| --- |
| language: multilingual |
| task_ids: |
| - token-classification |
| tags: |
| - clinical, medical, ner, healthcare |
| --- |
| |
| ## Model Card for Multilingual-Symptom-Xlm-Roberta-Large-Multiclass |
|
|
| ### 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-multiclass-ner" |
| 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). |
|
|
