readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electramed-small-ADE-ner
    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. -->
  
  # electramed-small-ADE-ner
  
  This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1548
  - Precision: 0.8358
  - Recall: 0.9064
  - F1: 0.8697
  - Accuracy: 0.9581
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.5587        | 1.0   | 201  | 0.4107          | 0.7291    | 0.7982 | 0.7621 | 0.8983   |
  | 0.2114        | 2.0   | 402  | 0.2663          | 0.7716    | 0.8826 | 0.8234 | 0.9445   |
  | 0.1421        | 3.0   | 603  | 0.2183          | 0.8033    | 0.9030 | 0.8502 | 0.9488   |
  | 0.2204        | 4.0   | 804  | 0.1878          | 0.8279    | 0.9012 | 0.8630 | 0.9553   |
  | 0.5825        | 5.0   | 1005 | 0.1712          | 0.8289    | 0.8967 | 0.8615 | 0.9566   |
  | 0.0685        | 6.0   | 1206 | 0.1647          | 0.8333    | 0.9067 | 0.8685 | 0.9572   |
  | 0.0973        | 7.0   | 1407 | 0.1593          | 0.8365    | 0.9049 | 0.8693 | 0.9578   |
  | 0.1683        | 8.0   | 1608 | 0.1574          | 0.8367    | 0.9082 | 0.8710 | 0.9577   |
  | 0.065         | 9.0   | 1809 | 0.1557          | 0.8397    | 0.9052 | 0.8712 | 0.9583   |
  | 0.179         | 10.0  | 2010 | 0.1548          | 0.8358    | 0.9064 | 0.8697 | 0.9581   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  