build HumanEvalRetrieval from embedding-benchmark/HumanEval@ed1f48ac · rteb_open v0.1.0

README.md

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+---
+pretty_name: RTEB · HumanEvalRetrieval
+license: mit
+language:
+- en
+multilinguality:
+- monolingual
+task_categories:
+- text-retrieval
+task_ids:
+- document-retrieval
+tags:
+- rteb
+- mteb
+- retrieval
+- code
+configs:
+- config_name: corpus
+  data_files:
+  - split: test
+    path: corpus/test-*.parquet
+- config_name: qrels
+  data_files:
+  - split: test
+    path: qrels/test-*.parquet
+- config_name: queries
+  data_files:
+  - split: test
+    path: queries/test-*.parquet
+---
+# HumanEvalRetrieval — RTEB open subset, unified schema
+
+A normalised copy of the dataset behind the `mteb` task **`HumanEvalRetrieval`**, one of the 17 open tasks in the
+[RTEB(beta)](https://huggingface.co/blog/rteb) retrieval benchmark. Same queries, documents and relevance
+judgements as the benchmark evaluates — reshaped into one strict schema shared by all 17.
+
+|  |  |
+|---|---|
+| Source | [`embedding-benchmark/HumanEval`](https://huggingface.co/datasets/embedding-benchmark/HumanEval/tree/ed1f48aca747f10bac146795328e2f03326e7625) @ `ed1f48aca747` (the revision pinned in `mteb`) |
+| Domain · languages | code · eng |
+| Queries / documents / qrels | 158 / 158 / 158 |
+| Qrels per query | min 1 · mean 1.0 · max 1 |
+| Score values | 1 ×158 |
+| Layout | `queries` · `corpus` · `qrels`, split `test` |
+| License | `mit` |
+
+## Schema
+
+| config | columns | rules |
+|---|---|---|
+| `queries` | `id: string`, `text: string` | ids unique and non-empty; every query has ≥ 1 qrel |
+| `corpus` | `id: string`, `title: string`, `text: string` | `title` is always present (`""` when the source has none) |
+| `qrels` | `query-id: string`, `corpus-id: string`, `score: int32` | referential integrity to both tables; no duplicate pairs; no floats |
+
+Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is enforced by a validator
+before publishing; `provenance.json` records the source file hashes, what changed, and the output file hashes.
+
+## What changed from the source
+
+- **byte-preserved** all text — no whitespace or newline normalisation (code dataset)
+- added a `title` column filled with `""` (the source has none)
+- cast `qrels.score` double -> int32
+- renamed source splits (`queries`←`queries/queries`, `corpus`←`corpus/corpus`, `qrels`←`default/test`) to `test`
+
+## Load it
+
+```python
+from datasets import load_dataset
+queries = load_dataset("Hyukkyu/rteb-HumanEvalRetrieval", "queries", split="test")
+corpus  = load_dataset("Hyukkyu/rteb-HumanEvalRetrieval", "corpus", split="test")
+qrels   = load_dataset("Hyukkyu/rteb-HumanEvalRetrieval", "qrels", split="test")
+```
+
+## License and attribution
+
+The data is redistributed under the source's terms — `mit`. All credit belongs to the
+original authors; see the source repository and the references in `mteb`'s task metadata (https://huggingface.co/datasets/embedding-benchmark/HumanEval).
+This repository is an independent repackaging and is not affiliated with the RTEB or MTEB maintainers.

corpus/test-00000-of-00001.parquet

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+size 12649

provenance.json

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+{
+ "task": "HumanEvalRetrieval",
+ "repo": "rteb-HumanEvalRetrieval",
+ "rteb_open_version": "0.1.0",
+ "source": {
+  "path": "embedding-benchmark/HumanEval",
+  "revision": "ed1f48aca747f10bac146795328e2f03326e7625",
+  "url": "https://huggingface.co/datasets/embedding-benchmark/HumanEval/tree/ed1f48aca747f10bac146795328e2f03326e7625"
+ },
+ "license": {
+  "card": "mit",
+  "mteb_task_metadata": "mit",
+  "upstream_checked": false
+ },
+ "languages": [
+  "eng"
+ ],
+ "domain": "code",
+ "byte_preserving_text": true,
+ "layout": {
+  "subsets": null,
+  "splits": [
+   "test"
+  ]
+ },
+ "parts": [
+  {
+   "subset": null,
+   "split": "test",
+   "source_configs": {
+    "queries": "queries/queries",
+    "corpus": "corpus/corpus",
+    "qrels": "default/test"
+   },
+   "normalize": {
+    "dropped_columns": {},
+    "orphan_queries_dropped": 0,
+    "duplicate_qrels_collapsed": 0,
+    "null_titles_filled": 0,
+    "score_cast": "double -> int32",
+    "id_renamed": false,
+    "title_added": true
+   },
+   "stats": {
+    "n_queries": 158,
+    "n_corpus": 158,
+    "n_qrels": 158,
+    "qrels_per_query": {
+     "min": 1,
+     "mean": 1.0,
+     "max": 1
+    },
+    "score_values": {
+     "1": 158
+    },
+    "dangling_qrels": 0,
+    "corpus_with_title": 0,
+    "avg_query_chars": 291.3,
+    "avg_doc_chars": 182.3
+   }
+  }
+ ],
+ "files": {
+  "queries": {
+   "test": [
+    {
+     "path": "queries/test-00000-of-00001.parquet",
+     "rows": 158,
+     "bytes": 19365,
+     "sha256": "87a6fe9839cd2812001bf9bec766bf4aae8ead2eb5aeffcd0c8ea7e9262760f3"
+    }
+   ]
+  },
+  "corpus": {
+   "test": [
+    {
+     "path": "corpus/test-00000-of-00001.parquet",
+     "rows": 158,
+     "bytes": 12649,
+     "sha256": "4a93f3e3111d48ee030d5aa097c0586cb53709528340525c03fba56b005e8483"
+    }
+   ]
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+  "qrels": {
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+    {
+     "path": "qrels/test-00000-of-00001.parquet",
+     "rows": 158,
+     "bytes": 1593,
+     "sha256": "c246ec9d0fb8094f22b6ba31299bc1678dfcf817e8f36aeed673c728282ccf65"
+    }
+   ]
+  }
+ },
+ "stats_total": {
+  "n_queries": 158,
+  "n_corpus": 158,
+  "n_qrels": 158
+ },
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+  },
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+   "sha256": null,
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+   "sha256": null,
+   "blob_id": "22f4530dea522b850703d1d82db7338bbd379c98"
+  }
+ ],
+ "build": {
+  "timestamp_utc": "2026-09-08T02:32:08Z",
+  "seconds": 7.1,
+  "python": "3.11.0",
+  "pyarrow": "25.0.1",
+  "datasets": "5.0.1",
+  "polars": "1.44.1"
+ }
+}

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