readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: all-15-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. -->
  
  # all-15-bert-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0081
  - Precision: 0.9630
  - Recall: 0.9661
  - F1: 0.9646
  - Accuracy: 0.9987
  
  ## 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: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.014         | 1.0   | 6693   | 0.0080          | 0.9048    | 0.9363 | 0.9203 | 0.9976   |
  | 0.007         | 2.0   | 13386  | 0.0070          | 0.9116    | 0.9459 | 0.9284 | 0.9976   |
  | 0.0034        | 3.0   | 20079  | 0.0050          | 0.9514    | 0.9529 | 0.9522 | 0.9985   |
  | 0.0027        | 4.0   | 26772  | 0.0065          | 0.9360    | 0.9618 | 0.9487 | 0.9982   |
  | 0.002         | 5.0   | 33465  | 0.0062          | 0.9485    | 0.9555 | 0.9520 | 0.9984   |
  | 0.0008        | 6.0   | 40158  | 0.0069          | 0.9498    | 0.9468 | 0.9483 | 0.9983   |
  | 0.0013        | 7.0   | 46851  | 0.0059          | 0.9591    | 0.9618 | 0.9605 | 0.9987   |
  | 0.0007        | 8.0   | 53544  | 0.0072          | 0.9635    | 0.9594 | 0.9614 | 0.9986   |
  | 0.0003        | 9.0   | 60237  | 0.0076          | 0.9656    | 0.9621 | 0.9638 | 0.9987   |
  | 0.0006        | 10.0  | 66930  | 0.0080          | 0.9598    | 0.9625 | 0.9611 | 0.9986   |
  | 0.0007        | 11.0  | 73623  | 0.0072          | 0.9584    | 0.9651 | 0.9618 | 0.9986   |
  | 0.0           | 12.0  | 80316  | 0.0073          | 0.9606    | 0.9658 | 0.9632 | 0.9987   |
  | 0.0001        | 13.0  | 87009  | 0.0072          | 0.9649    | 0.9636 | 0.9642 | 0.9987   |
  | 0.0           | 14.0  | 93702  | 0.0078          | 0.9629    | 0.9665 | 0.9647 | 0.9987   |
  | 0.0           | 15.0  | 100395 | 0.0081          | 0.9630    | 0.9661 | 0.9646 | 0.9987   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  