readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-uk-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-uk-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: 0.2396
  - Precision: 0.6410
  - Recall: 0.6370
  - F1: 0.6390
  - Accuracy: 0.9313
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.6689        | 1.0   | 725  | 0.3466          | 0.4923    | 0.4758 | 0.4839 | 0.9054   |
  | 0.3286        | 2.0   | 1450 | 0.2742          | 0.5723    | 0.5894 | 0.5807 | 0.9179   |
  | 0.2224        | 3.0   | 2175 | 0.2534          | 0.5884    | 0.6247 | 0.6060 | 0.9240   |
  | 0.2008        | 4.0   | 2900 | 0.2396          | 0.6410    | 0.6370 | 0.6390 | 0.9313   |
  | 0.1703        | 5.0   | 3625 | 0.2398          | 0.6336    | 0.6476 | 0.6405 | 0.9306   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  