Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- rigonsallauka/portugese_ner_dataset
+language:
+- pt
+metrics:
+- f1
+- precision
+- recall
+- confusion_matrix
+base_model:
+- google-bert/bert-base-cased
+pipeline_tag: token-classification
+tags:
+- NER
+- medical
+- symptoms
+- extraction
+- portugese
+---
+# Portugese 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 Portugese 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 Portugese.
+- **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
+
+## 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
+import torch
+
+model_name = "rigonsallauka/portugese_medical_ner"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Sample text for inference
+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."
+
+# Tokenize the input text
+inputs = tokenizer(text, return_tensors="pt")