🎓 Resume & profile extraction

This tutorial walks through three production-style use cases for structured data extraction with the gliner2-base-v1 model. You will use plain text only (resumes, LinkedIn-style exports, job posts). Not OCR or PDF parsing.

All sample people and companies in the example dataset are synthetic and fictional.

🎥 Watch the Video Guide

What you will build

Step	Input	Output
1	Plain-text resume	`candidate` JSON (name, title, skills, …)
2	Scraped profile text	`profile` JSON (headline, role, location, …)
3	Job description	Labeled skill/tool spans (`ner`)

Model (steps 1-3): gliner2-base-v1
Endpoint: POST https://api.zerogpu.ai/v1/responses Get your API key and project ID from the dashboard. See Authentication.

Before you start

Clone or browse the example data and scripts in the cookbook repo:

demos/data-extraction/
├── dataset/          # resumes.jsonl, profiles.jsonl, job_posts.jsonl
├── schemas/          # resume.json, linkedin-profile.json
├── scripts/          # run_batch_extraction.py
└── blog-post.md      # companion article (optional read)

GitHub: zerogpu/cookbook/demos/data-extraction (see the README for setup).

Step 1: Extract a candidate from a resume

Open dataset/resumes.jsonl and pick resume-001 (synthetic). Define a schema under metadata.schema with use case json.

curl --location 'https://api.zerogpu.ai/v1/responses' \
  --header 'content-type: application/json' \
  --header 'x-api-key: YOUR_API_KEY' \
  --header 'x-project-id: YOUR_PROJECT_ID' \
  --data '{
    "model": "gliner2-base-v1",
    "input": "ALEX RIVERA\nSan Francisco, CA | [email protected] | (415) 555-0198\n\nSUMMARY\nBackend engineer with 7 years building APIs and data pipelines...",
    "metadata": {
      "usecase": "json",
      "schema": {
        "candidate": [
          "full_name::str::Candidate full name",
          "email::str::Email address",
          "phone::str::Phone number",
          "location::str::City, state, or country",
          "current_title::str::Most recent job title",
          "current_company::str::Most recent employer",
          "skills::str::Comma-separated key skills mentioned"
        ]
      }
    }
  }'

import json
import requests

url = "https://api.zerogpu.ai/v1/responses"
headers = {
    "content-type": "application/json",
    "x-api-key": "YOUR_API_KEY",
    "x-project-id": "YOUR_PROJECT_ID",
}

# First line of demos/data-extraction/dataset/resumes.jsonl (clone cookbook repo)
with open("resumes.jsonl", encoding="utf-8") as f:
    record = json.loads(f.readline())

payload = {
    "model": "gliner2-base-v1",
    "input": record["text"],
    "metadata": {
        "usecase": "json",
        "schema": {
            "candidate": [
                "full_name::str::Candidate full name",
                "email::str::Email address",
                "current_title::str::Most recent job title",
                "current_company::str::Most recent employer",
                "skills::str::Comma-separated key skills mentioned",
            ]
        },
    },
}
body = requests.post(url, headers=headers, json=payload).json()
parsed = json.loads(body["output"][0]["content"][0]["text"])
# GLiNER json responses are usually nested: data.candidate[0]
candidate = parsed["data"]["candidate"][0]
print(json.dumps(candidate, indent=2))

Check your result: You should see a candidate object (often under data.candidate[0]) with populated strings. If a field is missing from the source text, it may be empty. Tighten field descriptions or add a second pass only where needed.

Step 2: Structure LinkedIn-style profile text

Scrapers often return a single blob: headline, About, Experience. Use a profile schema (same json use case).

"schema": {
  "profile": [
    "name::str::Person name",
    "headline::str::Professional headline",
    "location::str::Profile location",
    "current_role::str::Current job title",
    "current_company::str::Current company"
  ]
}

Paste text from profiles.jsonl (profile-001) as input. Parse the response the same way as Step 1.

This tutorial assumes you already have compliant text from your pipeline. ZeroGPU does not scrape LinkedIn for you.

Step 3: Tag skills in a job post (NER)

When you need categories instead of fixed columns, set metadata.usecase to ner and pass labels:

curl --location 'https://api.zerogpu.ai/v1/responses' \
  --header 'content-type: application/json' \
  --header 'x-api-key: YOUR_API_KEY' \
  --header 'x-project-id: YOUR_PROJECT_ID' \
  --data '{
    "model": "gliner2-base-v1",
    "input": "We are hiring a Backend Engineer to design REST APIs in Python, deploy on AWS EKS, and maintain PostgreSQL databases.",
    "metadata": {
      "usecase": "ner",
      "labels": ["programming language", "database", "cloud platform", "framework"],
      "threshold": 0.3
    }
  }'

# Use job_posts.jsonl record + same headers as Step 1
payload = {
    "model": "gliner2-base-v1",
    "input": record["text"],
    "metadata": {
        "usecase": "ner",
        "labels": ["programming language", "database", "cloud platform", "certification"],
        "threshold": 0.3,
    },
}

Lower threshold to recall more spans; raise it when precision matters more.

Step 4: Run the full example dataset

Batch all synthetic resumes or profiles locally:

cd cookbook/demos/data-extraction/scripts
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
export ZEROGPU_API_KEY="zgpu-..."
export ZEROGPU_PROJECT_ID="your-project-uuid"
.venv/bin/python run_batch_extraction.py --dataset resumes --limit 3
.venv/bin/python run_batch_extraction.py --dataset profiles --limit 3
.venv/bin/python run_batch_extraction.py --dataset job_posts

Results are written to demos/data-extraction/outputs/*.jsonl. Inspect them before wiring into production ETL. For many files at once, combine this with the Batch API (parallelism, retries).

Optional: PII on inbound text

Before storing user-submitted resumes, run PII extraction with the gliner-multi-pii-v1 model and extract-pii or redact.

Production tips

Start small: 5-8 schema fields beat 20 vague ones.
Golden set: Keep 10-20 labeled examples from your real text shapes; re-run after schema changes.
Text only: PDF and image pipelines are out of scope for this tutorial; convert to text upstream.
Monitor: Use Logs and Usage in the dashboard.

Go deeper

gliner2-base-v1 model

API reference and schema examples for json and NER extraction.

Example dataset (GitHub)

Synthetic resumes, profiles, and job posts (JSONL).

Companion article

Long-form walkthrough in the cookbook repo.

gliner2-base-v1 playground

Try schemas interactively in the model catalog.

Cookbook

Tutorials

Integrations & plugins

🎓 Resume & profile extraction

🎥 Watch the Video Guide

What you will build

Before you start

Step 1: Extract a candidate from a resume

Step 2: Structure LinkedIn-style profile text

Step 3: Tag skills in a job post (NER)

Step 4: Run the full example dataset

Optional: PII on inbound text

Production tips

Go deeper

gliner2-base-v1 model

Example dataset (GitHub)

Companion article

gliner2-base-v1 playground

​🎥 Watch the Video Guide

​What you will build

​Before you start

​Step 1: Extract a candidate from a resume

​Step 2: Structure LinkedIn-style profile text

​Step 3: Tag skills in a job post (NER)

​Step 4: Run the full example dataset

​Optional: PII on inbound text

​Production tips

​Go deeper

gliner2-base-v1 model

Example dataset (GitHub)

Companion article

gliner2-base-v1 playground

🎥 Watch the Video Guide

What you will build

Before you start

Step 1: Extract a candidate from a resume

Step 2: Structure LinkedIn-style profile text

Step 3: Tag skills in a job post (NER)

Step 4: Run the full example dataset

Optional: PII on inbound text

Production tips

Go deeper