readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-uk-base-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-uk-base-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: 0.2510
  - Precision: 0.5951
  - Recall: 0.6256
  - F1: 0.6100
  - Accuracy: 0.9264
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7224        | 1.0   | 514  | 0.3856          | 0.4590    | 0.4581 | 0.4586 | 0.8996   |
  | 0.3616        | 2.0   | 1028 | 0.2893          | 0.5528    | 0.5533 | 0.5531 | 0.9190   |
  | 0.2783        | 3.0   | 1542 | 0.2652          | 0.5661    | 0.5965 | 0.5809 | 0.9227   |
  | 0.2362        | 4.0   | 2056 | 0.2531          | 0.5882    | 0.6256 | 0.6063 | 0.9263   |
  | 0.2124        | 5.0   | 2570 | 0.2510          | 0.5951    | 0.6256 | 0.6100 | 0.9264   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  