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>
116 lines
3.0 KiB
Markdown
116 lines
3.0 KiB
Markdown
# OfficeQA
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## Overview
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OfficeQA is a grounded reasoning benchmark by Databricks, built for evaluating model/agent performance on end-to-end grounded reasoning tasks over U.S. Treasury Bulletin documents (1939-2025).
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## Task Description
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- **Task Type**: Agent-based Document QA (grep/search over corpus)
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- **Input**: A question + access to parsed Treasury Bulletin text files via bash tools
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- **Output**: A precise answer (numeric values, text, or structured data)
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- **Evaluation Mode**: Agent with bash tool (grep, cat, etc.) over the corpus
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## Key Features
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- Two subsets: `officeqa_pro` (133 questions, hard, default) and `officeqa_full` (246 questions, easy+hard)
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- Corpus: ~900 parsed Treasury Bulletin text files (~460MB total)
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- Agent uses bash tools (grep, cat, head, etc.) to search the corpus
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- Scoring uses fuzzy numeric matching with configurable tolerance (1% default)
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## Evaluation Notes
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- The agent is given access to parsed .txt files in a corpus directory
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- Each question's `source_files` field indicates which document(s) contain the answer
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- Uses **rule-based scoring** adapted from official reward.py
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- Numerical answers matched with 1% relative error tolerance
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- Text answers use case-insensitive substring matching
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `officeqa` |
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| **Dataset ID** | [evalscope/officeqa](https://modelscope.cn/datasets/evalscope/officeqa/summary) |
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| **Paper** | N/A |
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| **Tags** | `Agent`, `Knowledge`, `QA` |
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| **Metrics** | `accuracy` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 133 |
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| Prompt Length (Mean) | 443.06 chars |
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| Prompt Length (Min/Max) | 165 / 1186 chars |
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## Sample Example
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**Subset**: `default`
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```json
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{
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"input": [
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{
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"id": "a6357de6",
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"content": "What were the total expenditures (in millions of nominal dollars) for U.S national defense in the calendar year of 1940?\nPlease provide a precise and concise answer."
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}
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],
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"target": "2,602",
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"id": 0,
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"group_id": 0,
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"metadata": {
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"uid": "UID0001",
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"source_files": "treasury_bulletin_1941_01.txt",
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"difficulty": "hard"
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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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## 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 officeqa \
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--agent-config '{"mode":"native","strategy":"function_calling","max_steps":15}' \
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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 TaskConfig, run_task
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from evalscope.api.agent import NativeAgentConfig
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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=['officeqa'],
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agent_config=NativeAgentConfig(
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strategy='function_calling',
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max_steps=15,
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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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