Challenges

Before our solution, the client’s data categorization system frequently misclassified personal names as corporate names and vice versa. This led to data inconsistencies, inaccurate reporting, regulatory compliance issues, and operational inefficiencies. The misclassification impacted everything from customer segmentation to internal reporting, creating bottlenecks that slowed decision-making and reduced the client’s ability to perform high-quality analytics.

Our Innovative Solution

To solve this, we employed DistilBERT, a high-performing natural language processing model renowned for its contextual understanding of language. We fine-tuned DistilBERT specifically for recognizing and classifying names, resulting in a specialized model named EntityMaster.

EntityMaster was trained on a large dataset of over 1 million individual names and 10,000 corporate names, ensuring robust performance across diverse datasets. The model was optimized for speed, scalability, and accuracy, meeting both current and future needs.

How We Developed

We began by gathering a balanced dataset that included a wide variety of personal and corporate names. This allowed us to train the model without bias, ensuring high accuracy. Once the data was prepared, we fine-tuned DistilBERT to recognize the patterns and characteristics that distinguish personal names from corporate names.

During the training process, we ran multiple tests and validations to fine-tune the model’s performance. Our goal was to create a solution that was not only accurate but also efficient enough to handle large volumes of data in real-time. After refining the model’s performance, we moved forward with integrating it into the client’s existing data infrastructure.

Results and Impact

Validation Run

Run	Accuracy	Precision	Recall	F1-Score	Support
Run 1	0.9969	0.99	0.98	0.99	3770
		1.00	1.00	1.00	28742
Run 2	0.9977	0.99	0.99	0.99	3756
		1.00	1.00	1.00	28756
Run 3	0.9983	0.99	0.99	0.99	3840
		1.00	1.00	1.00	28672
Run 4	0.9981	1.00	0.99	0.99	3737
		1.00	1.00	1.00	28775

The implementation of our fine-tuned DistilBERT model for entity recognition achieved outstanding validation accuracies, as seen in the table above. The model’s accuracy remained consistently above 99% across three validation runs. The precision, recall, and F1-scores remained high for both classes (individuals and corporations), underscoring the model's ability to correctly classify entities with near-perfect performance.

The attached charts further demonstrate the model’s robust training performance, with a steep decline in training loss over five epochs and near-perfect accuracy throughout validation runs:

Training Loss Chart

Validation Accuracy Chart

• Training Loss Chart:
  Demonstrates the rapid convergence of the model, as the loss decreases significantly within just five epochs.
• Validation Accuracy Chart:
  Highlights the model’s ability to maintain high accuracy, stabilizing around 99.8%.

The project was a success, with EntityMaster achieving an impressive 99% accuracy in identifying and categorizing names as either individuals or corporations. The deployment of this model marked a significant milestone, leading to substantial improvements in data processing accuracy. The enhanced entity recognition capabilities enabled more refined data analytics, better compliance with data handling regulations, and increased operational efficiency. The client reported heightened satisfaction with the system’s performance, reflecting the effectiveness of our fine-tuned DistilBERT model in meeting their sophisticated data analysis needs.