readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: test4
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ner
        type: ner
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.594855305466238
      - name: Recall
        type: recall
        value: 0.6423611111111112
      - name: F1
        type: f1
        value: 0.6176961602671119
      - name: Accuracy
        type: accuracy
        value: 0.9579571605593911
  ---
  
  <!-- 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. -->
  
  # test4
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3100
  - Precision: 0.5949
  - Recall: 0.6424
  - F1: 0.6177
  - Accuracy: 0.9580
  
  ## 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: 1e-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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 418  | 0.2052          | 0.2415    | 0.2465 | 0.2440 | 0.9423   |
  | 0.3341        | 2.0   | 836  | 0.1816          | 0.4286    | 0.4792 | 0.4525 | 0.9513   |
  | 0.1296        | 3.0   | 1254 | 0.2039          | 0.4589    | 0.5035 | 0.4801 | 0.9526   |
  | 0.0727        | 4.0   | 1672 | 0.2130          | 0.5237    | 0.5764 | 0.5488 | 0.9566   |
  | 0.0553        | 5.0   | 2090 | 0.2290          | 0.5171    | 0.5764 | 0.5452 | 0.9551   |
  | 0.0412        | 6.0   | 2508 | 0.2351          | 0.5390    | 0.5521 | 0.5455 | 0.9555   |
  | 0.0412        | 7.0   | 2926 | 0.2431          | 0.5280    | 0.5903 | 0.5574 | 0.9542   |
  | 0.0321        | 8.0   | 3344 | 0.2490          | 0.5825    | 0.625  | 0.6030 | 0.9570   |
  | 0.0249        | 9.0   | 3762 | 0.2679          | 0.5764    | 0.5764 | 0.5764 | 0.9573   |
  | 0.0192        | 10.0  | 4180 | 0.2574          | 0.5506    | 0.6042 | 0.5762 | 0.9558   |
  | 0.0206        | 11.0  | 4598 | 0.2857          | 0.5498    | 0.5938 | 0.5710 | 0.9559   |
  | 0.0147        | 12.0  | 5016 | 0.2638          | 0.5548    | 0.5972 | 0.5753 | 0.9550   |
  | 0.0147        | 13.0  | 5434 | 0.2771          | 0.5677    | 0.5972 | 0.5821 | 0.9577   |
  | 0.0129        | 14.0  | 5852 | 0.3016          | 0.5761    | 0.6181 | 0.5963 | 0.9549   |
  | 0.0118        | 15.0  | 6270 | 0.3055          | 0.5587    | 0.6111 | 0.5837 | 0.9570   |
  | 0.0099        | 16.0  | 6688 | 0.2937          | 0.5682    | 0.6076 | 0.5872 | 0.9564   |
  | 0.0099        | 17.0  | 7106 | 0.3075          | 0.5313    | 0.6181 | 0.5714 | 0.9531   |
  | 0.0085        | 18.0  | 7524 | 0.3079          | 0.6026    | 0.6424 | 0.6218 | 0.9580   |
  | 0.0085        | 19.0  | 7942 | 0.3082          | 0.5833    | 0.6319 | 0.6067 | 0.9572   |
  | 0.0074        | 20.0  | 8360 | 0.3100          | 0.5949    | 0.6424 | 0.6177 | 0.9580   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  