Datasets:

Hyukkyu
/

train-cornstack-python

pretty_name: Training · CoRNStack Python
license: apache-2.0
language:
  - en
multilinguality:
  - monolingual
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
tags:
  - train
  - retrieval
  - code
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/train-*.parquet
  - config_name: hard-negatives
    data_files:
      - split: train
        path: hard-negatives/train-*.parquet
  - config_name: qrels
    data_files:
      - split: train
        path: qrels/train-*.parquet
  - config_name: queries
    data_files:
      - split: train
        path: queries/train-*.parquet
  - config_name: teacher-scores
    data_files:
      - split: train
        path: teacher-scores/train-*.parquet

CoRNStack Python — Training, unified schema

A seeded sample of nomic-ai/cornstack-python-v1, made into retrieval training pairs and reshaped into the strict schema shared by every dataset in this collection. One of the 15 domain sources (code, medical, science, finance, legal) added to the collection's general sources.


Source	`nomic-ai/cornstack-python-v1` @ `25fb04bd3537`
Task	query → Python function
Domain · languages	code · eng
Queries / documents / qrels	59,983 / 712,475 / 59,983
Qrels per query	min 1 · mean 1.0 · max 1
Score values	2 ×59,983 (2: the first positive, 1: any other)
Layout	`queries` · `corpus` · `qrels` · `hard-negatives` · `teacher-scores`, split `train`
Splits	`corpus`: train · `hard-negatives`: train · `qrels`: train · `queries`: train · `teacher-scores`: train
Hard negatives	sources: `dataset`, `dense` · 4,006,722 rows
Teacher scores	`jinaai/jina-embeddings-v5-text-small` (the miner's cosine, not a reranker) · 4,066,705 rows (positives included)
Ids	`sha1(text)[:20]`; identical texts collapse to one document / query
License	`apache-2.0`

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
`hard-negatives`	`query-id: string`, `corpus-id: string`, `rank: int32`, `source: string`	one row per negative; `(query-id, corpus-id, source)` unique; never a labelled positive of the same query
`teacher-scores`	`query-id: string`, `corpus-id: string`, `teacher: string`, `score: float32`	one row per scored pair (positives included); a row means scored — never a placeholder

Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is checked before publishing; provenance.json records the source revision, what changed, and the output file hashes.

What changed from the source

sampled: a seeded random sample (seed 1) of up to 60,000 pairs, streamed through a shuffle buffer of 50,000
reshaped: the natural-language query (query) is the query, the function (document) the document
negatives the source provides: up to 15 of the row's own mined negatives (negatives) (hard-negatives source = dataset)
decontaminated (exact): a pair was dropped when its normalised query equals any evaluation query, or a positive equals a document of a test or dev corpus; a repeated query keeps its first pair
decontaminated (near-duplicates): 14 passages and 3 queries that nearly copy a text of an evaluation set (word 13-grams for passages, 8-grams for queries; at least half shared with one text of the 23 test corpora (BEIR, RTEB, LitSearch) and the 3 dev corpora) were removed, and with them 17 queries in total
text: leading and trailing whitespace stripped; otherwise as converted above
ids re-keyed to sha1(text)[:20]: 0 documents and 0 queries collapsed into identical texts
added a title column filled with "" (the source has none)

Hard negatives and teacher scores

Filled by the owner's domain pipeline (convert_domain.py, mine_domain.py), not yet by the annotation pipeline of the other repositories in this collection:

Corpus: the source's own passages (its positives and the negatives it provides), not a larger collection.
Candidates: dense retrieval with jinaai/jina-embeddings-v5-text-small (full length) over that corpus to depth 300; 60 candidates per query drawn from the rank windows 1–30 (30), 31–100 (20), 101–300 (10), the query's positives excluded. rank is the dense rank; source is dense for a mined row and dataset for a negative the source labels itself (ranked after the mined ones).
Teacher scores: the miner's cosine between the query and passage embeddings, one row per (query, positive) and per (query, candidate), teacher = jinaai/jina-embeddings-v5-text-small. Not a reranker: the other repositories carry jinaai/jina-reranker-v3.5 scores. Re-mining with the collection's plan (100 candidates to depth 1,000) and re-scoring with that reranker are under way; they will replace these two tables, and this section will say so.

configs	queries	hard negatives	teacher scores
`hard-negatives` · `teacher-scores`	59,983 (all)	4,006,722 (452,048 dataset, 3,554,674 dense)	4,066,705

Load it

from datasets import load_dataset
queries   = load_dataset("Hyukkyu/train-cornstack-python", "queries", split="train")
corpus    = load_dataset("Hyukkyu/train-cornstack-python", "corpus", split="train")
qrels     = load_dataset("Hyukkyu/train-cornstack-python", "qrels", split="train")
negatives = load_dataset("Hyukkyu/train-cornstack-python", "hard-negatives", split="train")
scores    = load_dataset("Hyukkyu/train-cornstack-python", "teacher-scores", split="train")

License and attribution

The data is redistributed under the source's terms — apache-2.0. All credit belongs to the original authors; see the source repository (https://huggingface.co/datasets/nomic-ai/cornstack-python-v1). This repository is an independent repackaging.