readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-sv-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-sv-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: 0.2611
  - Precision: 0.6693
  - Recall: 0.6664
  - F1: 0.6679
  - Accuracy: 0.9244
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.6807        | 1.0   | 804  | 0.3764          | 0.5263    | 0.5183 | 0.5223 | 0.8956   |
  | 0.2691        | 2.0   | 1608 | 0.2995          | 0.6437    | 0.6104 | 0.6266 | 0.9172   |
  | 0.2278        | 3.0   | 2412 | 0.2708          | 0.6494    | 0.6485 | 0.6489 | 0.9226   |
  | 0.1834        | 4.0   | 3216 | 0.2623          | 0.6753    | 0.6549 | 0.6650 | 0.9251   |
  | 0.161         | 5.0   | 4020 | 0.2611          | 0.6693    | 0.6664 | 0.6679 | 0.9244   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  