resume-ner-bert-v2-onnx

Resume NER BERT v2 (ONNX)

Pre-exported ONNX version of yashpwr/resume-ner-bert-v2 for direct use without Python/PyTorch dependencies.

Converted using Optimum for use in lucidRESUME, a local-first resume analysis desktop app built with .NET and ONNX Runtime.

Model Details

Performance

Entity Types

The model recognises 12 entity types using BIO tagging (25 labels total):

Usage

With ONNX Runtime (.NET)

This model is used by lucidRESUME for resume entity extraction via ONNX Runtime in C#. The app downloads this model automatically on first launch -- no Python required.

With ONNX Runtime (Python)

from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("scottgal/resume-ner-bert-v2-onnx")
model = ORTModelForTokenClassification.from_pretrained("scottgal/resume-ner-bert-v2-onnx")

ner = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
results = ner("John Smith, Software Engineer at Google with 5 years of experience. BSc Computer Science from MIT.")
for entity in results:
    print(f"{entity['entity_group']}: {entity['word']} ({entity['score']:.2f})")

Training Data

The original model was trained on 22,542 samples:

• Resume-Corpus Dataset -- 349 samples
• DataTurks Resume NER -- 420 samples
• Custom Training Data -- 21,773 samples (rule-based extraction)
• Mehyaar Skills Dataset -- skills-focused data

See the original model card for full training details.

Limitations

• English only
• Best with text-based resumes (not scanned images)
• Primarily trained on technology and business resumes
• Optimal for resumes under 512 tokens (trained with max sequence length of 128)

Links

• Original model: yashpwr/resume-ner-bert-v2
• lucidRESUME: github.com/scottgal/lucidRESUME -- local-first desktop app for resume analysis, job matching, and career planning
• Conversion tool: Hugging Face Optimum