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| title: Clinical NER Pipeline Comparison | |
| emoji: 🧠 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| license: gpl-3.0 | |
| short_description: Comparison of strategies for NER. | |
| # Clinical NER Pipeline Comparison | |
| This demo compares three approaches to clinical entity recognition: | |
| 1. Fine-tuned clinical BERT (NER) | |
| 2. Vanilla BERT embeddings + similarity | |
| 3. Static Word2Vec embeddings + similarity | |
| The goal is to demonstrate why **fine-tuning and context matter**. | |
| ## How to use | |
| - Select a predefined sentence or type your own | |
| - Adjust prototype words if desired | |
| - Click **Execute** | |
| - Compare the outputs of the three pipelines | |
| ## Notes | |
| - Word2Vec is large and may take time to load on first run | |
| - This is a didactic comparison, not a production NER system | |