| --- |
| 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} |
| } |
| ``` |
|
|
