resume-ner-distilbert

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

Training Details

Training Data

• Dataset: Dataturks Resume Entities for NER
• Source: Kaggle
• 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

Usage

Using Transformers Pipeline

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: [email protected]
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

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:

@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

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Model trained on 2026-01-08