---
datasets:
- dataturks/resume-entities-for-ner
language:
- en
license: apache-2.0
metrics:
- f1
- precision
- recall
model-index:
- name: resume-ner-distilbert
  results:
  - dataset:
      name: Resume NER
      type: dataturks/resume-entities-for-ner
    metrics:
    - name: F1
      type: f1
      value: 0.211
    - name: Precision
      type: precision
      value: 0.2189
    - name: Recall
      type: recall
      value: 0.2037
    task:
      name: Named Entity Recognition
      type: token-classification
pipeline_tag: token-classification
tags:
- ner
- token-classification
- resume
- nlp
- transformers
- lora
---

# Resume Ner Distilbert

## Model Description

This is a fine-tuned Named Entity Recognition (NER) model for extracting structured information from resumes.
The model is trained using **Fine-tuning with LoRA** on a distilbert-base-uncased backbone.

### Supported Entity Types

| Entity Type | Description | Example |
|-------------|-------------|---------|
| NAME | Person's name | "John Doe" |
| EMAIL | Email address | "john@example.com" |
| PHONE | Phone number | "+1 555-123-4567" |
| LOCATION | Geographic location | "San Francisco, CA" |
| ORG | Organization/Company | "Google Inc." |
| TITLE | Job title | "Senior Software Engineer" |
| DEGREE | Academic degree | "Bachelor of Science in Computer Science" |
| SKILL | Technical or soft skill | "Python", "Machine Learning" |
| CERT | Certification | "AWS Solutions Architect" |
| DATE | Date or time period | "2020-2023", "January 2022" |

## Training Details

### Training Data
- **Dataset**: Dataturks Resume Entities for NER
- **Source**: [Kaggle](https://www.kaggle.com/datasets/dataturks/resume-entities-for-ner)
- **Training Examples**: N/A
- **Validation Examples**: N/A

### Training Configuration
- **Base Model**: distilbert-base-uncased
- **Training Method**: Fine-tuning with LoRA
- **Epochs**: 10
- **Learning Rate**: 3e-05
- **Batch Size**: 8
- **Max Sequence Length**: 512
- **Random Seed**: 42

### Performance Metrics

| Metric | Validation Set |
|--------|----------------|
| Precision | 0.2189 |
| Recall | 0.2037 |
| F1-Score | 0.2110 |

## Usage

### Using Transformers Pipeline

```python
from transformers import pipeline

# Load the model
ner = pipeline("ner", model="Joshuant/resume-ner-distilbert", aggregation_strategy="simple")

# Extract entities from resume text
text = """
John Doe
Email: john.doe@email.com
Phone: +1 555-123-4567

EDUCATION
Bachelor of Science in Computer Science, MIT, 2020

EXPERIENCE
Senior Software Engineer at Google, 2020-2023
- Developed ML pipelines using Python and TensorFlow
"""

entities = ner(text)
for entity in entities:
    print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")
```

### Using AutoModel

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Joshuant/resume-ner-distilbert")
model = AutoModelForTokenClassification.from_pretrained("Joshuant/resume-ner-distilbert")

# Tokenize input
text = "John Doe, Senior Software Engineer at Google"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=-1)

# Decode predictions
tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
labels = [model.config.id2label[p.item()] for p in predictions[0]]

for token, label in zip(tokens, labels):
    if label != "O":
        print(f"{token}: {label}")
```

## Limitations

- The model is primarily trained on English resumes and may not perform well on other languages
- Performance may vary based on resume formatting and structure
- The model may struggle with unusual entity formats or domain-specific terminology

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{resume_ner_slm_2026,
    title={Context-Aware Resume NER with Small Language Models},
    author={Research Team},
    year={2026},
    howpublished={\url{https://huggingface.co/Joshuant/resume-ner-distilbert}}
}
```

## License

This model is released under the apache-2.0 license.

## Acknowledgments

- Dataturks for the original Resume NER dataset
- Hugging Face for the transformers library and model hosting
- The open-source NLP community

---

*Model trained on 2026-01-08*