Model Card for BioMed-NER-English

Fine-tuned BiomedNLP-BiomedBERT model for medical entity recognition, achieving 0.9868 F1-score on disease entity extraction from clinical text.

Model Details

Model Description

• Developed by: Aashish Acharya
• Model type: BiomedNLP-BiomedBERT (Token Classification)
• Language(s): English
• License: MIT
• Finetuned from model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext

Model Sources

• Base Model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
• Training Dataset: rjac/biobert-ner-diseases-dataset

Uses

Direct Use

This model excels at extracting disease mentions from medical text using BIO tagging scheme:

• B-Disease: Beginning of disease mention
• I-Disease: Continuation of disease mention
• O: Non-disease tokens

Training

Training Data

Dataset: biobert-ner-diseases-dataset Size: 21,225 annotated medical sentences Split: 15,488 training (73%), 5,737 testing (27%) Average sentence length: 24.3 tokens Disease mention frequency: 1.8 per sentence

Training Procedure Training Hyperparameters

• Learning rate: 5e-5
• Batch size: 8
• Epochs: 8
• Optimizer: AdamW with weight decay (0.01)
• Warmup steps: 500
• Early stopping patience: 5
• Loss function: Cross-entropy with label smoothing (0.1)
• Gradient accumulation steps: 4
• Max gradient norm: 1.0

Evaluation Metrics Final model performancc Strict Entity Matching:

Precision: 0.9869
Recall: 0.9868
F1 Score: 0.9868

Partial Entity Matching:

Precision: 0.9527
Recall: 0.9456
F1 Score: 0.9491

Error Analysis

Boundary Errors: 1,154
Type Errors: 0

Environmental Impact

Hardware Type: Google Colab GPU
Hours used: ~2 hours
Cloud Provider: Google Cloud
Carbon Emitted: Not tracked

Technical Specifications Model Architecture

Base model: PubMedBERT
Hidden size: 768
Attention heads: 12
Layers: 12
Parameters: ~110M

Compute Infrastructure

Platform: Google Colab
GPU: Tesla T4/P100

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("acharya-jyu/BioMed-NER-English")
model = AutoModelForTokenClassification.from_pretrained("acharya-jyu/BioMed-NER-English")

# Example usage
text = "Patient diagnosed with diabetes and hypertension"
tokens = tokenizer(text, return_tensors="pt")
outputs = model(**tokens)
predictions = torch.argmax(outputs.logits, dim=2)