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: 0.8926985693142575
      - name: Recall
        type: recall
        value: 0.8648584060222249
      - name: F1
        type: f1
        value: 0.8785579899253504
      - name: Accuracy
        type: accuracy
        value: 0.985303647287535
  ---
  
  <!-- 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: 0.0799
  - Precision: 0.8927
  - Recall: 0.8649
  - F1: 0.8786
  - Accuracy: 0.9853
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0528        | 1.0   | 2904 | 0.0774          | 0.8784    | 0.8529 | 0.8655 | 0.9829   |
  | 0.0258        | 2.0   | 5808 | 0.0742          | 0.8769    | 0.8705 | 0.8737 | 0.9843   |
  | 0.0166        | 3.0   | 8712 | 0.0799          | 0.8927    | 0.8649 | 0.8786 | 0.9853   |
  
  
  ### Framework versions
  
  - Transformers 4.11.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  