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--- |
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license: apache-2.0 |
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datasets: |
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- rigonsallauka/portugese_ner_dataset |
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language: |
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- pt |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- confusion_matrix |
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base_model: |
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- google-bert/bert-base-cased |
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pipeline_tag: |
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token-classification |
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tags: |
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- NER |
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- medical |
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- symptoms |
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- extraction |
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- portugese |
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--- |
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# Portugese Medical NER |
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## Acknowledgement |
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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 Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors. |
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Authors: |
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dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc |
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## Use |
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- **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Portugese language. |
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- **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing. |
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- **Supported Entity Types**: |
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- `PROBLEM`: Diseases, symptoms, and medical conditions. |
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- `TEST`: Diagnostic procedures and laboratory tests. |
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- `TREATMENT`: Medications, therapies, and other medical interventions. |
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## Training Data |
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- **Data Sources**: Annotated datasets, including clinical data and translations of English medical text into Portugese. |
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- **Data Augmentation**: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures. |
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- **Dataset Split**: |
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- **Training Set**: 80% |
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- **Validation Set**: 10% |
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- **Test Set**: 10% |
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## Model Training |
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- **Training Configuration**: |
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- **Optimizer**: AdamW |
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- **Learning Rate**: 3e-5 |
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- **Batch Size**: 64 |
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- **Epochs**: 200 |
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- **Loss Function**: Focal Loss to handle class imbalance |
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- **Frameworks**: PyTorch, Hugging Face Transformers, SimpleTransformers |
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## Evaluation metrics |
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- eval_loss = 0.34290624315439794 |
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- f1_score = 0.7720704622812219 |
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- precision = 0.7724936121316581 |
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- recall = 0.7716477757556993 |
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Visit [HUMADEX/Weekly-Supervised-NER-pipline](https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) for more info. |
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## How to Use |
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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: |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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model_name = "rigonsallauka/portugese_medical_ner" |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForTokenClassification.from_pretrained(model_name) |
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# Sample text for inference |
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text = "O paciente reclamou de fortes dores de cabeça e náusea que persistiram por dois dias. Para aliviar os sintomas, foi prescrito paracetamol e recomendado descansar e beber bastante líquidos." |
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# Tokenize the input text |
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inputs = tokenizer(text, return_tensors="pt") |
