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README.md

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+---
+language: multilingual
+task_ids:
+- token-classification
+tags:
+- clinical, medical, ner, healthcare
+---
+
+## Model Card for Multilingual-Symptom-Xlm-Roberta-Large-Multilabel
+
+### Description
+
+Multilingual clinical NER model for symptom recognition
+
+### Model Details
+
+- **Language**: MULTILINGUAL
+- **Task**: token-classification
+- **Framework**: pytorch
+
+### How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+model_name = "DT4H/multilingual-symptom-xlm-roberta-large-multilabel"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+```
+
+### Citation
+
+```bibtex
+@inproceedings{danu2026siemens,
+  title={SIEMENS at\# SMM4H--HeaRD 2026: The Impact of Training Strategy and Backbone Selection on BERT-based Multilingual Clinical NER},
+  author={Danu, Manuela Daniela},
+  booktitle={Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks},
+  pages={216--221},
+  year={2026}
+}
+```
+
+### Acknowledgments
+This work received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101057849 (DataTools4Heart project).