readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: adr-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. -->
  
  # adr-ner
  
  This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0434
  - Precision: 0.7305
  - Recall: 0.6934
  - F1: 0.7115
  - Accuracy: 0.9941
  
  ## 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: 5e-05
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 107  | 0.0630          | 0.0       | 0.0    | 0.0    | 0.9876   |
  | No log        | 2.0   | 214  | 0.0308          | 0.4282    | 0.3467 | 0.3832 | 0.9900   |
  | No log        | 3.0   | 321  | 0.0254          | 0.5544    | 0.5603 | 0.5573 | 0.9920   |
  | No log        | 4.0   | 428  | 0.0280          | 0.6430    | 0.5751 | 0.6071 | 0.9929   |
  | 0.0465        | 5.0   | 535  | 0.0266          | 0.5348    | 0.7146 | 0.6118 | 0.9915   |
  | 0.0465        | 6.0   | 642  | 0.0423          | 0.7632    | 0.5793 | 0.6587 | 0.9939   |
  | 0.0465        | 7.0   | 749  | 0.0336          | 0.6957    | 0.6765 | 0.6860 | 0.9939   |
  | 0.0465        | 8.0   | 856  | 0.0370          | 0.6876    | 0.6702 | 0.6788 | 0.9936   |
  | 0.0465        | 9.0   | 963  | 0.0349          | 0.6555    | 0.7040 | 0.6789 | 0.9932   |
  | 0.0044        | 10.0  | 1070 | 0.0403          | 0.6910    | 0.6808 | 0.6858 | 0.9938   |
  | 0.0044        | 11.0  | 1177 | 0.0415          | 0.7140    | 0.6808 | 0.6970 | 0.9939   |
  | 0.0044        | 12.0  | 1284 | 0.0440          | 0.7349    | 0.6681 | 0.6999 | 0.9941   |
  | 0.0044        | 13.0  | 1391 | 0.0423          | 0.7097    | 0.6977 | 0.7036 | 0.9941   |
  | 0.0044        | 14.0  | 1498 | 0.0435          | 0.7174    | 0.6977 | 0.7074 | 0.9941   |
  | 0.0006        | 15.0  | 1605 | 0.0434          | 0.7305    | 0.6934 | 0.7115 | 0.9941   |
  
  
  ### Framework versions
  
  - Transformers 4.14.1
  - Pytorch 1.10.0+cu113
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  