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_bytes: 364025569
        num_examples: 99663
    download_size: 163830524
    dataset_size: 364025569
  - config_name: go-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_bytes: 1066841599
        num_examples: 1992985
    download_size: 438588731
    dataset_size: 1066841599
  - config_name: java-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_bytes: 359305298
        num_examples: 87504
    download_size: 130012787
    dataset_size: 359305298
  - config_name: java-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_bytes: 1331514393
        num_examples: 1752593
    download_size: 480145623
    dataset_size: 1331514393
  - config_name: javascript-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_bytes: 505829400
        num_examples: 98025
    download_size: 212768692
    dataset_size: 505829400
  - config_name: javascript-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_bytes: 1625855840
        num_examples: 1960276
    download_size: 704045988
    dataset_size: 1625855840
  - config_name: php-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_bytes: 409600818
        num_examples: 85460
    download_size: 163986468
    dataset_size: 409600818
  - config_name: php-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_bytes: 1379318420
        num_examples: 1710537
    download_size: 522306657
    dataset_size: 1379318420
  - config_name: python-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_bytes: 426330409
        num_examples: 99535
    download_size: 184706464
    dataset_size: 426330409
  - config_name: python-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_bytes: 1460994357
        num_examples: 1990051
    download_size: 644195658
    dataset_size: 1460994357
  - config_name: ruby-v1-hard-negatives-100k
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
      - name: negs
        list: string
    splits:
      - name: train
        num_bytes: 180626329
        num_examples: 79040
    download_size: 83095960
    dataset_size: 180626329
  - config_name: ruby-v1-pair-2M
    features:
      - name: query
        dtype: string
      - name: pos
        dtype: string
    splits:
      - name: train
        num_bytes: 713529581
        num_examples: 1583047
    download_size: 304652631
    dataset_size: 713529581
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 a deterministic rule-based noise filter.

Important:

Counts are post-filter counts, so they are slightly smaller than the nominal 2M / 100k targets.
Data is published in normalized IR format.

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 (Post-filter)

Subset (config name)	split	num_examples
`go-v1-pair-2M`	`train`	1,992,985
`go-v1-hard-negatives-100k`	`train`	99,663
`java-v1-pair-2M`	`train`	1,752,593
`java-v1-hard-negatives-100k`	`train`	87,504
`javascript-v1-pair-2M`	`train`	1,960,276
`javascript-v1-hard-negatives-100k`	`train`	98,025
`php-v1-pair-2M`	`train`	1,710,537
`php-v1-hard-negatives-100k`	`train`	85,460
`python-v1-pair-2M`	`train`	1,990,051
`python-v1-hard-negatives-100k`	`train`	99,535
`ruby-v1-pair-2M`	`train`	1,583,047
`ruby-v1-hard-negatives-100k`	`train`	79,040

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 is shorter than 30 characters.

Pair task: document length >= 30 required
Hard-negatives task: positive length >= 30 required

Hard-negative validity constraint For hard-negative configs, at least one valid negative must remain after normalization (min_negs = 1).

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