13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
142 lines
4.6 KiB
Markdown
142 lines
4.6 KiB
Markdown
# Claw-Eval
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## Overview
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Claw-Eval evaluates assistant agents on realistic personal-assistant workflows that require tool use, file and fixture
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access, multimodal inputs, and simulated user interactions. EvalScope runs the pinned official Claw-Eval Python runner,
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Docker sandbox, and graders while exposing each Claw-Eval task as a normal EvalScope sample for caching, repeats,
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parallel execution, reporting, and dashboard trace review.
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## Task Description
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- **Task Type**: Agentic personal-assistant tasks with tool use, sandbox files, multimodal fixtures, and optional
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simulated user turns.
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- **Dataset**: `claw-eval/Claw-Eval` on ModelScope.
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- **Subsets**: `general`, `multimodal`, and `multi_turn`; the current ModelScope manifest contains 300 tasks
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(161 general, 101 multimodal, and 38 multi_turn). Use `subset_list` to select subsets.
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- **Output**: Official Claw-Eval scores and JSONL traces, EvalScope sample-level reviews, grouped summary metrics, and
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dashboard-rendered agent traces.
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## Evaluation Notes
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- Requires Python 3.11+ and the official package installed from the pinned source commit:
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`pip install "claw-eval[sandbox,mock,web] @
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git+https://github.com/claw-eval/claw-eval.git@d3f02d4938ab0832377d90535013def2b1a2fdc0"`.
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- The installed package provides the Claw-Eval runner APIs. EvalScope also caches the same pinned source archive because
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`tasks/` and `Dockerfile.agent` are runtime assets, then loads the task manifest and fixtures from ModelScope.
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- Full fixtures are downloaded from ModelScope (`data/fixtures.tar.gz`) and linked into the official task tree before
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execution. The archive is large; use `limit` or `extra_params.task_ids` for smoke runs.
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- Each selected Claw-Eval task is one EvalScope sample. Official scoring runs once per sample; use EvalScope `repeats`
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for repeated trials per task and `eval_batch_size` for task-level worker concurrency.
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- Claw-Eval runs with the official Docker sandbox image. If `claw-eval-agent:latest` is missing locally, EvalScope
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builds it automatically from the cached official `Dockerfile.agent`. The first run can be slow.
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- EvalScope `use_cache` resumes completed task-level samples. Claw-Eval trace JSONL files are stored under
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`outputs/.../claw_eval/<split>/traces` and converted to EvalScope agent traces for dashboard visualization.
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `claw_eval` |
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| **Dataset ID** | [claw-eval/Claw-Eval](https://modelscope.cn/datasets/claw-eval/Claw-Eval/summary) |
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| **Paper** | N/A |
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| **Tags** | `Agent`, `MultiModal`, `MultiTurn` |
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| **Metrics** | `judge_score`, `pass_at_k`, `pass_hat_k`, `error_rate` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `test` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 300 |
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| Prompt Length (Mean) | 47.36 chars |
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| Prompt Length (Min/Max) | 30 / 60 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `general` | 161 | 46.47 | 34 | 58 |
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| `multimodal` | 101 | 49.75 | 30 | 60 |
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| `multi_turn` | 38 | 44.79 | 35 | 51 |
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## Sample Example
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**Subset**: `general`
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```json
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{
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"input": [
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{
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"id": "a34b7f6b",
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"content": "Run Claw-Eval task T001zh_email_triage."
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}
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],
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"target": "",
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"id": 0,
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"group_id": 0,
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"subset_key": "general",
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"metadata": {
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"task_id": "T001zh_email_triage",
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"split": "general",
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"task_name": "",
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"difficulty": "",
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"dataset_id": "claw-eval/Claw-Eval",
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"dataset_hub": "modelscope"
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}
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}
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```
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## Prompt Template
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**Prompt Template:**
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```text
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{question}
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```
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## Extra Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `task_ids` | `list` | `[]` | Optional exact Claw-Eval task ids to run after split filtering. |
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets claw_eval \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['claw_eval'],
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dataset_args={
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'claw_eval': {
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# subset_list: ['general', 'multimodal', 'multi_turn'] # optional, evaluate specific subsets
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# extra_params: {} # uses default extra parameters
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}
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},
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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