readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  language:
  - sr
  model_index:
  - name: distilbert-srb-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: sr
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9576561462374611
  ---
  
  <!-- 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. -->
  
  # distilbert-srb-ner
  
  This model was trained from scratch on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2972
  - Precision: 0.8871
  - Recall: 0.9100
  - F1: 0.8984
  - Accuracy: 0.9577
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3818        | 1.0   | 625   | 0.2175          | 0.8175    | 0.8370 | 0.8272 | 0.9306   |
  | 0.198         | 2.0   | 1250  | 0.1766          | 0.8551    | 0.8732 | 0.8640 | 0.9458   |
  | 0.1423        | 3.0   | 1875  | 0.1702          | 0.8597    | 0.8763 | 0.8679 | 0.9473   |
  | 0.079         | 4.0   | 2500  | 0.1774          | 0.8674    | 0.8875 | 0.8773 | 0.9515   |
  | 0.0531        | 5.0   | 3125  | 0.2011          | 0.8688    | 0.8965 | 0.8825 | 0.9522   |
  | 0.0429        | 6.0   | 3750  | 0.2082          | 0.8769    | 0.8970 | 0.8868 | 0.9538   |
  | 0.032         | 7.0   | 4375  | 0.2268          | 0.8764    | 0.8916 | 0.8839 | 0.9528   |
  | 0.0204        | 8.0   | 5000  | 0.2423          | 0.8726    | 0.8959 | 0.8841 | 0.9529   |
  | 0.0148        | 9.0   | 5625  | 0.2522          | 0.8774    | 0.8991 | 0.8881 | 0.9538   |
  | 0.0125        | 10.0  | 6250  | 0.2544          | 0.8823    | 0.9024 | 0.8922 | 0.9559   |
  | 0.0108        | 11.0  | 6875  | 0.2592          | 0.8780    | 0.9041 | 0.8909 | 0.9553   |
  | 0.007         | 12.0  | 7500  | 0.2672          | 0.8877    | 0.9056 | 0.8965 | 0.9571   |
  | 0.0048        | 13.0  | 8125  | 0.2714          | 0.8879    | 0.9089 | 0.8982 | 0.9583   |
  | 0.0049        | 14.0  | 8750  | 0.2872          | 0.8873    | 0.9068 | 0.8970 | 0.9573   |
  | 0.0034        | 15.0  | 9375  | 0.2915          | 0.8883    | 0.9114 | 0.8997 | 0.9577   |
  | 0.0027        | 16.0  | 10000 | 0.2890          | 0.8865    | 0.9103 | 0.8983 | 0.9581   |
  | 0.0028        | 17.0  | 10625 | 0.2885          | 0.8877    | 0.9085 | 0.8980 | 0.9576   |
  | 0.0014        | 18.0  | 11250 | 0.2928          | 0.8860    | 0.9073 | 0.8965 | 0.9577   |
  | 0.0013        | 19.0  | 11875 | 0.2963          | 0.8856    | 0.9099 | 0.8976 | 0.9576   |
  | 0.001         | 20.0  | 12500 | 0.2972          | 0.8871    | 0.9100 | 0.8984 | 0.9577   |
  
  
  ### Framework versions
  
  - Transformers 4.9.2
  - Pytorch 1.9.0
  - Datasets 1.11.0
  - Tokenizers 0.10.1
  