readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: test-bert-finetuned-ner
    results:
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - type: precision
        value: 0.9354625186165811
        name: Precision
      - type: recall
        value: 0.9513631773813531
        name: Recall
      - type: f1
        value: 0.943345848977889
        name: F1
      - type: accuracy
        value: 0.9867545770294931
        name: Accuracy
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: test
      metrics:
      - type: accuracy
        value: 0.9003797607979704
        name: Accuracy
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGVlNjEyMTJmOTBhMmE1NjY1ODA3MTE0ZjM1YjU5Mzk2ZTY1NWE2MTZiMGMxZTRiNDNjNzNiYzI2NzZiMzAxMiIsInZlcnNpb24iOjF9.ScTPJWA72u8-LTp78w7U8teH-TXdyWnoz4vnK-1TefERahcKQ51eekHI_2xjOPe-1uQmw5z8rKTZfh3MOv-HCw
      - type: precision
        value: 0.9286807108391197
        name: Precision
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjc0OGM4MTQ0OGM3NzA1ZTJmODg4YmJiZTZjOTVkZWYzZGYxZGYzZThhYzRkMzAxOWNhZmQ0NmJhNTMxZGI4MCIsInZlcnNpb24iOjF9.vloc_Hl4_UmVHUMTN2utIKJ2gYntSlZVuVJNkeGn-fR9SeRbKzmkBds4GQNjsV0JiVmnX0POB1hUqRGP4UjdAg
      - type: recall
        value: 0.9158238551580065
        name: Recall
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzE2ZGIwNTAzNDhkMDc0MmU2NTQ2MjIyNjA0NzI0N2JiNDM3NjgxNTU3YmNiNWIwOTRmYzNkMTE0MmUyOTNhNiIsInZlcnNpb24iOjF9.-mi3lImJs1-993tdLiTL7KGFEb-jZJVrviqUlFaVY0rgkojDvRyhbUBnJoD4dadh728kRDTH5NW-ZKb9B9FTDg
      - type: f1
        value: 0.9222074745602832
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGE1ODE0MGUzZmFhZTNhOWMwMzk3NzQ5MTQwOWIyNjAxZWUwMDgzNDBlNGIyNmY4YmQ4ZDRmOTljZmYyNGYzOCIsInZlcnNpb24iOjF9.PjQJinFobofJhCpsTLEuMSjsskLfbOmAPPQVGWBGk7jYOi3lvd9CUn9i_g1GlbbxuxmO1L9sMAj-pANn-aQiAA
      - type: loss
        value: 0.8705922365188599
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGI2YTU4ZmExYmZmMjBmMjM3ZWJhNDA0OGMwZjM4YWE4MjU1YmFjMTQxMjQ5MDlhNzYzYTBmYTc3YzRkN2UwOCIsInZlcnNpb24iOjF9.iyuIRW9M-yknXWi2Whboo-rjzicgxSGaeCpypgiQVYexjenzA5itKt_CDx52t7508zYshp-1ERnEHuEwBic9Aw
  ---
  
  <!-- 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. -->
  
  # test-bert-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0600
  - Precision: 0.9355
  - Recall: 0.9514
  - F1: 0.9433
  - Accuracy: 0.9868
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0849        | 1.0   | 1756 | 0.0713          | 0.9144    | 0.9366 | 0.9253 | 0.9817   |
  | 0.0359        | 2.0   | 3512 | 0.0658          | 0.9346    | 0.9500 | 0.9422 | 0.9860   |
  | 0.0206        | 3.0   | 5268 | 0.0600          | 0.9355    | 0.9514 | 0.9433 | 0.9868   |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.8.1+cu111
  - Datasets 1.12.1.dev0
  - Tokenizers 0.10.3
  