readme: ---
  license: gpl-3.0
  tags:
  - generated_from_trainer
  datasets:
  - mim_gold_ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: IceBERT-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: mim_gold_ner
        type: mim_gold_ner
        args: mim-gold-ner
      metrics:
      - name: Precision
        type: precision
        value: 0.8870349771350884
      - name: Recall
        type: recall
        value: 0.8575696021029992
      - name: F1
        type: f1
        value: 0.8720534629404617
      - name: Accuracy
        type: accuracy
        value: 0.9848236357672584
  ---
  
  <!-- 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. -->
  
  # IceBERT-finetuned-ner
  
  This model is a fine-tuned version of [vesteinn/IceBERT](https://huggingface.co/vesteinn/IceBERT) on the mim_gold_ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0815
  - Precision: 0.8870
  - Recall: 0.8576
  - F1: 0.8721
  - Accuracy: 0.9848
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0536        | 1.0   | 2904 | 0.0749          | 0.8749    | 0.8426 | 0.8585 | 0.9831   |
  | 0.0269        | 2.0   | 5808 | 0.0754          | 0.8734    | 0.8471 | 0.8600 | 0.9840   |
  | 0.0173        | 3.0   | 8712 | 0.0815          | 0.8870    | 0.8576 | 0.8721 | 0.9848   |
  
  
  ### Framework versions
  
  - Transformers 4.11.0
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  