README.md

metadata

pretty_name: Training · Magicoder OSS-Instruct
license: mit
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

Magicoder OSS-Instruct — Training, unified schema

A seeded sample of ise-uiuc/Magicoder-OSS-Instruct-75K, 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	ise-uiuc/Magicoder-OSS-Instruct-75K @ 5f839b1f368a
Task	coding problem → solution
Domain · languages	code · eng
Queries / documents / qrels	59,997 / 59,999 / 59,997
Qrels per query	min 1 · mean 1.0 · max 1
Score values	2 ×59,997 (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: dense · 5,939,734 rows
Teacher scores	jinaai/jina-reranker-v3.5 · 5,999,731 rows (positives included)
Ids	sha1(text)[:20]; identical texts collapse to one document / query
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
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
• reshaped: the coding problem (problem) is the query, its solution (solution) the document
• 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): 1 passages that nearly copy an evaluation document some evaluation query judges relevant, and 2 queries that nearly copy an evaluation query (word 13-grams for passages, 8-grams for queries; at least half shared with one text of the 23 test sets (BEIR, RTEB, LitSearch) or the 6 dev sets) were removed, and with them 3 queries in total; near copies of evaluation-corpus documents that no evaluation query judges relevant were kept
• 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 annotation pipeline (annotation=jina35-u2) for the train split of the query set(s) below; queries without a labelled positive are left out.

• Candidates: dense retrieval with jinaai/jina-embeddings-v5-text-small over the full corpus to depth 1,000; 100 candidates per query drawn from the rank windows 1–30 (30), 31–100 (30), 101–300 (20), 301–1000 (20), the query's labelled positives excluded. rank is the dense rank; source is dense for a mined row and dataset for a negative the source labels itself (those are kept for every query of the split, sampled or not).
• Teacher: jinaai/jina-reranker-v3.5, listwise: a query's positive and all of its candidates are scored together in one context of up to 32,768 tokens. score is the raw cosine score, one row per (query, positive) and per (query, candidate); a labelled negative that was also mined is scored once. Every document was cut to its first 1,024 reranker tokens before scoring (max_doc_tokens=1024). No filtering is applied to the tables.

configs	queries	hard negatives	teacher scores
hard-negatives · teacher-scores	59,997 (all)	5,939,734 (5,939,734 dense)	5,999,731

from datasets import load_dataset
negatives = load_dataset("Hyukkyu/train-magicoder", "hard-negatives", split="train")
scores    = load_dataset("Hyukkyu/train-magicoder", "teacher-scores", split="train")

Load it

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

License and attribution

The data is redistributed under the source's terms — mit. All credit belongs to the original authors; see the source repository (https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K). This repository is an independent repackaging.