README.md

---
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`](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K/tree/5f839b1f368a76b161028bb9edff055db34022b2), 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`](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K/tree/5f839b1f368a76b161028bb9edff055db34022b2) @ `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 |

```python
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

```python
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.