readme: ---
  language:
  - en
  - is
  - multilingual
  license: agpl-3.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: XLMR-ENIS-finetuned-ner
    results:
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - type: precision
        value: 0.9398313331170938
        name: Precision
      - type: recall
        value: 0.9517943664285128
        name: Recall
      - type: f1
        value: 0.9457750214207026
        name: F1
      - type: accuracy
        value: 0.9853686150987764
        name: Accuracy
  ---
  
  <!-- 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. -->
  
  # XLMR-ENIS-finetuned-ner
  
  This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0671
  - Precision: 0.9398
  - Recall: 0.9518
  - F1: 0.9458
  - Accuracy: 0.9854
  
  ## 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: 16
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2825        | 1.0   | 878  | 0.0712          | 0.9220    | 0.9379 | 0.9299 | 0.9815   |
  | 0.0688        | 2.0   | 1756 | 0.0689          | 0.9354    | 0.9477 | 0.9415 | 0.9839   |
  | 0.039         | 3.0   | 2634 | 0.0671          | 0.9398    | 0.9518 | 0.9458 | 0.9854   |
  
  
  ### Framework versions
  
  - Transformers 4.10.3
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  