Datasets:

hotchpotch
/

cornstack-samples

dataset_info:
  - config_name: go-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_examples: 87647
  - config_name: go-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_examples: 1541111
  - config_name: java-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_examples: 81657
  - config_name: java-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_examples: 1491655
  - config_name: javascript-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_examples: 79684
  - config_name: javascript-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_examples: 1310965
  - config_name: php-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_examples: 75632
  - config_name: php-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_examples: 1343442
  - config_name: python-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_examples: 97147
  - config_name: python-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_examples: 1807480
  - config_name: ruby-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_examples: 68382
  - config_name: ruby-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_examples: 1175219
configs:
  - config_name: go-v1-hard-negatives-100k
    data_files:
      - split: train
        path: go-v1-hard-negatives-100k/train-*
  - config_name: go-v1-pair-2M
    data_files:
      - split: train
        path: go-v1-pair-2M/train-*
  - config_name: java-v1-hard-negatives-100k
    data_files:
      - split: train
        path: java-v1-hard-negatives-100k/train-*
  - config_name: java-v1-pair-2M
    data_files:
      - split: train
        path: java-v1-pair-2M/train-*
  - config_name: javascript-v1-hard-negatives-100k
    data_files:
      - split: train
        path: javascript-v1-hard-negatives-100k/train-*
  - config_name: javascript-v1-pair-2M
    data_files:
      - split: train
        path: javascript-v1-pair-2M/train-*
  - config_name: php-v1-hard-negatives-100k
    data_files:
      - split: train
        path: php-v1-hard-negatives-100k/train-*
  - config_name: php-v1-pair-2M
    data_files:
      - split: train
        path: php-v1-pair-2M/train-*
  - config_name: python-v1-hard-negatives-100k
    data_files:
      - split: train
        path: python-v1-hard-negatives-100k/train-*
  - config_name: python-v1-pair-2M
    data_files:
      - split: train
        path: python-v1-pair-2M/train-*
  - config_name: ruby-v1-hard-negatives-100k
    data_files:
      - split: train
        path: ruby-v1-hard-negatives-100k/train-*
  - config_name: ruby-v1-pair-2M
    data_files:
      - split: train
        path: ruby-v1-pair-2M/train-*
license: apache-2.0

cornstack-samples

Filtered CoRNStack sample subsets for code retrieval training.

Source dataset and paper:

CoRNStack collection: https://huggingface.co/collections/nomic-ai/cornstack
CoRNStack paper: https://huggingface.co/papers/2412.01007

What This Release Contains

This release keeps the original subset layout (6 languages x pair + hard-negatives) and applies deterministic rule-based filtering.

In this revision, query-level deduplication is also applied per subset: if query is duplicated, only the first row is kept.

Config Layout And Schema

Each language is published as two configs with split train:

{lang}-v1-pair-2M
{lang}-v1-hard-negatives-100k

Schema:

Pair configs: query, pos
Hard-negative configs: query, pos, negs (list[string])

Subsets And Row Counts (After Filter + Query Dedup)

Subset (config name)	split	num_examples
`go-v1-pair-2M`	`train`	1,541,111
`go-v1-hard-negatives-100k`	`train`	87,647
`java-v1-pair-2M`	`train`	1,491,655
`java-v1-hard-negatives-100k`	`train`	81,657
`javascript-v1-pair-2M`	`train`	1,310,965
`javascript-v1-hard-negatives-100k`	`train`	79,684
`php-v1-pair-2M`	`train`	1,343,442
`php-v1-hard-negatives-100k`	`train`	75,632
`python-v1-pair-2M`	`train`	1,807,480
`python-v1-hard-negatives-100k`	`train`	97,147
`ruby-v1-pair-2M`	`train`	1,175,219
`ruby-v1-hard-negatives-100k`	`train`	68,382

Total rows:

Pair: 8,669,872
Hard-negatives: 490,149
Overall: 9,160,021

Filter Impact (Query Dedup Stage)

The table below shows only the query-dedup impact on top of the previous rule-based filter.

Subset	before	after	removed	removed_ratio
`go-v1-pair-2M`	1,992,985	1,541,111	451,874	22.67%
`go-v1-hard-negatives-100k`	99,663	87,647	12,016	12.06%
`java-v1-pair-2M`	1,752,593	1,491,655	260,938	14.89%
`java-v1-hard-negatives-100k`	87,504	81,657	5,847	6.68%
`javascript-v1-pair-2M`	1,960,276	1,310,965	649,311	33.12%
`javascript-v1-hard-negatives-100k`	98,025	79,684	18,341	18.71%
`php-v1-pair-2M`	1,710,537	1,343,442	367,095	21.46%
`php-v1-hard-negatives-100k`	85,460	75,632	9,828	11.50%
`python-v1-pair-2M`	1,990,051	1,807,480	182,571	9.17%
`python-v1-hard-negatives-100k`	99,535	97,147	2,388	2.40%
`ruby-v1-pair-2M`	1,583,047	1,175,219	407,828	25.76%
`ruby-v1-hard-negatives-100k`	79,040	68,382	10,658	13.48%

Quick Usage

from datasets import load_dataset

pair_ds = load_dataset("hotchpotch/cornstack-samples", "python-v1-pair-2M", split="train")
hard_ds = load_dataset("hotchpotch/cornstack-samples", "python-v1-hard-negatives-100k", split="train")

print(pair_ds.column_names, len(pair_ds))
print(hard_ds.column_names, len(hard_ds))

License

This dataset follows CoRNStack and is released under Apache-2.0.

Citation And Attribution

If you use this dataset, please cite and attribute CoRNStack:

Paper: https://huggingface.co/papers/2412.01007
Collection: https://huggingface.co/collections/nomic-ai/cornstack

Noise Filtering Algorithm (Rule-based)

The following deterministic rules are applied before publishing this release.

Prefix-based noisy query removal A row is dropped if query starts with any of the following prefixes:

TODO
GET /
POST /
PUT /
DELETE /
Display a listing of the resource.
Store a newly created resource in storage.
Show the form for editing the specified resource.
Update the specified resource in storage.
Show the form for creating a new resource.
Remove the specified resource from storage.
Display the specified resource.
Transform the resource into an array.
Autogenerated method stub
Auto generated
this down() migration is autogenerated
this up() migration is autogenerated
"/ renamed from:"
"/ access modifiers changed from:"

Minimum positive-document length A row is dropped if the positive side text is shorter than 30 characters.

Pair configs: pos length >= 30 required
Hard-negative configs: pos length >= 30 required

Hard-negative validity constraint For hard-negative configs, at least one valid negative must remain after normalization (min_negs = 1).
Query-level deduplication Within each subset split, rows are grouped by exact query string.

Keep the first occurrence
Drop all later duplicates

This filtering is purely rule-based (no model scoring), targeting high-noise templates and low-information positives while preserving broad retrieval coverage.