Resume NER Model

A fine-tuned BERT model for Named Entity Recognition (NER) specifically designed for resume/CV parsing and information extraction.

Model Description

This model is based on bert-base-cased and has been fine-tuned to extract key information from resume documents including:

label_to_id
id_to_label

Performance

Metric	Score
F1 Score	0.7128521806252412
Precision	0.6843275287143387
Recall	0.7438582360048329
Accuracy	0.9482567433286769

Usage

from transformers import pipeline

# Load the model
ner_pipeline = pipeline(
    "ner", 
    model="yashpwr/resume-ner-bert",
    aggregation_strategy="simple"
)

# Extract entities from resume text
text = "John Doe is a Software Engineer at Google. Email: [email protected]"
results = ner_pipeline(text)

for entity in results:
    print(f"{entity['word']}: {entity['entity_group']} ({entity['score']:.3f})")

Training Data

Training samples: 576
Validation samples: 144
Epochs: 3

Intended Use

This model is designed for:

Resume parsing systems
HR automation tools
Recruitment platforms
Document processing pipelines

Limitations

Optimized specifically for resume/CV documents
Performance may vary on other document types
Requires preprocessing for best results

Model Details

Base model: bert-base-cased
Model size: ~110M parameters
Language: English
License: Apache 2.0