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