Fix dataset viewer (configs block) + correct row count + add example row (#1290)

README.md

@@ -1,4 +1,9 @@
 ---
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: cqs-code-search-200k.jsonl
 dataset_info:
   features:
     - name: query
@@ -23,7 +28,7 @@ dataset_info:
         dtype: string
   splits:
     - name: train
-      num_examples: 200000
+      num_examples: 199998
 license: mit
 task_categories:
   - text-retrieval
@@ -43,7 +48,7 @@ Balanced code search training dataset with call graph metadata.
 
 ## Overview
 
-200,000 (query, code) pairs across 9 programming languages, extracted from ~5,000 high-quality GitHub repositories using [cqs](https://github.com/jamie8johnson/cqs) semantic code indexing.
+199,998 (query, code) pairs across 9 programming languages, extracted from ~5,000 high-quality GitHub repositories using [cqs](https://github.com/jamie8johnson/cqs) semantic code indexing.
 
 **Unique features:**
 - **Perfectly balanced**: 22,222 pairs per language
@@ -53,27 +58,56 @@ Balanced code search training dataset with call graph metadata.
 
 ## Intended Use
 
-Training code search embedding models (e.g., E5-base-v2 LoRA fine-tuning). The call graph metadata enables:
+Training code search embedding models. This dataset produced both [`e5-base-v2-code-search`](https://huggingface.co/jamie8johnson/e5-base-v2-code-search) and [`bge-large-v1.5-code-search`](https://huggingface.co/jamie8johnson/bge-large-v1.5-code-search). The call graph metadata enables:
+
 - **False-negative filtering**: skip hard negatives that are callers/callees of the positive
 - **Structural training signals**: teach embeddings that callers are semantically related
 
 ## Dataset Structure
 
 Each record contains:
-- `query`: Natural language description (from cqs NL generation)
-- `positive`: Source code (truncated to 2000 chars)
-- `language`: Programming language
-- `function_name`: Function/method name
-- `file`: Source file path
-- `repo`: GitHub repository
-- `callers`: List of functions that call this function (up to 20)
-- `callees`: List of functions this function calls (up to 20)
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `query` | string | Natural-language description (cqs NL generation) |
+| `positive` | string | Source code (truncated to 2000 chars) |
+| `language` | string | Programming language |
+| `function_name` | string | Function/method name |
+| `file` | string | Source file path |
+| `repo` | string | GitHub repository (`owner/name`) |
+| `source` | string | Provenance tag (e.g. `stack-cqs-index`) |
+| `callers` | list[string] | Functions that call this one (up to 20) |
+| `callees` | list[string] | Functions this one calls (up to 20) |
+
+### Example row
+
+```json
+{
+  "query": "query: Test helper function to filter a collection of compilation database entries fn filter_entries<I>(filter: &SourceEntryFilter, entries: I) -> Vec<Entry> ...",
+  "positive": "passage: fn filter_entries<I>(filter: &SourceEntryFilter, entries: I) -> Vec<Entry>\n    where I: IntoIterator<Item = Entry>,\n{\n    entries.into_iter().filter(|entry| filter.should_include(entry)).collect()\n}",
+  "language": "rust",
+  "function_name": "filter_entries",
+  "file": "bear/src/output/clang/filter_sources.rs",
+  "repo": "rizsotto/Bear",
+  "source": "stack-cqs-index",
+  "callers": ["get_entries", "read_directories", "run", "test_filter_entries_method"],
+  "callees": ["filter", "into_iter", "should_include", "collect"]
+}
+```
+
+## Files
+
+- `cqs-code-search-200k.jsonl` — the dataset (199,998 rows, ~229 MB).
+- `processing_manifest.jsonl` — per-repo extraction provenance (sidecar, not part of the dataset). One row per source repo with `{repo, language, status, pairs, edges, source_files, note}`.
 
 ## Creation
 
 Extracted using [cqs](https://github.com/jamie8johnson/cqs) v1.7.0:
-1. Clone repos from GitHub (filtered by stars, size)
-2. `cqs index` each repo (tree-sitter parsing, call graph extraction, NL generation)
-3. Extract (NL, code) pairs from the cqs index
-4. Balance to 22,222 per language
-5. Attach call graph edges from the same index
+
+1. Clone repos from GitHub (filtered by stars, size).
+2. `cqs index` each repo (tree-sitter parsing, call graph extraction, NL query generation).
+3. Extract (NL, code) pairs from the cqs index.
+4. Balance to 22,222 per language.
+5. Attach call graph edges from the same index.
+
+The dataset reflects the v1.7.0-era extraction pipeline. cqs has matured significantly since (current release: v1.33.0) — newer extractions would use higher-quality NL generation and richer metadata, but the underlying (query, code) pairs remain useful as-is for training general-purpose code-search embeddings.