention used by [sentence-transformers/NanoBEIR-en](https://huggingface.co/datasets/sentence-transformers/NanoBEIR-en): each Nano split has separate `corpus`, `queries`, and `qrels` tables, and BM25 candidates are provided separately in a `bm25` table. This layout follows the NanoBEIR-style evaluation approach summarized in [NanoBEIR](https://huggingface.co/blog/sionic-ai/eval-sionic-nano-beir).
NanoCodeRAG contains 4 Nano retrieval splits derived from CodeRAG. Each split keeps up to 200 eligible queries and up to 10000 corpus documents, with exact duplicate query and document text removed where the generator records that policy.
## Source Links
- Source benchmark: `CodeRAG`
- `code-rag-bench/library-documentation`: https://huggingface.co/datasets/code-rag-bench/library-documentation
- `code-rag-bench/online-tutorials`: https://huggingface.co/datasets/code-rag-bench/online-tutorials
- `code-rag-bench/programming-solutions`: https://huggingface.co/datasets/code-rag-bench/programming-solutions
- `code-rag-bench/stackoverflow-posts`: https://huggingface.co/datasets/code-rag-bench/stackoverflow-posts
## Data Layout
This dataset uses four Hugging Face Datasets configs:
- `corpus`: documents with `_id` and `text`
- `queries`: queries with `_id` and `text`
- `qrels`: positive relevance labels with `query-id` and `corpus-id`
- `bm25`: BM25 candidate lists with `query-id` and `corpus-ids`
Each config uses the same Nano split names. If the actual generated dataset uses a different schema, config name, path layout, or field name, revise this section before publishing the README.
## Construction Steps
This dataset was built as follows. If the actual generation procedure differs, revise this section before publishing the README.
1. Use CodeRAG as the upstream benchmark or dataset family.
2. Load the source datasets recorded in `manifest.json` and per-split metadata files.
3. Use the source benchmark evaluation split, preferring `test` when available as the source evaluation split policy.
4. Create one Nano split for each selected source retrieval task.
5. Keep up to 200 eligible queries per Nano split.
6. Include qrels-positive documents for the selected queries.
7. Fill the corpus from source corpus order up to 10000 documents.
8. Remove exact duplicate document text within each split. If a removed duplicate was referenced by qrels, rewrite qrels to the kept document id when the generator records that policy.
9. Store document title and body as a single `text` field when the source provides both.
10. Generate BM25 top-100 candidates with the tokenization policy recorded per split.
11. If a qrels-positive document is missing from the raw BM25 result, insert it into the final `bm25` candidate list by replacing a tail non-positive candidate.
## BM25 Subset Policy
The `bm25` config is a candidate subset for first-stage retrieval and reranking. It is not a separate source dataset. Each row contains one query id and a ranked list of corpus ids.
BM25 candidates are generated from the selected corpus for each split. The configured candidate cap is top-100. When a qrels-positive document is not present in the raw BM25 result, the missing positive is forced into the final candidate list by replacing a tail candidate that is not positive for that query. Candidate ids are kept unique after replacement.
## Split Mapping
| Nano split | Source task | Source dataset | Queries | Corpus | Qrels |
|---|---|---|---:|---:|---:|
| `NanoCodeRAGLibraryDocumentationSolutions` | `CodeRAGLibraryDocumentationSolutions` | `code-rag-bench/library-documentation` | 200 | 8683 | 200 |
| `NanoCodeRAGOnlineTutorials` | `CodeRAGOnlineTutorials` | `code-rag-bench/online-tutorials` | 200 | 9997 | 200 |
| `NanoCodeRAGProgrammingSolutions` | `CodeRAGProgrammingSolutions` | `code-rag-bench/programming-solutions` | 200 | 984 | 200 |
| `NanoCodeRAGStackoverflowPosts` | `CodeRAGStackoverflowPosts` | `code-rag-bench/stackoverflow-posts` | 200 | 10000 | 200 |
## BM25 nDCG@10
`nDCG@10` is computed from the included BM25 ranking against the included qrels.
Tokenizer policy summary: `whitespace:python`.
| Nano split | Tokenizer | Forced BM25 positives | BM25 nDCG@10 |
|---|---|---:|---:|
| `NanoCodeRAGLibraryDocumentationSolutions` | `whitespace:python` | 85 | 0.2279 |
| `NanoCodeRAGOnlineTutorials` | `whitespace:python` | 17 | 0.7472 |
| `NanoCodeRAGProgrammingSolutions` | `whitespace:python` | 149 | 0.0138 |
| `NanoCodeRAGStackoverflowPosts` | `whitespace:python` | 10 | 0.6902 |
## Skipped Tasks
No source tasks were skipped.
## License
NanoCodeRAG is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.
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