yashpwr/resume-ner-bert

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:

• —labeltoid
• —idtolabel

Performance

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: john.doe@gmail.com"
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