readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlmr-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: et
      metrics:
      - name: Precision
        type: precision
        value: 0.9044097027481772
      - name: Recall
        type: recall
        value: 0.9136978539556626
      - name: F1
        type: f1
        value: 0.9090300532008596
      - name: Accuracy
        type: accuracy
        value: 0.9649304793632428
  ---
  
  <!-- 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-finetuned-ner
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1395
  - Precision: 0.9044
  - Recall: 0.9137
  - F1: 0.9090
  - Accuracy: 0.9649
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.4215        | 1.0   | 938  | 0.1650          | 0.8822    | 0.8781 | 0.8802 | 0.9529   |
  | 0.1559        | 2.0   | 1876 | 0.1412          | 0.9018    | 0.9071 | 0.9045 | 0.9631   |
  | 0.1051        | 3.0   | 2814 | 0.1395          | 0.9044    | 0.9137 | 0.9090 | 0.9649   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  