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: 38 documents (handwritten + adversarial distractors)
• Schema Sections Scored: 9 (basics, experience, education, projects, summary, certifications, awards, volunteering, skills)
• Domains: 6 (engineering, healthcare, legal, data science, government, general)
• License: CC-BY-4.0

Quick Links: 💻 Code & CLI | 🏢 Careerflow

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Dataset Introduction

Document Composition

Challenge Category	Documents	Description
Handwritten resumes	28	Scanned handwritten resumes testing OCR and layout understanding
Adversarial distractors	10	Synthetic resumes with intentional parsing challenges
Total	38

Source Composition

• Handwritten documents: 28
• Synthetic (LaTeX-generated): 10
• All documents classified as "hard" difficulty

Challenge Categories

C1: Handwritten Resumes — Scanned handwritten documents with natural variation in handwriting, layout, and legibility. Tests OCR accuracy, spatial reasoning, and robustness to non-standard formatting. Typical failures: misread characters, merged/split words, missed sections, hallucinated content from ambiguous handwriting.

C2: Adversarial Distractors — Synthetic resumes with intentional parsing challenges designed to stress-test extraction robustness:

Distractor	Challenge
Date chaos	Inconsistent date formats within the same resume (Jan 2020, 2020-06, 06/2021, Summer 2022)
Esoteric titles	Non-standard job titles (Chief Vibes Officer, Growth Hacker), salary listed, entity confusion
European regional	German-style CV with date of birth, marital status, nationality, military service, German grade scale
Functional format	Skills-first layout with no chronological work history
Identity confusion	Multi-part names, honorifics, maiden names, father's name, testimonial quotes with other people's titles
Multilingual	English resume with French and Spanish intertwined
Prompt injection	Hidden white text injection, PDF metadata injection, keyword stuffing in 6pt font
Prose narrative	Third-person writing, no bullet points, 500-word personal statement, emoji section markers
Same company repeat	Same employer listed 4 times with company renamed mid-tenure and overlapping dates
Skill-name collision	Candidate name collides with programming language (Ruby Chen at Python Solutions Inc.)

Domain Coverage

Domain	Documents
Engineering	11
General	10
Healthcare	7
Legal	7
Data Science	2
Government	1

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Usage

Loading with Datasets

from huggingface_hub import snapshot_download
import json

root = snapshot_download(
    repo_id="careerflow/ResumeExtractBench",
    repo_type="dataset",
)

with open(f"{root}/test.jsonl") as f:
    cases = [json.loads(line) for line in f if line.strip()]

schema = json.load(open(f"{root}/schema.json"))

for case in cases:
    pdf_path = f"{root}/{case['files']['pdf']}"
    ground_truth = case["ground_truth"]
    # Run your extractor on pdf_path against schema
    # Compare output to ground_truth

Running Evaluation with CLI

pip install git+https://github.com/careerflow/resume-extract-bench.git
resume-bench download                                          # download dataset
resume-bench run --pipeline gpt-5.6 --split test               # run extraction
resume-bench grade --split test                                 # score against GT
resume-bench leaderboard                                        # view results

Bring Your Own Predictions

resume-bench grade-file my_predictions.jsonl --split test

Predictions JSONL supports two formats:

Flat format (recommended):

{"resume_id": "board-certified-ocularist-jane-doe", "basics": {"fname": "Jane", "lname": "Doe", ...}, "experience": [...], ...}

Wrapped format:

{"resume_id": "board-certified-ocularist-jane-doe", "prediction": {"basics": {...}, "experience": [...], ...}}

Dataset Files

• test.jsonl: 38 test cases (one JSON object per line)
• schema.json: Target JSON Schema for extraction (resume_v1)
• pdfs/: Source resume PDFs

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Dataset Format

Each JSONL line represents one resume test case:

{
    "resume_id": "distractor-date-chaos-inconsistent-date-formats-throughout",
    "files": {"pdf": "pdfs/distractor-date-chaos-inconsistent-date-formats-throughout.pdf"},
    "ground_truth": { ... },
    "difficulty": "hard",
    "layout_tags": ["distractor", "inconsistent-date-formats"],
    "source": "synthetic-distractor",
    "domain": "software-engineering",
    "schema_version": "resume_v1"
}

Field Definitions

Field	Type	Description
resume_id	string	Unique identifier (PDF filename stem)
files.pdf	string	Relative path to source PDF
ground_truth	object	Human-verified structured extraction conforming to schema.json
difficulty	string	Difficulty level (easy, medium, hard)
layout_tags	list[string]	Visual and structural challenge tags
source	string	Resume origin: expert-handwritten, synthetic-distractor
domain	string	Professional domain of the resume
schema_version	string	Schema version (resume_v1)

Extraction Schema (resume_v1)

The schema defines 9 sections that must be extracted from each resume:

Section	Type	Key Fields	Scored
basics	Singleton object	fname, lname, email, phone, city, state, country, hasPersonalPhoto	Per-field accuracy
experience	Entity list	company, position, startMonth/Year, endMonth/Year, city, description[]	Entity P/R/F1 + description token F1
education	Entity list	institution, area, studyType, startMonth/Year, endMonth/Year, description[]	Entity P/R/F1 + description token F1
projects	Entity list	name, url, description[]	Entity P/R/F1 + description token F1
personalSummary	Free text	—	Token F1
certifications	Entity list	name, issuer	Entity P/R/F1
awards	Entity list	title	Entity P/R/F1
volunteering	Entity list	organization, position, startYear, endYear, description[]	Entity P/R/F1 + description token F1
skills	Flat list	category, skills[]	Set-level P/R/F1

See schema.json for the full JSON Schema definition.

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Evaluation Metrics

Scoring Methods

1. Entity-Level F1: Entities (experience entries, education entries, etc.) are aligned between prediction and ground truth using the Hungarian algorithm (optimal bipartite matching via scipy.optimize.linear_sum_assignment). Match quality is determined by Jaro-Winkler similarity on key fields (threshold: 0.5).

2. Description Token F1: For matched entities with description arrays, a bag-of-words token F1 score measures description quality.

3. Hallucination Rate: Fraction of predicted entities that have no match in ground truth (spurious predictions).

4. Omission Rate: Fraction of ground truth entities that have no match in predictions (missed extractions).

All scoring is deterministic with no model in the loop.

Aggregate Metrics

Per-section scores are averaged across all resumes. The overall F1 is the mean of all section F1 scores, providing a single leaderboard ranking metric.

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Leaderboard

Results from the full 155-resume benchmark (includes this dataset plus additional medium-difficulty resumes):

Rank	Model	Overall F1	Halluc. Rate	Omission Rate
1	LlamaExtract Agentic Plus	0.945	5.1%	2.9%
2	Claude Opus	0.937	5.1%	3.6%
3	Extend Extract	0.937	4.3%	4.3%
4	Extend Max Context	0.935	4.7%	4.6%
5	Reducto Extract	0.931	5.4%	4.1%
6	Gemini 3.5 Flash	0.928	5.8%	4.3%
7	Reducto Deep	0.923	6.3%	4.9%
8	GPT-5.5	0.921	9.4%	2.5%
9	GPT-5.6	0.920	8.7%	3.2%
10	Gemma 4 26B	0.914	6.5%	5.4%

19 models benchmarked. Full results available in the code repository.

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Tag Taxonomy

ResumeExtractBench tags documents along three axes:

Axis	Tags	Description
Source	expert-handwritten, synthetic-distractor	How the resume was created
Difficulty	easy, medium, hard	Overall extraction difficulty
Domain	engineering, healthcare, legal, data-science, government, general	Professional domain

Layout tags provide additional visual/structural metadata per resume (e.g., handwritten, fancy-templates, canva).

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Citation

@misc{careerflow2026resumeextractbench,
    title={ResumeExtractBench: A Benchmark for Schema-Guided Resume Extraction},
    author={Careerflow and LlamaIndex},
    year={2026},
    url={https://huggingface.co/datasets/careerflow/ResumeExtractBench},
}

License

All documents are synthetic or expert-created with fictional personal information. Released under CC-BY-4.0.