readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: German-MedBERT-Birads-NER
    results: []
  widget:
  - text: "Beurteilung  Mammographisch beidseits kein Anhalt für Malignität.  Links ACR-Typ d.  Rechts BIRADS 4. Links BIRADS 1."
    example_title: "NER Example"
  ---
  
  <!-- 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. -->
  
  # German-MedBERT-Birads-NER
  
  This model is a fine-tuned version of [smanjil/German-MedBERT](https://huggingface.co/smanjil/German-MedBERT) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0617
  - Precision: 0.5
  - Recall: 0.6667
  - F1: 0.5714
  - Accuracy: 0.9987
  
  ## 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: 8
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 10   | 0.0125          | 1.0       | 1.0    | 1.0    | 1.0      |
  | No log        | 2.0   | 20   | 0.0718          | 0.5       | 0.6667 | 0.5714 | 0.9987   |
  | No log        | 3.0   | 30   | 0.0617          | 0.5       | 0.6667 | 0.5714 | 0.9987   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  