Datasets:
metadata
language:
- en
license: cc-by-4.0
task_categories:
- text-generation
pretty_name: SWE-rebench-V2-Filtered-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: 276137448
num_examples: 6272
download_size: 226272977
dataset_size: 276137448
SWE-rebench-V2-Filtered-Verified
Filtered and gold-patch-verified subset of Nebius's
SWE-rebench-V2
(paper): 6,272 / 32,079 freshly-mined GitHub PR tasks
across 17 languages. Default dataset of the swerebench_v2_v1 taskset.
Changes vs upstream
Filtered (selection — the bulk of the cut):
- Upstream's own per-row LLM-judge metadata:
difficultylabeled easy/medium/hard, judge gradecode == "A"(clearly solvable),intent_completeness == "complete", nodetected_issues, judgeconfidence ≥ 0.95. - Leak hygiene:
external_urls == [], plus a regex scrub dropping rows whose problem statement contains inline#1234/gh-1234//pull/1234-style references (the fixing PR is look-up-able otherwise). - Wholesale language drops — Julia, C++, Clojure images are broken for scoring (missing
runtimes / wholesale-failing suites); a manual image blocklist (including 29 C#/Go images that deterministically fail Prime sandbox-image conversion, so every kept row has a runnable public Prime-registry image); a
statamic/cmsrepo drop (its tasks run unrelated JS suites, scoring 1.0 without the fix).
Verified (our passes, the final gate):
- Gold-patch validation pass 1 via SolveEnv — reward 1.0 required.
- Independent pass 2 — flaky rows removed.
- No-edit pass — rows solvable with zero edits removed.
- RL always-fail audit (2026-07) — instances that scored 0.0 in every 16-rollout group across independent GLM-5.2 RL samplings were re-gold-validated twice; 2 deterministically unresolvable + 1 flaky removed.
Per-row outcomes ship with this repo (swe-rebench-v2-validation.jsonl,
swe-rebench-v2-flaky-instances.json, swe-rebench-v2-no-edit-pass-exclusions.jsonl,
swe-rebench-v2-rl-alwaysfail-exclusions.json).
image_name is rewritten to the public Prime image registry (prime/primeintellect/...) so
sandbox rollouts avoid Docker Hub rate limits and get fast pulls. Schema is otherwise unchanged.
License mirrors upstream: CC-BY-4.0.
Splits
| Split | Rows |
|---|---|
train |
6,272 |
How to use
Install the swerebench_v2_v1 taskset from
research-environments, then run it
end-to-end with verifiers:
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
Reproduction script — swe-rebench-v2-filtered-verified.py
This dataset was created by running:
uv run datasets/swe-rebench-v2-filtered-verified.py -H
# swe-rebench-v2-clean.py
"""Filter `nebius/SWE-rebench-V2` (32k) to a clean RL-trainable subset.
Filters (all applied conjunctively against `meta.llm_metadata`, which Nebius
populates per row using the `meta_info.j2` LLM-judge rubric — see
https://github.com/SWE-rebench/SWE-rebench-V2/blob/main/prompts/annotations/meta_info.j2):
* `difficulty in {"easy", "medium", "hard"}` — drops only ``null`` /
unannotated rows. Each difficulty is validated independently below;
``meta.llm_metadata.difficulty`` remains on every row so downstream
consumers can still partition by difficulty if they want.
* `code == "A"` — judge graded the instance SOLVABLE (problem clearly
specified, tests align with stated requirements). B1–B6 grades flag
test-suite coupling, implicit naming, missing external-URL info,
ambiguous spec, patch artifacts, or implicit domain knowledge.
* `intent_completeness == "complete"` — problem statement provides
sufficient detail (vs. partial / insufficient).
* `detected_issues` all false — no B1–B6 environment/spec problems detected.
* `confidence >= --min-confidence` (default 0.95) — judge classification
certainty.
* `external_urls == []` — judge extracted no referenced URLs from the
issue text. B3 already drops cases where the URL info is *essential*
and missing; this extra filter drops cases where any URL is present
(paranoid, since the V2 problem_statement is the raw GitHub issue body
and incidental URLs there are still a solvability risk).
Plus one regex pass on `problem_statement` itself, since `external_urls`
is URL-only and misses inline GitHub-style issue/PR references like
`#1234`, `gh-1234`, `PR #1234`, `/issues/1234`, `/pull/1234`. Any such
reference drops the row.
Plus a manually-curated image blocklist at
``swe-rebench-v2-exclude-images.json`` (sibling of this file) — drops
rows whose ``image_name`` matches. This includes 29 C#/Go images
(microsoft-kiota, devlooped-moq, microsoft-typescript-go,
asynkron-protoactor-dotnet, btcpayserver, spectreconsole) whose Docker Hub
images deterministically fail Prime platform sandbox-image conversion
(3 independent attempts, 2026-07) — excluded so every kept row has a
runnable public image in the Prime registry.
Plus a wholesale lang drop (``_DROP_LANGS``: julia, cpp, clojure):
SolveEnv pass-1 found these were 0/N because Nebius's images for these
langs miss the runtime / have wholesale-broken test setups.
Plus a SolveEnv gold-patch validation gate: only instances where applying
the gold patch and running the upstream ``test_cmd`` yields reward 1.0
are kept. Per-row outcomes (reward, reason, elapsed, test_output_tail tail)
live in ``swe-rebench-v2-validation.jsonl`` for posterity / reproducibility.
The file is the union of two passes (one over the hard+medium split, one
over the easy split).
Plus a flaky-instance blocklist at ``swe-rebench-v2-flaky-instances.json``:
instances that passed pass 1 but failed an independent re-validation. The
hard/medium half of the list is conservative (only ``test_failed`` /
``gold_apply_failed`` reasons retained); the easy half includes all
non-passing reasons including infra-noise (sandbox_error, timeout, None)
plus one explicit hang row (``brazilian-utils__brutils-python-126``).
Plus a manual repo blocklist (``_DROP_REPOS``) for repositories discovered to
have systematically non-discriminating tests after validation.
Plus a no-edit pass blocklist at
``swe-rebench-v2-no-edit-pass-exclusions.jsonl``: instances where the no-edit
debugger got reward 1.0, meaning the tests pass without applying any agent edit.
Plus an RL always-fail audit blocklist at
``swe-rebench-v2-rl-alwaysfail-exclusions.json``: instances flagged during
GLM-5.2 RL training (every 16-rollout group scored 0.0 across independent
samplings) and then gold-validated twice via the ``swerebench_v2_v1`` taskset's
``validate`` hook (2026-07-17). Two are deterministically unresolvable under
the current verifier (graded FAIL_TO_PASS tests the row's ``test_cmd`` never
runs, or tests needing JDK toolchains absent from the image) and one is flaky
(2,422 all-or-nothing tests spawning servers/ports). Per-row verdicts and
reasons live in the file.
After filtering, ``image_name`` is rewritten from
``docker.io/swerebenchv2/<name>:<tag>`` to the public Prime image registry at
``prime/primeintellect/<name>:<tag>`` (the ``swerebenchv2/`` namespace is
stripped) so sandbox rollouts avoid Docker Hub rate limits and get fast pulls.
Field source notes:
* `problem_statement` (V2) is the raw GitHub issue body.
* `pr_description` (V2) is the raw merged PR description (NOT LLM-rewritten —
contrary to what one might assume from `pr_description.j2`, which is what
Nebius uses to generate the `problem_statement` of the *separate* 126k
`nebius/SWE-rebench-V2-PRs` release, not V2's pr_description).
"""
# /// script
# requires-python = ">=3.12"
# dependencies = ["datasets>=4.0.0", "jinja2"]
# ///
import argparse
import json
import re
import sys
import time
from pathlib import Path
from typing import cast
from huggingface_hub import create_repo, upload_file, whoami
from datasets import Dataset, load_dataset
SOURCE_REPO = "nebius/SWE-rebench-V2"
# Inline GitHub issue/PR references not captured by `external_urls`
# (which is URL-only). Catches `#1234`, `gh-1234`, `PR #1234`,
# `issue #1234`, `/issues/1234`, `/pull/1234`.
_RE_ISSUE_REF = re.compile(
r"(#\d{2,}|gh-\d+|PR\s*#?\d+|issue\s*#?\d+|/issues/\d+|/pull/\d+|/pulls/\d+)",
re.IGNORECASE,
)
_EXCLUDE_IMAGES_PATH = Path(__file__).parent / "swe-rebench-v2-exclude-images.json"
_EXCLUDE_IMAGES = frozenset(json.loads(_EXCLUDE_IMAGES_PATH.read_text()))
# Wholesale-drop languages that had 0/N pass rate in SolveEnv pass-1
# validation (Nebius's images for these langs are missing the runtime
# or have wholesale-broken test setups). Verified across the easy split;
# applied to all difficulties uniformly since the underlying images are
# shared across difficulties.
_DROP_LANGS = frozenset({"julia", "cpp", "clojure"})
# SolveEnv gold-patch validation outcomes, one row per instance_id. Built by
# running `vf-eval solve_swe -a '{"task_type":"swerebench-v2",...}' -m none`
# over the prior post-filter set; kept here for reproducibility / posterity.
# Only instances with reward == 1.0 are retained downstream.
_VALIDATION_PATH = Path(__file__).parent / "swe-rebench-v2-validation.jsonl"
_VALIDATION_PASSING = frozenset(
json.loads(line)["instance_id"]
for line in _VALIDATION_PATH.read_text().splitlines()
if json.loads(line).get("reward") == 1.0
)
# Flaky instances: passed gold-patch validation in pass 1 but failed (or hung)
# in an independent pass 2 with the same args, no retries. Removed downstream
# so training rollouts don't see noisy reward signal.
_FLAKY_PATH = Path(__file__).parent / "swe-rebench-v2-flaky-instances.json"
_FLAKY = frozenset(entry["instance_id"] for entry in json.loads(_FLAKY_PATH.read_text()))
_NO_EDIT_PASS_PATH = Path(__file__).parent / "swe-rebench-v2-no-edit-pass-exclusions.jsonl"
_NO_EDIT_PASSING = frozenset(
json.loads(line)["instance_id"] for line in _NO_EDIT_PASS_PATH.read_text(encoding="utf-8").splitlines() if line
)
# RL always-fail audit exclusions: instances whose reward was 0.0 in every
# 16-rollout group across independent GLM-5.2 RL samplings, then gold-validated
# twice (2026-07-17) — two deterministically unresolvable, one flaky.
_RL_ALWAYSFAIL_PATH = Path(__file__).parent / "swe-rebench-v2-rl-alwaysfail-exclusions.json"
_RL_ALWAYSFAIL = frozenset(entry["instance_id"] for entry in json.loads(_RL_ALWAYSFAIL_PATH.read_text()))
# statamic/cms rows add PHP tests but run unrelated JS suites, so base +
# test_patch can score reward 1.0 without the issue fix.
_DROP_REPOS = frozenset({"statamic/cms"})
_DOCKERHUB_PREFIX = "docker.io/swerebenchv2/"
_PRIME_PREFIX = "prime/primeintellect/"
def _normalize_image(image_name: str) -> str:
# Dataset rows carry e.g. ``docker.io/swerebenchv2/foo-bar:tag``; the
# exclusion list omits the ``docker.io/`` prefix. Strip it for comparison.
prefix = "docker.io/"
return image_name[len(prefix) :] if image_name.startswith(prefix) else image_name
def _rewrite_image_to_prime_registry(image_name: str) -> str:
# Subset images are re-transferred as PUBLIC images to the Prime platform
# registry under ``prime/primeintellect/`` (the Docker Hub ``swerebenchv2/``
# namespace is stripped); pulls from there avoid Docker Hub rate limits and
# are much faster from sandbox hosts.
if image_name.startswith(_DOCKERHUB_PREFIX):
return _PRIME_PREFIX + image_name[len(_DOCKERHUB_PREFIX) :]
return image_name
def _coerce_llm_metadata(meta: dict) -> dict:
# `meta.llm_metadata` is a dict in the current revision; tolerate JSON-string variants too.
llm = meta.get("llm_metadata")
if isinstance(llm, str):
return json.loads(llm)
assert isinstance(llm, dict), f"unexpected llm_metadata type: {type(llm).__name__}"
return llm
def _passes_filter(example: dict, min_confidence: float) -> bool:
llm = _coerce_llm_metadata(example["meta"])
if llm.get("difficulty") not in ("easy", "medium", "hard"):
return False
if llm.get("code") != "A":
return False
if llm.get("intent_completeness") != "complete":
return False
if any(llm.get("detected_issues", {}).values()):
return False
if llm.get("external_urls"):
return False
confidence = llm.get("confidence")
if confidence is None or confidence < min_confidence:
return False
if _RE_ISSUE_REF.search(example.get("problem_statement") or ""):
return False
if _normalize_image(example.get("image_name") or "") in _EXCLUDE_IMAGES:
return False
if example.get("language") in _DROP_LANGS:
return False
if example.get("instance_id") not in _VALIDATION_PASSING:
return False
if example.get("instance_id") in _FLAKY:
return False
if example.get("instance_id") in _NO_EDIT_PASSING:
return False
if example.get("instance_id") in _RL_ALWAYSFAIL:
return False
if (example.get("repo") or "").lower() in _DROP_REPOS:
return False
return True
def prepare_data(min_confidence: float) -> Dataset:
ds = cast(Dataset, load_dataset(SOURCE_REPO, split="train"))
filtered = ds.filter(
lambda ex: _passes_filter(ex, min_confidence),
num_proc=8,
load_from_cache_file=False,
)
filtered = filtered.map(
lambda ex: {"image_name": _rewrite_image_to_prime_registry(ex["image_name"])},
num_proc=8,
load_from_cache_file=False,
)
return filtered
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 push_card_to_hub(repo_name: str, push_to_hub: bool):
_, dataset_name = repo_name.split("/")
card = _swe_card("swe-rebench-v2-filtered-verified")
if push_to_hub:
print(f"Pushing card to `{repo_name}`")
card.push_to_hub(repo_name, repo_type="dataset")
print(f"✅ Pushed card to `{repo_name}` to HF Hub")
else:
print("ℹ️ Skipped pushing card to HF Hub. To push, use the `--push-to-hub` or `-H` flag.")
def main(repo_name: str, push_to_hub: bool, private: bool, min_confidence: float, also_report_090: bool):
print(f"⚙️ Filtering {SOURCE_REPO} (confidence >= {min_confidence})")
start_time = time.time()
dataset = prepare_data(min_confidence)
elapsed = time.time() - start_time
print(f"✅ Filtered to {len(dataset):,} rows in {elapsed:.2f} seconds")
if also_report_090 and len(dataset) < 50:
print("ℹ️ <50 rows at confidence >= 0.95; also reporting confidence >= 0.90")
start_time = time.time()
dataset_090 = prepare_data(0.90)
elapsed = time.time() - start_time
print(f"✅ At confidence >= 0.90: {len(dataset_090):,} rows in {elapsed:.2f} seconds")
if push_to_hub:
create_repo(repo_name, private=private, repo_type="dataset", exist_ok=True)
push_card_to_hub(repo_name, push_to_hub)
dataset.push_to_hub(repo_name, private=private)
for sidecar in (_NO_EDIT_PASS_PATH, _RL_ALWAYSFAIL_PATH):
upload_file(
path_or_fileobj=str(sidecar),
path_in_repo=sidecar.name,
repo_id=repo_name,
repo_type="dataset",
)
print(f"✅ Pushed dataset to https://huggingface.co/datasets/{repo_name}")
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-Verified", type=str, help="The dataset name."
)
parser.add_argument("--dataset-private", "-p", action="store_true", help="Whether to make the dataset private.")
parser.add_argument("--push-to-hub", "-H", action="store_true", help="Whether to push the dataset to the hub.")
parser.add_argument(
"--min-confidence", "-c", default=0.95, type=float, help="Minimum llm_metadata.confidence to keep."
)
parser.add_argument(
"--also-report-090",
action="store_true",
default=True,
help="If <50 rows at the chosen confidence, also report the count at >= 0.90.",
)
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,
min_confidence=args.min_confidence,
also_report_090=args.also_report_090,
)
Original Dataset Card
Snapshot of the nebius/SWE-rebench-V2
card at card-build time — see the live card for updates.
Original nebius/SWE-rebench-V2 dataset card
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”.
Quick Start
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
@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>