Custom NER Dramatically Outperforms Open-Source in Domain-Specific Text
Fine-tuned named entity recognition on pharma-domain market research transcripts vs. leading open-source pre-trained models:
| Entity Type | Custom (Precision/Recall) | Open-Source (Precision/Recall) |
|---|---|---|
| Person | 0.98 / 0.99 | 0.47 / 0.65 |
| Location | 1.00 / 0.98 | 0.22 / 0.90 |
| Organization | 0.98 / 0.98 | 0.06 / 0.45 |
| Drug | 0.99 / 0.99 | 0.86 / 0.58 |
| Disease | 0.99 / 0.99 | 0.90 / 0.96 |
The gap for Person entities is striking: 0.98 vs 0.47 precision. Open-source models generate massive false positives when domain-specific vocabulary (protein names, gene markers like “HER-2-negative”) gets confused with person names.
The lesson: for high-stakes NER tasks (PII redaction, compliance), off-the-shelf models aren’t enough. Domain-specific fine-tuning is essential, and the investment pays off dramatically.
Related: [None yet]