Update README.md

README.md

@@ -40,7 +40,7 @@ base_model: xlm-roberta-base
 
 This **multilingual clinical Named Entity Recognition (NER)** model is designed to identify **disease**, **symptom**, and **clinical procedure**  mentions in biomedical and clinical text. It is based on [`xlm-roberta-base`](https://huggingface.co/FacebookAI/xlm-roberta-base) and fine-tuned on translated variants of the clinical NER datasets **DisTEMIST**,  **SympTEMIST**, **MedProcNER**, and **CardioCCC**, which consist of clinical case reports with manually annotated mentions of three entity types, following a **multi-task learning (MTL)** approach and using the BIO tagging scheme for sequence labeling.
 
-The model consists of a **shared multilingual** encoder and a set of **entity-specific token classification heads**, each one being responsible for a different task. In this configuration, each classification head is trained on **entity-specific data from all supported languages**.
+The model consists of a **shared multilingual encoder** and a set of **entity-specific token classification heads**, each one being responsible for a different task. In this configuration, each classification head is trained on **entity-specific data from all supported languages**.
 
 - **Architecture:** Multi-task learning (MTL)  
 - **Training setup:** Multilingual, Multilabel  (DISEASE, SYMPTOM, PROCEDURE)