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Datasets:
Careerflow
/
ResumeExtractBench

Tasks:
Document Question Answering
Feature Extraction
Modalities:
Document
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
structured-extraction
information-extraction
document-understanding
pdf
resume
json-schema
Libraries:
Datasets
pandas
Polars
License:
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ResumeExtractBench / README.md
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metadata 
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

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

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

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

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.

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.

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.

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).

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.