readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner
    results: []
  ---
  
  <!-- 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-finetuned-ner
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6370
  - Precision: 0.5313
  - Recall: 0.4530
  - F1: 0.4891
  - Accuracy: 0.9290
  
  ## 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: 8
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 125  | 0.5387          | 0.2190    | 0.0552 | 0.0882 | 0.8991   |
  | No log        | 2.0   | 250  | 0.4241          | 0.3430    | 0.1750 | 0.2317 | 0.9117   |
  | No log        | 3.0   | 375  | 0.4721          | 0.3502    | 0.1786 | 0.2366 | 0.9088   |
  | 0.1529        | 4.0   | 500  | 0.6204          | 0.4300    | 0.2320 | 0.3014 | 0.9134   |
  | 0.1529        | 5.0   | 625  | 0.6479          | 0.4470    | 0.2486 | 0.3195 | 0.9104   |
  | 0.1529        | 6.0   | 750  | 0.4640          | 0.4532    | 0.4015 | 0.4258 | 0.9220   |
  | 0.1529        | 7.0   | 875  | 0.5170          | 0.4288    | 0.4217 | 0.4253 | 0.9224   |
  | 0.0229        | 8.0   | 1000 | 0.5846          | 0.5524    | 0.4273 | 0.4818 | 0.9233   |
  | 0.0229        | 9.0   | 1125 | 0.5569          | 0.4644    | 0.4328 | 0.4480 | 0.9234   |
  | 0.0229        | 10.0  | 1250 | 0.5818          | 0.5502    | 0.4438 | 0.4913 | 0.9258   |
  | 0.0229        | 11.0  | 1375 | 0.6183          | 0.5607    | 0.4254 | 0.4838 | 0.9231   |
  | 0.0048        | 12.0  | 1500 | 0.6148          | 0.5385    | 0.4254 | 0.4753 | 0.9250   |
  | 0.0048        | 13.0  | 1625 | 0.6271          | 0.4896    | 0.4328 | 0.4594 | 0.9255   |
  | 0.0048        | 14.0  | 1750 | 0.6475          | 0.5668    | 0.4217 | 0.4836 | 0.9267   |
  | 0.0048        | 15.0  | 1875 | 0.6428          | 0.5704    | 0.4328 | 0.4921 | 0.9282   |
  | 0.0016        | 16.0  | 2000 | 0.6577          | 0.5487    | 0.4254 | 0.4793 | 0.9270   |
  | 0.0016        | 17.0  | 2125 | 0.6688          | 0.5556    | 0.4144 | 0.4747 | 0.9262   |
  | 0.0016        | 18.0  | 2250 | 0.6481          | 0.5434    | 0.4383 | 0.4852 | 0.9282   |
  | 0.0016        | 19.0  | 2375 | 0.6432          | 0.5428    | 0.4438 | 0.4883 | 0.9289   |
  | 0.0007        | 20.0  | 2500 | 0.6370          | 0.5313    | 0.4530 | 0.4891 | 0.9290   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.8.0
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  