readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-de-aug-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. -->
  
  # xlm-roberta-base-de-aug-ner
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3820
  - Precision: 0.5214
  - Recall: 0.5660
  - F1: 0.5428
  - Accuracy: 0.8966
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 463  | 0.6140          | 0.2884    | 0.2925 | 0.2904 | 0.8438   |
  | 0.8329        | 2.0   | 926  | 0.4504          | 0.4092    | 0.4423 | 0.4251 | 0.8720   |
  | 0.4385        | 3.0   | 1389 | 0.4046          | 0.4634    | 0.5042 | 0.4829 | 0.8875   |
  | 0.3364        | 4.0   | 1852 | 0.3843          | 0.5       | 0.5446 | 0.5213 | 0.8954   |
  | 0.2919        | 5.0   | 2315 | 0.3820          | 0.5214    | 0.5660 | 0.5428 | 0.8966   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  