readme: ---
  license: apache-2.0
  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 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: 0.0453
  - Precision: 0.9275
  - Recall: 0.9492
  - F1: 0.9382
  - Accuracy: 0.9934
  
  ## 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: 30
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 407   | 0.0539          | 0.8283    | 0.8758 | 0.8514 | 0.9866   |
  | 0.1524        | 2.0   | 814   | 0.0333          | 0.8931    | 0.9123 | 0.9026 | 0.9915   |
  | 0.0381        | 3.0   | 1221  | 0.0345          | 0.8835    | 0.9280 | 0.9052 | 0.9906   |
  | 0.0179        | 4.0   | 1628  | 0.0351          | 0.8890    | 0.9361 | 0.9119 | 0.9909   |
  | 0.0089        | 5.0   | 2035  | 0.0310          | 0.9102    | 0.9372 | 0.9235 | 0.9924   |
  | 0.0089        | 6.0   | 2442  | 0.0344          | 0.9198    | 0.9383 | 0.9289 | 0.9922   |
  | 0.0057        | 7.0   | 2849  | 0.0331          | 0.9144    | 0.9448 | 0.9294 | 0.9931   |
  | 0.0039        | 8.0   | 3256  | 0.0340          | 0.9144    | 0.9481 | 0.9309 | 0.9928   |
  | 0.0027        | 9.0   | 3663  | 0.0423          | 0.9032    | 0.9481 | 0.9251 | 0.9921   |
  | 0.0018        | 10.0  | 4070  | 0.0373          | 0.9047    | 0.9507 | 0.9271 | 0.9923   |
  | 0.0018        | 11.0  | 4477  | 0.0448          | 0.8932    | 0.9474 | 0.9195 | 0.9910   |
  | 0.0014        | 12.0  | 4884  | 0.0380          | 0.9079    | 0.9474 | 0.9272 | 0.9928   |
  | 0.0015        | 13.0  | 5291  | 0.0360          | 0.9231    | 0.9474 | 0.9351 | 0.9936   |
  | 0.0013        | 14.0  | 5698  | 0.0378          | 0.9243    | 0.9456 | 0.9348 | 0.9935   |
  | 0.0013        | 15.0  | 6105  | 0.0414          | 0.9197    | 0.9496 | 0.9344 | 0.9930   |
  | 0.0009        | 16.0  | 6512  | 0.0405          | 0.9202    | 0.9478 | 0.9338 | 0.9929   |
  | 0.0009        | 17.0  | 6919  | 0.0385          | 0.9305    | 0.9441 | 0.9373 | 0.9933   |
  | 0.0006        | 18.0  | 7326  | 0.0407          | 0.9285    | 0.9437 | 0.9360 | 0.9934   |
  | 0.0009        | 19.0  | 7733  | 0.0428          | 0.9203    | 0.9488 | 0.9343 | 0.9929   |
  | 0.0006        | 20.0  | 8140  | 0.0455          | 0.9232    | 0.9536 | 0.9382 | 0.9928   |
  | 0.0004        | 21.0  | 8547  | 0.0462          | 0.9261    | 0.9529 | 0.9393 | 0.9930   |
  | 0.0004        | 22.0  | 8954  | 0.0423          | 0.9359    | 0.9492 | 0.9425 | 0.9940   |
  | 0.0005        | 23.0  | 9361  | 0.0446          | 0.9180    | 0.9529 | 0.9351 | 0.9931   |
  | 0.0005        | 24.0  | 9768  | 0.0430          | 0.9361    | 0.9467 | 0.9413 | 0.9938   |
  | 0.0002        | 25.0  | 10175 | 0.0436          | 0.9322    | 0.9496 | 0.9408 | 0.9935   |
  | 0.0002        | 26.0  | 10582 | 0.0440          | 0.9275    | 0.9492 | 0.9382 | 0.9935   |
  | 0.0002        | 27.0  | 10989 | 0.0450          | 0.9272    | 0.9488 | 0.9379 | 0.9932   |
  | 0.0002        | 28.0  | 11396 | 0.0445          | 0.9304    | 0.9470 | 0.9386 | 0.9935   |
  | 0.0003        | 29.0  | 11803 | 0.0449          | 0.9278    | 0.9481 | 0.9378 | 0.9934   |
  | 0.0001        | 30.0  | 12210 | 0.0453          | 0.9275    | 0.9492 | 0.9382 | 0.9934   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  