---
language: en
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
tags:
- resume-parsing
- named-entity-recognition
- ner
- bert
- information-extraction
widget:
- text: "John Doe is a Software Engineer at Google. Email: john.doe@gmail.com, Phone: +1-555-123-4567"
  example_title: "Resume Information Extraction"
---

# 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

```python
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