MultiClinNER EN Models

Clinical NER models for EN, trained with Multi-Head CRF architecture.

Best Model

Model: microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.25-P0.5-42
Best F1: 0.7368
Branch: main

Usage

# Load the best model (main branch)
from transformers import AutoTokenizer, AutoModelForTokenClassification

model = AutoModelForTokenClassification.from_pretrained("IEETA/MultiClinNER-EN")
tokenizer = AutoTokenizer.from_pretrained("IEETA/MultiClinNER-EN")

# Load a specific model variant
model = AutoModelForTokenClassification.from_pretrained("IEETA/MultiClinNER-EN", revision="BRANCH_NAME")

All Models (20 variants)

Downloads last month: 208

Safetensors

Model size

0.3B params

Tensor type

F32

Collection including IEETA/MultiClinNER-EN

MultiClinNER Models

Collection

Multi-Head CRF models for clinical Named Entity Recognition across 7 languages (CZ, EN, ES, IT, NL, RO, SV) plus multilingual and 3 entities. • 8 items • Updated 7 days ago • 1

Branch	Model	Best?
`main`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.25-P0.5-42`	Yes
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-pct0.2-P0.5-123`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-%0.2-P0.5-123`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-pct0.2-P0.5-42`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-%0.2-P0.5-42`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-pct0.2-P0.5-456`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-%0.2-P0.5-456`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-pct0.2-P0.5-999`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Anone-%0.2-P0.5-999`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.1-P0.5-123`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.1-P0.5-123`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.1-P0.5-42`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.1-P0.5-42`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.1-P0.5-456`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.1-P0.5-456`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.1-P0.5-999`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.1-P0.5-999`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.25-P0.5-123`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.25-P0.5-123`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.25-P0.5-456`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.25-P0.5-456`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-pct0.25-P0.5-999`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Arandom-%0.25-P0.5-999`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.2-123`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.2-123`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.2-42`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.2-42`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.2-456`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.2-456`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.2-999`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.2-999`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.5-123`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.5-123`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.5-42`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.5-42`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.5-456`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.5-456`
`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-pct0.25-P0.5-999`	`microsoft-BiomedNLP-PubMedBERT-large-uncased-abstract-C64-H3-E30-Aukn-%0.25-P0.5-999`