readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-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. -->
  
  # distilbert-base-uncased-finetuned-ner
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1080
  - Precision: 0.9631
  - Recall: 0.9740
  - F1: 0.9685
  - Accuracy: 0.9774
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 14
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1714        | 1.0   | 1492  | 0.1178          | 0.9209    | 0.9470 | 0.9337 | 0.9613   |
  | 0.1018        | 2.0   | 2984  | 0.1062          | 0.9391    | 0.9546 | 0.9468 | 0.9668   |
  | 0.0826        | 3.0   | 4476  | 0.0878          | 0.9460    | 0.9577 | 0.9518 | 0.9705   |
  | 0.0621        | 4.0   | 5968  | 0.0945          | 0.9507    | 0.9663 | 0.9584 | 0.9728   |
  | 0.0567        | 5.0   | 7460  | 0.0891          | 0.9562    | 0.9690 | 0.9625 | 0.9752   |
  | 0.0422        | 6.0   | 8952  | 0.0805          | 0.9572    | 0.9699 | 0.9635 | 0.9776   |
  | 0.0337        | 7.0   | 10444 | 0.0940          | 0.9575    | 0.9685 | 0.9629 | 0.9752   |
  | 0.0282        | 8.0   | 11936 | 0.0940          | 0.9584    | 0.9717 | 0.9650 | 0.9756   |
  | 0.0239        | 9.0   | 13428 | 0.0939          | 0.9579    | 0.9717 | 0.9647 | 0.9769   |
  | 0.0213        | 10.0  | 14920 | 0.0980          | 0.9618    | 0.9745 | 0.9681 | 0.9775   |
  | 0.0192        | 11.0  | 16412 | 0.1075          | 0.9606    | 0.9731 | 0.9668 | 0.9764   |
  | 0.0156        | 12.0  | 17904 | 0.1046          | 0.9623    | 0.9734 | 0.9678 | 0.9774   |
  | 0.0134        | 13.0  | 19396 | 0.1055          | 0.9614    | 0.9735 | 0.9674 | 0.9769   |
  | 0.0125        | 14.0  | 20888 | 0.1080          | 0.9631    | 0.9740 | 0.9685 | 0.9774   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.8.0
  - Tokenizers 0.12.1
  