readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model_index:
  - name: bert-srb-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: sr
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9546696220907545
  ---
  
  <!-- 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. -->
  
  # bert-srb-ner
  
  This model was trained from scratch on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3561
  - Precision: 0.8909
  - Recall: 0.9082
  - F1: 0.8995
  - Accuracy: 0.9547
  
  ## 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: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3907        | 1.0   | 625   | 0.2316          | 0.8255    | 0.8314 | 0.8285 | 0.9259   |
  | 0.2091        | 2.0   | 1250  | 0.1920          | 0.8598    | 0.8731 | 0.8664 | 0.9420   |
  | 0.1562        | 3.0   | 1875  | 0.1833          | 0.8608    | 0.8820 | 0.8713 | 0.9441   |
  | 0.0919        | 4.0   | 2500  | 0.1985          | 0.8712    | 0.8886 | 0.8798 | 0.9476   |
  | 0.0625        | 5.0   | 3125  | 0.2195          | 0.8762    | 0.8923 | 0.8842 | 0.9485   |
  | 0.0545        | 6.0   | 3750  | 0.2320          | 0.8706    | 0.9004 | 0.8852 | 0.9495   |
  | 0.0403        | 7.0   | 4375  | 0.2459          | 0.8817    | 0.8957 | 0.8887 | 0.9505   |
  | 0.0269        | 8.0   | 5000  | 0.2603          | 0.8813    | 0.9021 | 0.8916 | 0.9516   |
  | 0.0193        | 9.0   | 5625  | 0.2916          | 0.8812    | 0.8949 | 0.8880 | 0.9500   |
  | 0.0162        | 10.0  | 6250  | 0.2938          | 0.8814    | 0.9025 | 0.8918 | 0.9520   |
  | 0.0134        | 11.0  | 6875  | 0.3330          | 0.8809    | 0.8961 | 0.8885 | 0.9497   |
  | 0.0076        | 12.0  | 7500  | 0.3141          | 0.8840    | 0.9025 | 0.8932 | 0.9524   |
  | 0.0069        | 13.0  | 8125  | 0.3292          | 0.8819    | 0.9065 | 0.8940 | 0.9535   |
  | 0.0053        | 14.0  | 8750  | 0.3454          | 0.8844    | 0.9018 | 0.8930 | 0.9523   |
  | 0.0038        | 15.0  | 9375  | 0.3519          | 0.8912    | 0.9061 | 0.8986 | 0.9539   |
  | 0.0034        | 16.0  | 10000 | 0.3437          | 0.8894    | 0.9038 | 0.8965 | 0.9539   |
  | 0.0024        | 17.0  | 10625 | 0.3518          | 0.8896    | 0.9072 | 0.8983 | 0.9543   |
  | 0.0018        | 18.0  | 11250 | 0.3572          | 0.8877    | 0.9072 | 0.8973 | 0.9543   |
  | 0.0015        | 19.0  | 11875 | 0.3554          | 0.8910    | 0.9081 | 0.8994 | 0.9549   |
  | 0.0011        | 20.0  | 12500 | 0.3561          | 0.8909    | 0.9082 | 0.8995 | 0.9547   |
  
  
  ### Framework versions
  
  - Transformers 4.9.2
  - Pytorch 1.9.0
  - Datasets 1.11.0
  - Tokenizers 0.10.1
  