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Datasets:
NEAR-AI
/
pinchbench

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
< 1K
Tags:
agents
agentic-benchmark
evaluation
pinchbench
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pandas
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pinchbench / README.md
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pranav-near
Add nearai-bench flat packaging (one row per task)
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metadata 
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - agents
  - agentic-benchmark
  - evaluation
  - pinchbench
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

PinchBench (nearai-bench packaging)

A flat, self-contained repackaging of the upstream PinchBench skill suite (147 tasks, BENCHMARK_VERSION 2.0.0) for agent-harness consumption. Task content is unmodified — ids, filenames, prompts, rubrics and graders are byte-identical to upstream, so scores stay comparable to the pinchbench.com leaderboard.

Why this exists

Upstream ships tasks as a directory of markdown files plus a separate asset pool that has to be fetched with a shell script (and Git LFS). That is awkward for an eval/RL harness that wants to stream tasks across workers. Here, each task is one row: the verbatim markdown in task_md, and every asset the task references as a deterministic tar.gz in assets_tar. No clone, no LFS, no fetch script.

from datasets import load_dataset
ds = load_dataset("NEAR-AI/pinchbench", split="train")

Columns

Column Type Notes
task_id string Upstream task id, matches the upstream filename stem
name string Human-readable title
category string One of the 11 upstream categories
grading_type string automated | llm_judge | hybrid
timeout_seconds int64 Upstream per-task budget
prompt string ## Prompt section
expected_behavior string ## Expected Behavior section
automated_checks string ## Automated Checks — Python grade(transcript, workspace_path)
llm_judge_rubric string ## LLM Judge Rubric, empty when the task has none
grading_criteria string ## Grading Criteria; upstream judge falls back to this when the rubric is empty
grading_weights string (JSON) {"automated": w, "llm_judge": w}; {} ⇒ upstream 0.5/0.5 default
multi_session bool Multi-turn dialogue task
sessions string (JSON) Session definitions for multi-session tasks
prerequisites string (JSON) External tooling the task needs, e.g. ["cli:gh"]
workspace_files string (JSON) Upstream workspace_files verbatim — inline content entries and source/dest asset refs
asset_paths string (JSON) Asset paths bundled in assets_tar
assets_tar string base64(tar.gz) of this task's assets; "" when the task has none
task_md string The verbatim upstream markdown file, frontmatter included

Reconstructing an on-disk task

task_md + assets_tar reproduce the upstream layout byte-for-byte, which is what keeps grading identical:

import base64, io, json, tarfile, pathlib

def materialize(row, out_dir):
    out = pathlib.Path(out_dir); out.mkdir(parents=True, exist_ok=True)
    (out / f"{row['task_id']}.md").write_text(row["task_md"])
    if row["assets_tar"]:
        blob = base64.b64decode(row["assets_tar"])
        with tarfile.open(fileobj=io.BytesIO(blob), mode="r:gz") as tar:
            tar.extractall(out / "assets")
    return out

Then seed a task workspace from workspace_files: entries with path + content are written inline; entries with source + dest are copied from the extracted assets/ tree.

Grading

Upstream's three modes, unchanged:

  • automated — run the Python grade() in automated_checks over the agent transcript and workspace; task score is the arithmetic mean of the returned criterion scores.
  • llm_judge — score against llm_judge_rubric (or grading_criteria when the rubric is absent); the judge's total is the task score.
  • hybrid — weighted combination using grading_weights, defaulting to 0.5/0.5.

A reference implementation of all three (a faithful port of upstream lib_grading.py) lives in nearai/benchmarks at src/adapters/pinchbench.rs.

Caveats

  • 4 tasks (gws_*, gh_issue_triage) declare prerequisites — npm @juppytt/fws, the gh/gws CLIs — that no loader installs for you. They score near zero without that tooling (and without a mock backend for the live services they drive).
  • Assets are scoped to what the 147 upstream tasks actually reference.

Provenance & license