Datasets:
File size: 10,882 Bytes
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configs:
- config_name: corpus
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: corpus/NanoCodeRAGLibraryDocumentationSolutions-00000-of-00001.parquet
- split: NanoCodeRAGOnlineTutorials
path: corpus/NanoCodeRAGOnlineTutorials-00000-of-00001.parquet
- split: NanoCodeRAGProgrammingSolutions
path: corpus/NanoCodeRAGProgrammingSolutions-00000-of-00001.parquet
- split: NanoCodeRAGStackoverflowPosts
path: corpus/NanoCodeRAGStackoverflowPosts-00000-of-00001.parquet
- config_name: queries
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: queries/NanoCodeRAGLibraryDocumentationSolutions-00000-of-00001.parquet
- split: NanoCodeRAGOnlineTutorials
path: queries/NanoCodeRAGOnlineTutorials-00000-of-00001.parquet
- split: NanoCodeRAGProgrammingSolutions
path: queries/NanoCodeRAGProgrammingSolutions-00000-of-00001.parquet
- split: NanoCodeRAGStackoverflowPosts
path: queries/NanoCodeRAGStackoverflowPosts-00000-of-00001.parquet
default: true
- config_name: qrels
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: qrels/NanoCodeRAGLibraryDocumentationSolutions-00000-of-00001.parquet
- split: NanoCodeRAGOnlineTutorials
path: qrels/NanoCodeRAGOnlineTutorials-00000-of-00001.parquet
- split: NanoCodeRAGProgrammingSolutions
path: qrels/NanoCodeRAGProgrammingSolutions-00000-of-00001.parquet
- split: NanoCodeRAGStackoverflowPosts
path: qrels/NanoCodeRAGStackoverflowPosts-00000-of-00001.parquet
- config_name: bm25
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: bm25/NanoCodeRAGLibraryDocumentationSolutions-00000-of-00001.parquet
- split: NanoCodeRAGOnlineTutorials
path: bm25/NanoCodeRAGOnlineTutorials-00000-of-00001.parquet
- split: NanoCodeRAGProgrammingSolutions
path: bm25/NanoCodeRAGProgrammingSolutions-00000-of-00001.parquet
- split: NanoCodeRAGStackoverflowPosts
path: bm25/NanoCodeRAGStackoverflowPosts-00000-of-00001.parquet
- config_name: harrier_oss_v1_270m
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: harrier_oss_v1_270m/NanoCodeRAGLibraryDocumentationSolutions-00000-of-00001.parquet
- split: NanoCodeRAGOnlineTutorials
path: harrier_oss_v1_270m/NanoCodeRAGOnlineTutorials-00000-of-00001.parquet
- split: NanoCodeRAGProgrammingSolutions
path: harrier_oss_v1_270m/NanoCodeRAGProgrammingSolutions-00000-of-00001.parquet
- split: NanoCodeRAGStackoverflowPosts
path: harrier_oss_v1_270m/NanoCodeRAGStackoverflowPosts-00000-of-00001.parquet
- config_name: reranking_hybrid
data_files:
- split: NanoCodeRAGLibraryDocumentationSolutions
path: reranking_hybrid/NanoCodeRAGLibraryDocumentationSolutions-00000-of-00001.parquet
- split: NanoCodeRAGOnlineTutorials
path: reranking_hybrid/NanoCodeRAGOnlineTutorials-00000-of-00001.parquet
- split: NanoCodeRAGProgrammingSolutions
path: reranking_hybrid/NanoCodeRAGProgrammingSolutions-00000-of-00001.parquet
- split: NanoCodeRAGStackoverflowPosts
path: reranking_hybrid/NanoCodeRAGStackoverflowPosts-00000-of-00001.parquet
language:
- code
tags:
- information-retrieval
- retrieval
- nano
- bm25
- dense-retrieval
- reranking
- hakari-bench
dataset_info:
- config_name: bm25
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
num_bytes: 1180757
num_examples: 200
- name: NanoCodeRAGOnlineTutorials
num_bytes: 1191012
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
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
num_bytes: 17956921
num_examples: 8683
- name: NanoCodeRAGOnlineTutorials
num_bytes: 57578239
num_examples: 9997
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 200886
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
num_bytes: 1179425
num_examples: 200
- name: NanoCodeRAGOnlineTutorials
num_bytes: 1190640
num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 1112088
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
num_bytes: 1189868
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
num_bytes: 3384
num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 3547
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
num_bytes: 3380
num_examples: 200
download_size: 13196
dataset_size: 13718
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
num_bytes: 81590
num_examples: 200
- name: NanoCodeRAGOnlineTutorials
num_bytes: 12499
num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 17812
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
num_bytes: 44059
num_examples: 200
download_size: 81432
dataset_size: 155960
- config_name: reranking_hybrid
features:
- name: query-id
dtype: string
- name: corpus-ids
list: string
splits:
- name: NanoCodeRAGLibraryDocumentationSolutions
num_bytes: 236212
num_examples: 200
- name: NanoCodeRAGOnlineTutorials
num_bytes: 239489
num_examples: 200
- name: NanoCodeRAGProgrammingSolutions
num_bytes: 219840
num_examples: 200
- name: NanoCodeRAGStackoverflowPosts
num_bytes: 239067
num_examples: 200
download_size: 940041
dataset_size: 934608
---
# NanoCodeRAG
This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench).
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.
## Usage
```python
from datasets import load_dataset
dataset_id = "hakari-bench/NanoCodeRAG"
split = "NanoCodeRAGLibraryDocumentationSolutions"
queries = load_dataset(dataset_id, "queries", split=split)
corpus = load_dataset(dataset_id, "corpus", split=split)
qrels = load_dataset(dataset_id, "qrels", split=split)
reranking_candidates = load_dataset(dataset_id, "reranking_hybrid", split=split)
```
## Data Layout
This dataset uses six 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`
- `harrier_oss_v1_270m`: dense candidate lists from `microsoft/harrier-oss-v1-270m`
- `reranking_hybrid`: RRF candidate lists built from `bm25` and `harrier_oss_v1_270m`
Each config has the same Nano split names.
## Candidate Construction
- `bm25`: local BM25 top-500 with automatic language-aware tokenization. The resolved tokenizer is shown in the Candidate Quality table, for example `wordseg@ja`.
- `harrier_oss_v1_270m`: dense top-500 from `microsoft/harrier-oss-v1-270m`. In tables this is shown as `Dense`; Dense means `microsoft/harrier-oss-v1-270m` with the `web_search_query` prompt for queries and cosine similarity over normalized embeddings.
- `reranking_hybrid`: RRF over `bm25` and `harrier_oss_v1_270m` using `rrf_k=100`, keeping the RRF top-100.
Safeguard means rank 101 is appended only when RRF top-100 contains no qrels-positive document.
## Split Statistics
Length statistics are character counts computed with `len(str(text))`.
| Nano split | Queries | Corpus | Qrels | Query chars avg | Query chars p50 | Query chars p75 | Doc chars avg | Doc chars p50 | Doc chars p75 |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| NanoCodeRAGLibraryDocumentationSolutions | 200 | 8683 | 200 | 397.4 | 110.0 | 127.2 | 2045.7 | 765.0 | 1842.5 |
| NanoCodeRAGOnlineTutorials | 200 | 9997 | 200 | 51.9 | 47.0 | 63.0 | 5722.5 | 3419.0 | 6795.0 |
| NanoCodeRAGProgrammingSolutions | 200 | 984 | 200 | 78.3 | 78.0 | 89.2 | 189.1 | 148.5 | 227.2 |
| NanoCodeRAGStackoverflowPosts | 200 | 10000 | 200 | 209.8 | 185.5 | 269.0 | 4735.0 | 2864.5 | 5301.0 |
## Candidate Quality
`nDCG@10` and `Recall@100` are computed from the included candidate rankings against the included qrels, then reported as 0-100 scores such as `52.45`. `Recall@100` uses only the top 100 candidates; an optional rank-101 safeguard positive is not counted in `Recall@100`.
Dense means `microsoft/harrier-oss-v1-270m` with the `web_search_query` prompt and cosine similarity.
| Nano split | BM25 tokenizer | BM25 nDCG@10 | Dense nDCG@10 | Hybrid nDCG@10 | BM25 Recall@100 | Dense Recall@100 | Hybrid Recall@100 | Hybrid candidates | Safeguard positives |
|---|---|---:|---:|---:|---:|---:|---:|---:|---:|
| Mean | - | 58.23 | 82.96 | 66.85 | 80.50 | 95.12 | 97.75 | - | 18 |
| NanoCodeRAGLibraryDocumentationSolutions | regex | 68.67 | 76.45 | 75.44 | 92.00 | 92.50 | 94.50 | 100-101 | 11 |
| NanoCodeRAGOnlineTutorials | regex | 81.75 | 90.27 | 86.73 | 97.00 | 95.50 | 100.00 | 100 | 0 |
| NanoCodeRAGProgrammingSolutions | regex | 5.12 | 76.46 | 21.51 | 36.50 | 96.50 | 96.50 | 100-101 | 7 |
| NanoCodeRAGStackoverflowPosts | regex | 77.37 | 88.65 | 83.73 | 96.50 | 96.00 | 100.00 | 100 | 0 |
## Hybrid Safeguard Summary
- Safeguard positives: 18
- Rows limited by corpus size: 0
- Metadata file: `reranking_hybrid_metadata.json`
## 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
## 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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