readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner3
    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-ner3
  
  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: 0.0002
  - Precision: 0.9795
  - Recall: 0.9922
  - F1: 0.9858
  - Accuracy: 0.9999
  - Name Precision: 0.9892
  - Name Recall: 0.9946
  - Name F1: 0.9919
  - Dob Precision: 0.9707
  - Dob Recall: 0.9900
  - Dob F1: 0.9803
  
  ## 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 | Name Precision | Name Recall | Name F1 | Dob Precision | Dob Recall | Dob F1 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|
  | No log        | 1.0   | 108  | 0.0024          | 0.7581    | 0.9766 | 0.8536 | 0.9989   | 0.9579         | 0.9891      | 0.9733  | 0.6340        | 0.9652     | 0.7653 |
  | No log        | 2.0   | 216  | 0.0016          | 0.8519    | 0.9558 | 0.9009 | 0.9995   | 0.9624         | 0.9728      | 0.9676  | 0.7683        | 0.9403     | 0.8456 |
  | No log        | 3.0   | 324  | 0.0007          | 0.9193    | 0.9766 | 0.9471 | 0.9998   | 0.9784         | 0.9837      | 0.9810  | 0.8705        | 0.9701     | 0.9176 |
  | No log        | 4.0   | 432  | 0.0006          | 0.9305    | 0.9740 | 0.9518 | 0.9998   | 0.9838         | 0.9891      | 0.9864  | 0.8853        | 0.9602     | 0.9212 |
  | 0.0023        | 5.0   | 540  | 0.0007          | 0.8929    | 0.9532 | 0.9221 | 0.9997   | 0.9733         | 0.9891      | 0.9811  | 0.8259        | 0.9204     | 0.8706 |
  | 0.0023        | 6.0   | 648  | 0.0003          | 0.9646    | 0.9922 | 0.9782 | 0.9999   | 0.9892         | 0.9946      | 0.9919  | 0.9431        | 0.9900     | 0.9660 |
  | 0.0023        | 7.0   | 756  | 0.0006          | 0.9259    | 0.9740 | 0.9494 | 0.9998   | 0.9581         | 0.9946      | 0.976   | 0.8972        | 0.9552     | 0.9253 |
  | 0.0023        | 8.0   | 864  | 0.0007          | 0.9322    | 0.9636 | 0.9476 | 0.9998   | 0.9945         | 0.9891      | 0.9918  | 0.8791        | 0.9403     | 0.9087 |
  | 0.0023        | 9.0   | 972  | 0.0002          | 0.9821    | 0.9948 | 0.9884 | 0.9999   | 0.9946         | 0.9946      | 0.9946  | 0.9709        | 0.9950     | 0.9828 |
  | 0.0008        | 10.0  | 1080 | 0.0003          | 0.9646    | 0.9922 | 0.9782 | 0.9999   | 0.9946         | 0.9946      | 0.9946  | 0.9387        | 0.9900     | 0.9637 |
  | 0.0008        | 11.0  | 1188 | 0.0002          | 0.9846    | 0.9974 | 0.9910 | 0.9999   | 0.9946         | 0.9946      | 0.9946  | 0.9757        | 1.0        | 0.9877 |
  | 0.0008        | 12.0  | 1296 | 0.0002          | 0.9720    | 0.9922 | 0.9820 | 0.9999   | 0.9786         | 0.9946      | 0.9865  | 0.9660        | 0.9900     | 0.9779 |
  | 0.0008        | 13.0  | 1404 | 0.0002          | 0.9745    | 0.9922 | 0.9833 | 0.9999   | 0.9839         | 0.9946      | 0.9892  | 0.9660        | 0.9900     | 0.9779 |
  | 0.0004        | 14.0  | 1512 | 0.0002          | 0.9846    | 0.9974 | 0.9910 | 0.9999   | 0.9839         | 0.9946      | 0.9892  | 0.9853        | 1.0        | 0.9926 |
  | 0.0004        | 15.0  | 1620 | 0.0003          | 0.9744    | 0.9896 | 0.9820 | 0.9999   | 0.9786         | 0.9946      | 0.9865  | 0.9706        | 0.9851     | 0.9778 |
  | 0.0004        | 16.0  | 1728 | 0.0002          | 0.9769    | 0.9896 | 0.9832 | 0.9999   | 0.9838         | 0.9891      | 0.9864  | 0.9707        | 0.9900     | 0.9803 |
  | 0.0004        | 17.0  | 1836 | 0.0003          | 0.9693    | 0.9844 | 0.9768 | 0.9999   | 0.9892         | 0.9946      | 0.9919  | 0.9515        | 0.9751     | 0.9631 |
  | 0.0004        | 18.0  | 1944 | 0.0002          | 0.9846    | 0.9948 | 0.9897 | 1.0000   | 0.9892         | 0.9946      | 0.9919  | 0.9804        | 0.9950     | 0.9877 |
  | 0.0002        | 19.0  | 2052 | 0.0002          | 0.9795    | 0.9922 | 0.9858 | 0.9999   | 0.9892         | 0.9946      | 0.9919  | 0.9707        | 0.9900     | 0.9803 |
  | 0.0002        | 20.0  | 2160 | 0.0002          | 0.9795    | 0.9922 | 0.9858 | 0.9999   | 0.9892         | 0.9946      | 0.9919  | 0.9707        | 0.9900     | 0.9803 |
  
  
  ##