--- 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.