distilbert-resume-job-matcher

AI Resume–Job Description Semantic Matching System

Model Description

This repository contains a DistilBERT (distilbert-base-uncased) transformer model fine-tuned for sequence pair classification on candidate resumes and job descriptions.

Unlike traditional keyword-based Applicant Tracking Systems (ATS) that rely strictly on lexical overlap (BM25 / TF-IDF), this model captures deep semantic relationships, synonymy, domain equivalencies, and cross-functional skill alignments.

Intended Use

• Candidate Self-Assessment: Analyze compatibility with target job descriptions and identify missing skills.
• Recruiter Decision-Support: Assist human recruiters in prioritizing relevant candidate profiles.
• NLP Research & Education: Demonstration of fine-tuning Transformers on structured-unstructured text pairs.

Model Architecture

Resume [SEP] Job Description
         ↓
  DistilBERT Encoder (6 Layers, 768 Hidden Dim, 12 Heads)
         ↓
  [CLS] Representation
         ↓
  Pre-Classifier Dense Layer (ReLU + Dropout 0.2)
         ↓
  Classification Head (3 Output Logits)
         ↓
  Softmax Probabilities: [No Fit, Potential Fit, Good Fit]

Classes & Label Definitions

• Class 0 (No Fit): Cross-domain mismatch (e.g. Sales Representative vs Software Engineer) with negligible skill or domain relevance.
• Class 1 (Potential Fit): Adjacent role family or partial skill overlap (e.g. Data Analyst vs BI Analyst, or same role family with a seniority shift).
• Class 2 (Good Fit): Target role family, high core skill coverage (≥70%), and aligned experience/seniority level.

Training Details

• Base Architecture: distilbert-base-uncased
• Source Dataset: michaelozon/candidate-matching-synthetic (10,000 candidate profiles, 24 roles, 73 skills)
• Zero-Leakage Splitting: Resumes partitioned by candidate ID before pair generation (70% Train, 15% Val, 15% Test)
• Optimizer: AdamW (Learning Rate: 2e-5, Weight Decay: 0.01)
• Batch Size: 8–16 with Warmup
• Sequence Length: 512 (Max Truncation)
• Epochs: 2–3

Evaluation Results

How to Use with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "anshika-saklani/resume-job-distilbert"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

resume = "Senior Python Engineer with 6 years experience in Django, REST APIs, Docker, and PostgreSQL."
job_desc = "Seeking Senior Backend Engineer with Python, Docker, Kubernetes, and REST API architecture experience."

inputs = tokenizer(
    resume,
    job_desc,
    truncation=True,
    max_length=512,
    return_tensors="pt"
)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1).squeeze().tolist()

id2label = {0: "No Fit", 1: "Potential Fit", 2: "Good Fit"}
for idx, prob in enumerate(probs):
    print(f"{id2label[idx]}: {prob * 100:.2f}%")

Ethical Considerations & Limitations

• Synthetic Data: The training corpus is derived from synthetic candidate profiles. Real-world resumes contain unstructured formatting, idiosyncrasies, and regional terminology differences.
• Fairness & Non-Discrimination: This tool evaluates lexical and semantic relevance only. It MUST NOT be used as an automated hiring decision-maker.
• Truncation: Inputs exceeding 512 subword tokens are truncated, which may omit details in multi-page resumes.