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tags:
- named-entity-recognition
- clinical-nlp
- multiclinner
- multi-head-crf
- token-classification
language:
- sv
license: apache-2.0
---
# MultiClinNER SV Models
Clinical NER models for SV, trained with Multi-Head CRF architecture.
## Best Model
- **Model**: `KB-C64-H3-E60-Arandom-%0.1-P0.5-42`
- **Best F1**: 0.7051
- **Branch**: `main`
## Usage
```python
# Load the best model (main branch)
from transformers import AutoTokenizer, AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("IEETA/MultiClinNER-SV")
tokenizer = AutoTokenizer.from_pretrained("IEETA/MultiClinNER-SV")
# Load a specific model variant
model = AutoModelForTokenClassification.from_pretrained("IEETA/MultiClinNER-SV", revision="BRANCH_NAME")
```
## All Models (11 variants)
| Branch | Model | Best? |
|--------|-------|-------|
| `main` | `KB-C64-H3-E60-Arandom-%0.1-P0.5-42` | **Yes** |
| `KB-C64-H3-E60-Arandom-pct0.1-P0.2-123` | `KB-C64-H3-E60-Arandom-%0.1-P0.2-123` | |
| `KB-C64-H3-E60-Arandom-pct0.1-P0.2-42` | `KB-C64-H3-E60-Arandom-%0.1-P0.2-42` | |
| `KB-C64-H3-E60-Arandom-pct0.1-P0.2-999` | `KB-C64-H3-E60-Arandom-%0.1-P0.2-999` | |
| `KB-C64-H3-E60-Arandom-pct0.1-P0.5-123` | `KB-C64-H3-E60-Arandom-%0.1-P0.5-123` | |
| `KB-C64-H3-E60-Arandom-pct0.25-P0.2-42` | `KB-C64-H3-E60-Arandom-%0.25-P0.2-42` | |
| `KB-C64-H3-E60-Arandom-pct0.25-P0.2-999` | `KB-C64-H3-E60-Arandom-%0.25-P0.2-999` | |
| `KB-C64-H3-E60-Aukn-pct0.1-P0.5-123` | `KB-C64-H3-E60-Aukn-%0.1-P0.5-123` | |
| `KB-C64-H3-E60-Aukn-pct0.1-P0.5-42` | `KB-C64-H3-E60-Aukn-%0.1-P0.5-42` | |
| `KB-C64-H3-E60-Aukn-pct0.25-P0.2-123` | `KB-C64-H3-E60-Aukn-%0.25-P0.2-123` | |
| `KB-C64-H3-E60-Aukn-pct0.25-P0.2-42` | `KB-C64-H3-E60-Aukn-%0.25-P0.2-42` | |
|