Spaces:
Sleeping
Sleeping
File size: 787 Bytes
180d0b8 2272891 180d0b8 2272891 180d0b8 2272891 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ---
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
|