metadata

license: apache-2.0
datasets:
  - rigonsallauka/greek_ner_dataset
language:
  - el
metrics:
  - f1
  - precision
  - recall
  - confusion_matrix
base_model:
  - google-bert/bert-base-cased
pipeline_tag: token-classification
tags:
  - NER
  - medical
  - symptom
  - extraction
  - greek

Greek Medical NER

Use

Primary Use Case: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Greek 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 Greek.
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 = 0.4112480320792267
f1_score = 0.6910085729376871
precision = 0.7068717096148518
recall = 0.675841788751424

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:

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_name = "rigonsallauka/greek_medical_ner"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

# Sample text for inference
text = "Ο ασθενής παραπονέθηκε για έντονους πονοκεφάλους και ναυτία που διαρκούσαν δύο ημέρες. Για την ανακούφιση των συμπτωμάτων, του χορηγήθηκε παρακεταμόλη και του συστήθηκε να ξεκουραστεί και να πίνει πολλά υγρά."

# Tokenize the input text
inputs = tokenizer(text, return_tensors="pt")