readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - generator
  model-index:
  - name: bert-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. -->
  
  # bert-base-uncased-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the generator dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9799
  - Overall Precision: 0.4362
  - Overall Recall: 0.5285
  - Overall F1: 0.4779
  - Overall Accuracy: 0.9515
  - Datasetname F1: 0.3262
  - Hyperparametername F1: 0.7339
  - Hyperparametervalue F1: 0.7619
  - Methodname F1: 0.5193
  - Metricname F1: 0.6525
  - Metricvalue F1: 0.6452
  - Taskname F1: 0.2704
  
  ## 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: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:|
  | No log        | 1.0   | 132  | 0.5733          | 0.2215            | 0.3313         | 0.2655     | 0.9350           | 0.0            | 0.1273                | 0.325                  | 0.4369        | 0.2759        | 0.3478         | 0.0639      |
  | No log        | 2.0   | 264  | 0.4130          | 0.2929            | 0.5996         | 0.3936     | 0.9348           | 0.2930         | 0.3316                | 0.6133                 | 0.5104        | 0.5622        | 0.5263         | 0.2452      |
  | No log        | 3.0   | 396  | 0.4654          | 0.3302            | 0.5793         | 0.4207     | 0.9431           | 0.2732         | 0.5                   | 0.7463                 | 0.5189        | 0.5930        | 0.5405         | 0.2228      |
  | 0.5901        | 4.0   | 528  | 0.4540          | 0.3604            | 0.5772         | 0.4438     | 0.9457           | 0.2644         | 0.5271                | 0.7027                 | 0.5346        | 0.6386        | 0.4878         | 0.2804      |
  | 0.5901        | 5.0   | 660  | 0.5776          | 0.3920            | 0.5346         | 0.4523     | 0.9483           | 0.2529         | 0.6241                | 0.7761                 | 0.5056        | 0.6369        | 0.6875         | 0.2601      |
  | 0.5901        | 6.0   | 792  | 0.4903          | 0.3934            | 0.6037         | 0.4763     | 0.9493           | 0.3651         | 0.6230                | 0.6667                 | 0.5775        | 0.6136        | 0.4848         | 0.3084      |
  | 0.5901        | 7.0   | 924  | 0.6343          | 0.3991            | 0.5549         | 0.4643     | 0.9492           | 0.3286         | 0.6047                | 0.6301                 | 0.5705        | 0.6216        | 0.5            | 0.2508      |
  | 0.1282        | 8.0   | 1056 | 0.6686          | 0.4185            | 0.5691         | 0.4823     | 0.9507           | 0.3546         | 0.6565                | 0.7353                 | 0.5786        | 0.6174        | 0.4615         | 0.2540      |
  | 0.1282        | 9.0   | 1188 | 0.6801          | 0.4144            | 0.5610         | 0.4767     | 0.9507           | 0.3729         | 0.5616                | 0.7576                 | 0.5569        | 0.5839        | 0.5263         | 0.2821      |
  | 0.1282        | 10.0  | 1320 | 0.5843          | 0.4022            | 0.5976         | 0.4808     | 0.9493           | 0.3262         | 0.6667                | 0.6957                 | 0.5417        | 0.6267        | 0.6286         | 0.2969      |
  | 0.1282        | 11.0  | 1452 | 0.7041          | 0.4187            | 0.5813         | 0.4868     | 0.9508           | 0.3158         | 0.608                 | 0.6857                 | 0.5583        | 0.6174        | 0.7429         | 0.3145      |
  | 0.0579        | 12.0  | 1584 | 0.7037          | 0.4143            | 0.5894         | 0.4866     | 0.9509           | 0.3310         | 0.6032                | 0.7164                 | 0.5426        | 0.6497        | 0.6486         | 0.3207      |
  | 0.0579        | 13.0  | 1716 | 0.6623          | 0.3961            | 0.5732         | 0.4684     | 0.9512           | 0.3490         | 0.6847                | 0.6462             