distilBERT-finetuned-resumes-sections
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | Geotrend/distilbert-base-en-fr-cased |
| Training Framework | PyTorch 1.12.1 |
| Performance Metrics | F1: 0.9652, ROC AUC: 0.9808, Accuracy: 0.9621 |
What is distilBERT-finetuned-resumes-sections?
This is a specialized natural language processing model fine-tuned for resume section classification. Built upon the distilBERT architecture, it has been optimized to accurately identify and classify different sections within resume documents. The model demonstrates exceptional performance with a 96.52% F1 score and 98.08% ROC AUC.
Implementation Details
The model was trained using a carefully crafted process over 20 epochs, utilizing the Adam optimizer with a learning rate of 2e-05. Training was conducted with batch sizes of 8 for both training and evaluation, implementing a linear learning rate scheduler.
- Training Framework: PyTorch with Transformers 4.21.1
- Optimizer: Adam (β1=0.9, β2=0.999, ε=1e-08)
- Training Duration: 20 epochs with 23,460 steps
- Final Loss: Training 0.0005, Validation 0.0381
Core Capabilities
- High-accuracy resume section classification
- Robust performance with 96.21% accuracy
- Efficient processing using distilBERT architecture
- Bilingual support (English-French) inherited from base model
Frequently Asked Questions
Q: What makes this model unique?
This model combines the efficiency of distilBERT with specialized fine-tuning for resume analysis, achieving remarkably high accuracy while maintaining computational efficiency. Its high F1 and ROC AUC scores indicate exceptional reliability in section classification tasks.
Q: What are the recommended use cases?
The model is ideal for automated resume parsing systems, HR technology platforms, and recruitment software that needs to accurately segment and classify different sections of resumes. It's particularly suited for applications requiring high-precision document structure analysis.
