CareerBERT Classifier

A text classification model fine-tuned for career-related text analysis. Classifies job-description-relevant texts with 1, non-relevant texts with 0

Corresponding Paper:

https://www.sciencedirect.com/science/article/pii/S0957417425006657
arxiv.org/abs/2503.02056

Installation

Install the required dependencies:

pip install transformers torch

Quick Start

Load and use the model in a few lines:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import pipeline

modelpath = "lwolfrum2/careerbert-classifier"
model = AutoModelForSequenceClassification.from_pretrained(modelpath)
tokenizer = AutoTokenizer.from_pretrained(modelpath)
pipe = pipeline("text-classification", model, tokenizer=tokenizer)

# Classify text
result = pipe("Your text here")
print(result)

Usage

Simple Classification

# Single example
text = "I am looking for a job in software development."
result = pipe(text)
print(result)
# Output: [{'label': 'career_query', 'score': 0.98}]

Batch Processing

texts = [
    "Software engineer with 5 years experience",
    "Just looking for a new job",
    "Tell me about this coffee",
]

results = pipe(texts)
for text, result in zip(texts, results):
    print(f"{text} → {result['label']} ({result['score']:.2f})")

Output Format

Each prediction returns a dictionary with:

label: The predicted class (0 = not relevant, 1 = relevant)
score: Confidence score (0–1)

Notes

The model runs on CPU by default. For faster inference on large batches, use GPU:
```
pipe = pipeline("text-classification", model, tokenizer=tokenizer, device=0)
```
Texts longer than the model's max token length will be truncated.

Model Details

Model: lwolfrum2/careerbert-classifier
Base: BERT
Task: Text classification

Citing & Authors

@article{ROSENBERGER2025127043, title = {CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations}, journal = {Expert Systems with Applications}, volume = {275}, pages = {127043}, year = {2025}, issn = {0957-4174}, doi = {https://doi.org/10.1016/j.eswa.2025.127043}, url = {https://www.sciencedirect.com/science/article/pii/S0957417425006657}, author = {Julian Rosenberger and Lukas Wolfrum and Sven Weinzierl and Mathias Kraus and Patrick Zschech}, keywords = {Job consultation, Job markets, Job recommendation system, BERT, NLP}, abstract = {The rapidly evolving labor market, driven by technological advancements and economic shifts, presents significant challenges for traditional job matching and consultation services. In response, we introduce an advanced support tool for career counselors and job seekers based on CareerBERT, a novel approach that leverages the power of unstructured textual data sources, such as resumes, to provide more accurate and comprehensive job recommendations. In contrast to previous approaches that primarily focus on job recommendations based on a fixed set of concrete job advertisements, our approach involves the creation of a corpus that combines data from the European Skills, Competences, and Occupations (ESCO) taxonomy and EURopean Employment Services (EURES) job advertisements, ensuring an up-to-date and well-defined representation of general job titles in the labor market. Our two-step evaluation approach, consisting of an application-grounded evaluation using EURES job advertisements and a human-grounded evaluation using real-world resumes and Human Resources (HR) expert feedback, provides a comprehensive assessment of CareerBERT’s performance. Our experimental results demonstrate that CareerBERT outperforms both traditional and state-of-the-art embedding approaches while showing robust effectiveness in human expert evaluations. These results confirm the effectiveness of CareerBERT in supporting career consultants by generating relevant job recommendations based on resumes, ultimately enhancing the efficiency of job consultations and expanding the perspectives of job seekers. This research contributes to the field of NLP and job recommendation systems, offering valuable insights for both researchers and practitioners in the domain of career consulting and job matching.} }