Update README.md

README.md

@@ -32,15 +32,36 @@ In our paper, we outline the steps taken to train this model and demonstrate its
 ```python
 from transformers import pipeline
 
-# Load the pipeline
-model = pipeline("ner", model="pei-germany/MEDNER-de-fp-gbert", aggregation_strategy='simple')
+# Load the NER pipeline
+model = pipeline("ner", model="pei-germany/MEDNER-de-fp-gbert", aggregation_strategy="none")
 
 # Input text
-text="Der Patient bekam den COVID-Impfstoff und nahm danach Aspirin."
+text = "Der Patient wurde mit AstraZeneca geimpft und nahm anschließend Ibuprofen, um das Fieber zu senken."
+
+# Get raw predictions and merge subwords
+merged_predictions = []
+current = None
+
+for pred in model(text):
+    if pred['word'].startswith("##"):
+        if current:
+            current['word'] += pred['word'][2:]
+            current['end'] = pred['end']
+            current['score'] = (current['score'] + pred['score']) / 2
+    else:
+        if current:
+            merged_predictions.append(current)
+        current = pred.copy()
+
+if current:
+    merged_predictions.append(current)
+
+# Filter by confidence threshold and print
+threshold = 0.5
+filtered_predictions = [p for p in merged_predictions if p['score'] >= threshold]
+for p in filtered_predictions:
+    print(f"Entity: {p['entity']}, Word: {p['word']}, Score: {p['score']:.2f}, Start: {p['start']}, End: {p['end']}")
 
-# Get predictions
-predictions = model(text)
-print(predictions)
 ```