Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 license: apache-2.0
 datasets:
-- rigonsallauka/english_ner_dataset
+- HUMADEX/english_ner_dataset
 language:
 - en
 metrics:
@@ -33,6 +33,7 @@ dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc
 - **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.
 
@@ -69,7 +70,7 @@ You can easily use this model with the Hugging Face `transformers` library. Here
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 
-model_name = "rigonsallauka/english_medical_ner"
+model_name = "HUMADEX/english_medical_ner"
 
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -79,5 +80,4 @@ model = AutoModelForTokenClassification.from_pretrained(model_name)
 text = "The patient complained of severe headaches and nausea that had persisted for two days. To alleviate the symptoms, he was prescribed paracetamol and advised to rest and drink plenty of fluids."
 
 # Tokenize the input text
-inputs = tokenizer(text, return_tensors="pt")
-
+inputs = tokenizer(text, return_tensors="pt")