Blame for README.md

---
license: cc-by-4.0
task_categories:
  - document-question-answering
  - feature-extraction
language:
  - en
tags:
  - structured-extraction
  - information-extraction
  - document-understanding
  - pdf
  - resume
  - json-schema
  - benchmark
size_categories:
  - n<1K
pretty_name: ResumeExtractBench
configs:
  - config_name: default
    data_files:
      - split: test
        path: test.jsonl
      - split: mini
        path: mini.jsonl
---

# ResumeExtractBench

**ResumeExtractBench** is a benchmark for schema-guided structured extraction from resume documents. Given a resume PDF and a JSON Schema, systems must return structured data covering personal details, work history, education, skills, and more.

- **Dataset Size**: 30 documents (real-world anonymized + synthetic + handwritten + expert-created)
- **Schema Sections Scored**: 14 (basics, experience, education, projects, personalSummary, certifications, awards, volunteering, skills, publications, languages, interests, profiles, customSections)
- **Domains**: 11 (engineering, software-engineering, legal, government, healthcare, finance, education, consulting, design, non-profit, general)
- **License**: CC-BY-4.0

**Quick Links**: [Code & CLI](https://github.com/careerflow/resume-extract-bench) | [Careerflow](https://careerflow.ai)

---

## Dataset Introduction

### Document Composition

| Source Category | Documents | Description |
|---|---|---|
| **Real-world anonymized** | 5 | Careerflow user resumes with PII replaced via synthetic anonymization |
| **Synthetic forge** | 5 | Generated resumes with diverse Word/LaTeX templates and real-like layouts |
| **Expert-created (Canva)** | 5 | Manually designed resumes using Canva templates with intentional distractors |
| **Synthetic LaTeX** | 4 | Template-diverse LaTeX resumes (academic, executive, ATS, creative styles) |
| **Handwritten** | 4 | Scanned handwritten resumes testing OCR and layout understanding |
| **Synthetic legal** | 3 | LaTeX-generated legal professional resumes with domain-specific content |
| **Synthetic government** | 4 | LaTeX-generated government/military resumes with clearances, MOS codes |
| **Total** | **30** | |

### Domain Distribution

| Domain | Count |
|---|---|
| Government | 7 |
| Legal | 6 |
| Engineering | 4 |
| General | 3 |
| Education | 3 |
| Finance | 2 |
| Consulting | 2 |
| Software Engineering | 1 |
| Non-profit | 1 |
| Design | 1 |

### Scoring Methodology: ResumeBench-Strict

This benchmark uses **ResumeBench-Strict** scoring with three rules that make it harder than per-cell scoring:

1. **Record-level units.** An entity (one job, school, certification, etc.) is ONE unit: it is correct only if it is aligned AND every non-empty field matches AND its bullets match one-for-one (same count, each bullet equal).

2. **Empty is not free.** Basics fields count only where the label or the parser has a value. Skills: one unit per skill.

3. **Section-averaged.** Per document, F1 / precision / recall are computed per section and averaged, so every section weighs the same.

**Verbatim mode** (on by default): strings match only after whitespace collapsing — case and punctuation count. Typographic variants (smart quotes, em dashes, soft hyphens) are folded.

---

## Quick Start

```bash
git clone https://github.com/careerflow/resume-extract-bench.git
cd resume-extract-bench
pip install -e ".[dev]"

# Download the dataset
resume-bench download

# Run extraction (example: Anthropic Haiku, needs RESUME_BENCH_ANTHROPIC_API_KEY)
resume-bench run anthropic_claude-haiku-4-5_text --split mini --limit 5

# Grade results (verbatim by default)
resume-bench grade anthropic_claude-haiku-4-5_text --split mini

# View leaderboard
resume-bench leaderboard --split mini
```

---

## Dataset Format

### Splits

| Split | Count | Purpose |
|---|---|---|
| `test` | 30 | Full evaluation set |
| `mini` | 5 | Quick smoke test |

### Record Schema (JSONL)

Each line in `test.jsonl` / `mini.jsonl`:

```json
{
  "resume_id": "009b8ddc-4d4e-4452-ba2a-cc43ef83df77",
  "files": {"pdf": "pdfs/009b8ddc-4d4e-4452-ba2a-cc43ef83df77.pdf"},
  "ground_truth": {
    "basics": {"fname": "...", "lname": "...", "email": "...", ...},
    "experience": {"sectionTitle": null, "items": [{"company": "...", "position": "...", "description": [...]}]},
    "education": {"sectionTitle": null, "items": [{"institution": "...", "area": "..."}]},
    "skills": {"sectionTitle": null, "items": [{"category": "...", "skills": ["Python", "Go"]}]},
    "personalSummary": {"sectionTitle": null, "text": "..."},
    ...
  },
  "difficulty": "medium",
  "layout_tags": ["real-anonymized", "table_grid"],
  "source": "real-anonymized",
  "domain": "software-engineering",
  "schema_version": "resume_v1"
}
```

### 14 Scored Sections

| Section | Type | Key Fields |
|---|---|---|
| `basics` | singleton | prefix, fname, lname, suffix, email, phone, city, state, country, linkedinUrl |
| `experience` | entity-list | company, position, dates, city/state/country, description bullets |
| `education` | entity-list | institution, area, studyType, score, dates, descriptions |
| `projects` | entity-list | name, dates, url, description |
| `personalSummary` | text | Free-text professional summary |
| `certifications` | entity-list | name, issuer, date, url |
| `awards` | entity-list | title, awarder, date, summary |
| `volunteering` | entity-list | organization, position, dates, description |
| `skills` | flat-list | Flat skill names across categories |
| `publications` | entity-list | name, publisher, date, summary |
| `languages` | entity-list | name, level |
| `interests` | entity-list | name |
| `profiles` | entity-list | network, url |
| `customSections` | entity-list | sectionTitle, summary, description |

---

## Grading Your Own Model

### Python API

```python
from resume_bench.grading.grader import grade_single_strict

result = grade_single_strict(ground_truth, prediction, verbatim=True)
print(f"F1: {result['f1']:.4f}")
print(f"Sections: {result['sections']}")
```

### JSONL Predictions File

```bash
# Create predictions file
echo '{"resume_id": "abc123", "prediction": {...}}' > preds.jsonl

# Grade
resume-bench grade-file preds.jsonl --split test
```

---

## Citation

```bibtex
@misc{resumeextractbench2026,
  title={ResumeExtractBench: A Benchmark for Schema-Guided Structured Extraction from Resumes},
  author={Careerflow and LlamaIndex},
  year={2026},
  url={https://huggingface.co/datasets/careerflow/ResumeExtractBench}
}
```