readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-multilingual-cased-finetuned-ner-4
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # bert-base-multilingual-cased-finetuned-ner-4
  #This model is part of a test for creating multilingual BioMedical NER systems. Not intended for proffesional use now.
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the CRAFT+BC4CHEMD+BioNLP09 datasets concatenated.
  It achieves the following results on the evaluation set:
  - Loss: 0.1027
  - Precision: 0.9830
  - Recall: 0.9832
  - F1: 0.9831
  - Accuracy: 0.9799
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0658        | 1.0   | 6128  | 0.0751          | 0.9795    | 0.9795 | 0.9795 | 0.9758   |
  | 0.0406        | 2.0   | 12256 | 0.0753          | 0.9827    | 0.9815 | 0.9821 | 0.9786   |
  | 0.0182        | 3.0   | 18384 | 0.0934          | 0.9834    | 0.9825 | 0.9829 | 0.9796   |
  | 0.011         | 4.0   | 24512 | 0.1027          | 0.9830    | 0.9832 | 0.9831 | 0.9799   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  