Resume Parser with Hugging Face Spaces & Agentic‑Resume‑Parser

Introduction

Parsing a resume into structured data (like name, contact info, job titles, education) is hugely useful, whether you're building an ATS, automating HR intake, or organizing job applications. This guide walks you end‑to‑end using the Agentic‑Resume‑Parser Hugging Face Space, breaking concepts down for beginners. You’ll learn how it works, see detailed Python code, and understand each component step-by-step.

What is Agentic‑Resume‑Parser?

The Hugging Face Space csccorner/Agentic‑Resume‑Parser is a demo app that accepts PDF or DOCX resumes and outputs structured JSON with fields like:

• Name, email, phone

• Education history

• Work experience (company, dates, roles)

• Skills

It uses Hugging Face models (NER, zero-shot classification, layout-aware transformers) behind the scenes to extract data from semi-structured text (discuss.huggingface.co, huggingface.co, huggingface.co).

How It Works: High-Level Flow

1. Upload & Convert: The Space converts PDF and DOCX files into plain text.

2. Section Detection: Uses zero-shot classification (e.g., labels like Education, Work Experience) on text chunks.

3. NER Tagging: Runs a Named‑Entity Recognition model (e.g., BERT NER) to extract ORG, DATE, NAME, SKILL tokens.

4. Assembly: It groups NER output under detected sections to form a structured JSON.

Deep Dive into Key Concepts

❐ Zero‑Shot Classification

Allows you to categorize unlabeled chunks (e.g., “Harvard, 2010–2014”) into resume sections without training data.

from transformers import pipeline

classifier = pipeline("zero-shot-classification",
                      model="facebook/bart-large-mnli")
labels = ["Education", "Experience", "Skills", "Contact Info"]

def get_section(text):
    result = classifier(text, candidate_labels=labels)
    return result["labels"][0]  # top match

❐ Named‑Entity Recognition (NER)

Extracts entities like names, dates, and organizations:

ner = pipeline("ner", model="dslim/bert-base-NER", grouped_entities=True)

def extract_entities(text):
    return ner(text)  # returns list of {entity, score, word}

❐ Layout‑Aware Models

These (e.g., LayoutLM) use document structure—where text appears on the page—to improve extraction accuracy (discuss.huggingface.co).

Sample Code: From PDF to Parsed JSON

Here’s a Python script illustrating a simplified version:

from transformers import pipeline
import PyPDF2

# Load pipelines
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
ner = pipeline("ner", model="dslim/bert-base-NER", grouped_entities=True)

# PDF → text
def extract_pdf_text(path):
    text = ""
    for page in PyPDF2.PdfReader(path).pages:
        text += page.extract_text() + "\n"
    return text

def parse_resume(path):
    text = extract_pdf_text(path)
    lines = [l for l in text.splitlines() if l.strip()]

    sections = {"Education": [], "Experience": [], "Skills": [], "Contact Info": []}
    for line in lines:
        sec = classifier(line, labels)["labels"][0]
        entities = extract_entities(line)
        sections[sec].append({"text": line, "entities": entities})

    return sections

if __name__ == "__main__":
    json_out = parse_resume("my_resume.pdf")
    print(json_out)

👉 For production use, you’d group lines into paragraphs and refine entity mappings.

Benefits & Caveats

👍 Pros:

• No training data required

• Modular: swap model architectures

• Structured JSON—easy to ingest downstream

⚠️ Cons:

• May be misclassified with ambiguous formatting

• Entity boundaries aren’t always perfect

• LayoutLM is more accurate, but heavier and harder to deploy

Extending the Parser

• Add rule‑based checks: ensure email, phone, or dates exist; throw a warning if missing.

• Use layout information: integrate LayoutLM pipelines for PDF structure.

• Fine‑tune on your data: if you have labeled resumes, supervised training boosts performance.

Deploying with Agentic‑Resume‑Parser Space

Once set up locally, you can deploy on Hugging Face Spaces:

• Create a requirements.txt with transformers, torch, PyPDF2, etc.

• Build a Streamlit or Gradio front‑end for file uploads.

• Push to your HF repo; enable “Running” from the space UI.

You get full resume‑parsing functionality in the cloud, just like the reference Space.

Wrap‑Up

You now have in-depth knowledge and a working code to build your resume parser:

• Converting PDF content

• Section classification

• Named entity extraction

• Light pipeline assembly

• Tips for improving accuracy

This is a beginner-friendly way to bring NLP-powered resume analysis into your apps. Dive in, play around, and adapt it to your needs—whether for personal projects or real-world ATS.

You can have a live demo here or try here

Happy parsing! 😊