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---
license: apache-2.0
datasets:
- rigonsallauka/polish_ner_dataset
language:
- pl
metrics:
- f1
- recall
- precision
- confusion_matrix
base_model:
- google-bert/bert-base-cased
pipeline_tag: token-classification
tags:
- NER
- medical
- extraction
- symptom
- polish
---
# Polish Medical NER
## Acknowledgement
This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Sk艂odowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.
Authors:
dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz,聽dr.聽Matej聽Rojc
## Use
- **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Polish language.
- **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
- **Supported Entity Types**:
- `
PROBLEM`: Diseases, symptoms, and medical conditions.
- `TEST`: Diagnostic procedures and laboratory tests.
- `TREATMENT`: Medications, therapies, and other medical interventions.
## Training Data
- **Data Sources**: Annotated datasets, including clinical data and translations of English medical text into Polish.
- **Data Augmentation**: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures.
- **Dataset Split**:
- **Training Set**: 80%
- **Validation Set**: 10%
- **Test Set**: 10%
## Model Training
- **Training Configuration**:
- **Optimizer**: AdamW
- **Learning Rate**: 3e-5
- **Batch Size**: 64
-
**Epochs**: 200
- **Loss Function**: Focal Loss to handle class imbalance
- **Frameworks**: PyTorch, Hugging Face Transformers, SimpleTransformers
## Evaluation metrics
- eval_loss = 0.3968946770636102
- f1_score = 0.7556232119891866
- precision = 0.7552069671056083
- recall = 0.7560399159663865
Visit [HUMADEX/Weekly-Supervised-NER-pipline](https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) for more info.
## How to Use
You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
model_name = "rigonsallauka/polish_medical_ner"
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
# Sample text for inference
text = "Pacjent skar偶y艂 si臋 na silne b贸le g艂owy i nudno艣ci, kt贸re utrzymywa艂y si臋 przez dwa dni. W celu z艂agodzenia objaw贸w przepisano mu paracetamol oraz zalecono odpoczynek i picie du偶ej ilo艣ci p艂yn贸w."
# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt") |