readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - generator
  model-index:
  - name: deberta-v3-base-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. -->
  
  # deberta-v3-base-finetuned-ner
  
  This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the generator dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7679
  - Overall Precision: 0.4915
  - Overall Recall: 0.6463
  - Overall F1: 0.5584
  - Overall Accuracy: 0.9555
  - Datasetname F1: 0.3304
  - Hyperparametername F1: 0.6341
  - Hyperparametervalue F1: 0.7463
  - Methodname F1: 0.6093
  - Metricname F1: 0.7089
  - Metricvalue F1: 0.7500
  - Taskname F1: 0.4426
  
  ## 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  | 0.5046          | 0.2771            | 0.5041         | 0.3576     | 0.9356           | 0.2405         | 0.1988                | 0.4545                 | 0.4638        | 0.4539        | 0.6486         | 0.2793      |
  | No log        | 2.0   | 264  | 0.3928          | 0.3344            | 0.6463         | 0.4407     | 0.9376           | 0.2449         | 0.3968                | 0.6292                 | 0.5641        | 0.5373        | 0.4583         | 0.3359      |
  | No log        | 3.0   | 396  | 0.4714          | 0.4419            | 0.6179         | 0.5153     | 0.9533           | 0.3822         | 0.5310                | 0.7536                 | 0.6262        | 0.6328        | 0.6857         | 0.3291      |
  | 0.5663        | 4.0   | 528  | 0.3741          | 0.4493            | 0.7114         | 0.5507     | 0.9509           | 0.4717         | 0.7241                | 0.6353                 | 0.5918        | 0.5714        | 0.6275         | 0.4372      |
  | 0.5663        | 5.0   | 660  | 0.4202          | 0.3930            | 0.6870         | 0.5        | 0.9458           | 0.2759         | 0.6525                | 0.65                   | 0.5596        | 0.7097        | 0.7368         | 0.3573      |
  | 0.5663        | 6.0   | 792  | 0.4676          | 0.4244            | 0.6850         | 0.5241     | 0.9473           | 0.3333         | 0.5949                | 0.7397                 | 0.5653        | 0.6988        | 0.7568         | 0.3652      |
  | 0.5663        | 7.0   | 924  | 0.5744          | 0.4328            | 0.5955         | 0.5013     | 0.9517           | 0.2585         | 0.6167                | 0.5915                 | 0.5825        | 0.6386        | 0.7500         | 0.3824      |
  | 0.1503        | 8.0   | 1056 | 0.5340          | 0.4309            | 0.6585         | 0.5209     | 0.9499           | 0.2976         | 0.6299                | 0.7105                 | 0.6140        | 0.6708        | 0.7568         | 0.3544      |
  | 0.1503        | 9.0   | 1188 | 0.5229          | 0.4628            | 0.6829         | 0.5517     | 0.9531           | 0.4630         | 0.5103                | 0.6087                 | 0.625         | 0.6541        | 0.7778         | 0.4493      |
  | 0.1503        | 10.0  | 1320 | 0.6287          | 0.4978            | 0.6748         | 0.5729     | 0.9563           | 0.4314         | 0.6500                | 0.7463                 | 0.6413        | 0.7432        | 0.7568         | 0.4108      |
  | 0.1503        | 11.0  | 1452 | 0.5163          | 0.4571            | 0.7033         | 0.5540     | 0.9519           | 0.3925         | 0.5256                | 0.6024                 | 0.6828        | 0.6626        | 0.7368         | 0.4466      |
  | 0.0735        | 12.0  | 1584 | 0.6737          | 0.5046            | 0.6687         | 0.5752     | 0.9555           | 0.3883         | 0.6615                | 0.6757                 | 0.6074        | 0.7051        | 0.7778         | 0.4577      |
  | 0.0735        | 13.0  | 1716 | 0.5849          | 0.44              | 0.6931         | 0.5383     | 0.9480           | 0.3770         | 0.6555                | 0.6479        