readme: # BERT MCN-Model using SMM4H 2017 (subtask 3) data
  
  The model was trained using [clagator/biobert_v1.1_pubmed_nli_sts](https://huggingface.co/clagator/biobert_v1.1_pubmed_nli_sts) as a base and the smm4h dataset from 2017 from subtask 3. 
  
  ## Dataset
  
  See [here](https://github.com/olastor/medical-concept-normalization/tree/main/data/smm4h) for the scripts and datasets.
  
  **Attribution**
  
  Sarker, Abeed (2018), “Data and systems for medication-related text classification and concept normalization from Twitter: Insights from the Social Media Mining for Health (SMM4H)-2017 shared task”, Mendeley Data, V2, doi: 10.17632/rxwfb3tysd.2
  
  ### Test Results
  
  - Acc: 89.44 
  - Acc@2: 91.84 
  - Acc@3: 93.20
  - Acc@5: 94.32
  - Acc@10: 95.04
  
  Acc@N denotes the accuracy taking the top N predictions of the model into account, not just the first one. 