README.md

---
language:
- en
license: cc-by-4.0
task_categories:
- text-generation
pretty_name: SWE-rebench-V2-Filtered-Easy-Verified
tags:
- software-engineering
- code
- swe
- rl
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: base_commit
    dtype: string
  - name: created_at
    dtype: string
  - name: image_name
    dtype: string
  - name: instance_id
    dtype: string
  - name: interface
    dtype: string
  - name: language
    dtype: string
  - name: license
    dtype: string
  - name: patch
    dtype: string
  - name: pr_description
    dtype: string
  - name: problem_statement
    dtype: string
  - name: repo
    dtype: string
  - name: test_patch
    dtype: string
  - name: FAIL_TO_PASS
    list: string
  - name: PASS_TO_PASS
    list: string
  - name: install_config
    struct:
    - name: base_image_name
      dtype: string
    - name: docker_specs
      struct:
      - name: _variant
        dtype: string
      - name: bazel_version
        dtype: string
      - name: bun_version
        dtype: string
      - name: cargo_version
        dtype: string
      - name: deno_version
        dtype: string
      - name: docker_version
        dtype: string
      - name: erlang_version
        dtype: string
      - name: gcc_version
        dtype: string
      - name: go_version
        dtype: string
      - name: helm_version
        dtype: string
      - name: java_version
        dtype: string
      - name: jdk_version
        dtype: string
      - name: llvm_version
        dtype: string
      - name: lua_version
        dtype: string
      - name: luajit_version
        dtype: string
      - name: neovim_version
        dtype: string
      - name: node_version
        dtype: string
      - name: npm_version
        dtype: string
      - name: nvim_version
        dtype: string
      - name: pnpm_version
        dtype: string
      - name: python_image
        dtype: string
      - name: python_version
        dtype: string
      - name: redis_version
        dtype: string
      - name: ruby_version
        dtype: string
      - name: rust_version
        dtype: string
      - name: rustc_version
        dtype: string
      - name: solana_version
        dtype: string
      - name: sqlite_version
        dtype: string
    - name: install
      list: string
    - name: log_parser
      dtype: string
    - name: test_cmd
      dtype: string
  - name: meta
    struct:
    - name: llm_metadata
      struct:
      - name: code
        dtype: string
      - name: confidence
        dtype: float64
      - name: detected_issues
        struct:
        - name: B1
          dtype: bool
        - name: B2
          dtype: bool
        - name: B3
          dtype: bool
        - name: B4
          dtype: bool
        - name: B5
          dtype: bool
        - name: B6
          dtype: bool
      - name: difficulty
        dtype: string
      - name: external_urls
        list: string
      - name: intent_completeness
        dtype: string
      - name: pr_categories
        list: string
      - name: reasoning
        dtype: string
      - name: test_alignment_issues
        list: string
    - name: num_modified_files
      dtype: int64
    - name: num_modified_lines
      dtype: int64
    - name: pr_author
      dtype: string
    - name: pr_labels
      list: string
    - name: pr_url
      dtype: string
  splits:
  - name: train
    num_bytes: 77583440
    num_examples: 2288
  download_size: 68346342
  dataset_size: 77583440
---

# SWE-rebench-V2-Filtered-Easy-Verified

[![GitHub](https://img.shields.io/badge/research--environments-swerebench__v2__v1-181717?logo=github)](https://github.com/PrimeIntellect-ai/research-environments/tree/main/environments/swe/swerebench_v2_v1)

Easy slice of
[`PrimeIntellect/SWE-rebench-V2-Filtered-Verified`](https://huggingface.co/datasets/PrimeIntellect/SWE-rebench-V2-Filtered-Verified):
rows whose upstream LLM-judge `difficulty` is `easy` (implementation-time estimate < 15 min).
Useful as a lower-variance starting pool for RL curricula.

## Changes vs upstream

* Pure slice of the Filtered-Verified set — it inherits every filter and verification pass from
  the parent (see its card), including the pass-2 flaky removal, no-edit pass, and repo/image
  blocklists that the earlier standalone `-Easy-Clean` derivation lacked.
* `image_name` carries the raw upstream Docker Hub source ref
  (`docker.io/swerebenchv2/<name>:<tag>`): since the platform's 2026-07-15 org-less image
  migration (ENG-4518), source refs resolve natively on Prime, so no registry rewrite is needed.
  (The parent still temporarily carries `prime/primeintellect/...` refs for prod-training
  compatibility; any such ref is mapped back to its source form here.)

License mirrors upstream: CC-BY-4.0.

## Splits

| Split | Rows |
|---|---:|
| `train` | see parent slice at build time |

## How to use

Install the [`swerebench_v2_v1`](https://github.com/PrimeIntellect-ai/research-environments/tree/main/environments/swe/swerebench_v2_v1) taskset from
[research-environments](https://github.com/PrimeIntellect-ai/research-environments), then run it
end-to-end with [verifiers](https://github.com/PrimeIntellect-ai/verifiers):

```bash
uv pip install --prerelease=allow "git+https://github.com/PrimeIntellect-ai/research-environments.git#subdirectory=environments/swe/swerebench_v2_v1"
uv run eval --taskset.id swerebench_v2_v1 -m <your-model> -n 100 -r 4
```

## Generation

<details>
<summary>Reproduction script — <code>swe-rebench-v2-filtered-easy-verified.py</code></summary>

This dataset was created by running:

````bash
uv run datasets/swe-rebench-v2-filtered-easy-verified.py -H
````

````python
# swe-rebench-v2-filtered-easy-verified.py
"""Derive the easy slice of `PrimeIntellect/SWE-rebench-V2-Filtered-Verified`.

Rows whose upstream LLM-judge ``meta.llm_metadata.difficulty`` is ``"easy"``
(implementation-time estimate < 15 min). A pure slice of the already
filtered-and-verified parent, so it inherits every gate from
``swe-rebench-v2-filtered-verified.py`` — including the pass-2 flaky
removal, the no-edit pass, and the repo/image blocklists that the earlier
standalone ``SWE-rebench-V2-Easy-Clean`` derivation (prime-data PR #21,
closed unmerged) lacked.

One transform on top of the slice: ``image_name`` is mapped back to the raw
upstream Docker Hub source ref (``prime/primeintellect/<name>:<tag>`` →
``docker.io/swerebenchv2/<name>:<tag>``). Since the platform's 2026-07-15
org-less image migration (ENG-4518), source refs resolve natively on Prime,
so no registry rewrite is needed. The parent dataset still temporarily
carries the ``prime/primeintellect/`` rewrite for prod-training
compatibility (until its 6,275 images finish transferring to the platform
registry); once the parent is reverted to source refs too, this mapping
becomes a no-op.
"""

# /// script
# requires-python = ">=3.12"
# dependencies = ["datasets>=4.0.0", "jinja2"]
# ///
import argparse
import sys
from pathlib import Path
from typing import cast

from huggingface_hub import create_repo, whoami

from datasets import Dataset, load_dataset

SOURCE_REPO = "PrimeIntellect/SWE-rebench-V2-Filtered-Verified"

_PRIME_PREFIX = "prime/primeintellect/"
_SOURCE_PREFIX = "docker.io/swerebenchv2/"


def _restore_source_image_ref(image_name: str) -> str:
    # Undo the parent's prime-registry rewrite; no-op for refs already in
    # source form (i.e. once the parent itself is reverted to source refs).
    if image_name.startswith(_PRIME_PREFIX):
        return _SOURCE_PREFIX + image_name[len(_PRIME_PREFIX) :]
    return image_name


def _swe_card(key: str):
    """Build this dataset's card from the shared SWE card registry (swe_cards.py)."""
    sys.path.insert(0, str(Path(__file__).resolve().parent))
    from swe_cards import build_card

    return build_card(key)


def _is_easy(example: dict) -> bool:
    llm = (example.get("meta") or {}).get("llm_metadata") or {}
    return llm.get("difficulty") == "easy"


def prepare_data(source_repo: str) -> Dataset:
    ds = cast(Dataset, load_dataset(source_repo, split="train"))
    n_before = len(ds)
    easy = ds.filter(_is_easy, num_proc=8)
    print(f"Kept {len(easy):,} / {n_before:,} rows with difficulty == easy")
    return easy.map(
        lambda ex: {"image_name": _restore_source_image_ref(ex["image_name"])},
        num_proc=8,
        load_from_cache_file=False,
    )


def main(repo_name: str, push_to_hub: bool, private: bool, source_repo: str) -> None:
    print(f"⚙️ Slicing {source_repo} to difficulty == easy")
    dataset = prepare_data(source_repo)

    card = _swe_card("swe-rebench-v2-filtered-easy-verified")

    if push_to_hub:
        create_repo(repo_name, private=private, repo_type="dataset", exist_ok=True)
        card.push_to_hub(repo_name, repo_type="dataset")
        dataset.push_to_hub(repo_name, private=private)
        print(f"✅ Pushed dataset to https://huggingface.co/datasets/{repo_name}")
    else:
        print("ℹ️  Skipped pushing to HF Hub. To push, use the `--push-to-hub` or `-H` flag.")


def check_write_access(org: str):
    is_authed = False
    try:
        info = whoami()
        token = info["auth"]["accessToken"]["displayName"]
        for entity in info["auth"]["accessToken"]["fineGrained"]["scoped"]:
            if entity["entity"]["name"] == org and "repo.write" in entity["permissions"]:
                is_authed = True
    except Exception:
        raise ValueError("❌ You are not logged in. Please run `hf auth login` or `export HF_TOKEN=...`")
    if not is_authed:
        raise ValueError(f"❌ Your current token `{token}` does not have write access to `{org}`")
    print(f"✅ Confirmed write access with token `{token}` to `{org}`")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--username", "-U", default="PrimeIntellect", type=str, help="The username to push the dataset to."
    )
    parser.add_argument(
        "--dataset-name",
        "-D",
        default="SWE-rebench-V2-Filtered-Easy-Verified",
        type=str,
        help="The dataset name.",
    )
    parser.add_argument("--source-repo", "-S", default=SOURCE_REPO, type=str, help="The parent dataset to slice.")
    parser.add_argument("--push-to-hub", "-H", action="store_true", help="Whether to push the dataset to the hub.")
    parser.add_argument("--dataset-private", "-p", action="store_true", help="Whether to make the dataset private.")
    args = parser.parse_args()

    assert len(args.dataset_name.split("/")) == 1, "Dataset name must not include the username"
    if args.push_to_hub:
        check_write_access(args.username)

    main(
        repo_name=f"{args.username}/{args.dataset_name}",
        push_to_hub=args.push_to_hub,
        private=args.dataset_private,
        source_repo=args.source_repo,
    )

````

</details>



## Original Dataset Card

Snapshot of the [`nebius/SWE-rebench-V2`](https://huggingface.co/datasets/nebius/SWE-rebench-V2)
card at card-build time — see the live card for updates.

<details>
<summary>Original <code>nebius/SWE-rebench-V2</code> dataset card</summary>

# SWE-rebench-V2

## Dataset Summary

SWE-rebench-V2 is a curated dataset of software-engineering tasks derived from real GitHub issues and pull requests. The dataset contains 32,079 samples covering Python, Go, TypeScript, JavaScript, Rust, Java, PHP, Kotlin, Julia, Elixir, Scala, Swift, Dart, C, C++, C#, R, Clojure, OCaml, and Lua.
For log parser functions, base Dockerfiles, and the prompts used, please see https://github.com/SWE-rebench/SWE-rebench-V2  
The detailed technical report is available at [“SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale”](https://arxiv.org/abs/2602.23866).

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("nebius/SWE-rebench-V2", split="train")
print(len(ds))  # 32079
```

## Dataset Structure

| Field | Type | Description |
|---|---|---|
| `instance_id` | `string` | Unique identifier for the instance |
| `repo` | `string` | GitHub repository in `owner/repo` format |
| `base_commit` | `string` | Git commit SHA of the base before the fix |
| `patch` | `string` | The gold patch that resolves the issue |
| `test_patch` | `string` | Diff adding or modifying tests that verify the fix |
| `problem_statement` | `string` | Issue description the patch addresses |
| `pr_description` | `string` | Full pull request description |
| `created_at` | `int64` | Unix timestamp (milliseconds) of the issue/PR creation |
| `image_name` | `string` | Docker image name used for the evaluation environment |
| `language` | `string` | Primary programming language of the repository |
| `interface` | `string` | Description of the code interface changed by the patch |
| `license` | `string` | SPDX license identifier of the repository |
| `FAIL_TO_PASS` | `list[string]` | Test IDs that fail before the patch and pass after |
| `PASS_TO_PASS` | `list[string]` | Test IDs that pass both before and after the patch |
| `install_config` | `struct` | Configuration needed to reproduce the test environment |
| `meta` | `struct` | Metadata and LLM-generated quality annotations |

# License
The dataset is licensed under the Creative Commons Attribution 4.0 license. However, please respect the license of each specific repository on which a particular instance is based. To facilitate this, the license of each repository at the time of the commit is provided for every instance.


# Citation

```bibtex
@misc{badertdinov2026swerebenchv2languageagnosticswe,
      title={SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale}, 
      author={Ibragim Badertdinov and Maksim Nekrashevich and Anton Shevtsov and Alexander Golubev},
      year={2026},
      eprint={2602.23866},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2602.23866}, 
}

</details>