readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - generator
  model-index:
  - name: scideberta-cs-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. -->
  
  # scideberta-cs-finetuned-ner
  
  This model is a fine-tuned version of [KISTI-AI/scideberta-cs](https://huggingface.co/KISTI-AI/scideberta-cs) on the generator dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8848
  - Overall Precision: 0.5492
  - Overall Recall: 0.6240
  - Overall F1: 0.5842
  - Overall Accuracy: 0.9552
  - Datasetname F1: 0.4590
  - Hyperparametername F1: 0.7273
  - Hyperparametervalue F1: 0.7937
  - Methodname F1: 0.6227
  - Metricname F1: 0.7597
  - Metricvalue F1: 0.6250
  - Taskname F1: 0.4348
  
  ## 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  | 0.4127          | 0.3852            | 0.6646         | 0.4877     | 0.9411           | 0.3875         | 0.4690                | 0.6                    | 0.6338        | 0.6438        | 0.5806         | 0.3670      |
  | No log        | 2.0   | 264  | 0.3424          | 0.3447            | 0.6972         | 0.4613     | 0.9353           | 0.3204         | 0.4103                | 0.5600                 | 0.5691        | 0.5848        | 0.7027         | 0.3594      |
  | No log        | 3.0   | 396  | 0.3942          | 0.4767            | 0.6850         | 0.5621     | 0.9534           | 0.5385         | 0.6500                | 0.7429                 | 0.6583        | 0.6437        | 0.6111         | 0.3830      |
  | 0.4541        | 4.0   | 528  | 0.3542          | 0.4516            | 0.7012         | 0.5494     | 0.9503           | 0.4127         | 0.6552                | 0.5417                 | 0.6068        | 0.6243        | 0.4762         | 0.4895      |
  | 0.4541        | 5.0   | 660  | 0.4092          | 0.5076            | 0.6829         | 0.5823     | 0.9560           | 0.3857         | 0.5827                | 0.6933                 | 0.6866        | 0.7465        | 0.6875         | 0.4865      |
  | 0.4541        | 6.0   | 792  | 0.4450          | 0.4465            | 0.6870         | 0.5412     | 0.9491           | 0.3613         | 0.5985                | 0.6506                 | 0.6278        | 0.6667        | 0.6857         | 0.4332      |
  | 0.4541        | 7.0   | 924  | 0.4487          | 0.4985            | 0.6707         | 0.5719     | 0.9552           | 0.4407         | 0.6400                | 0.5789                 | 0.6590        | 0.6980        | 0.7429         | 0.4667      |
  | 0.1083        | 8.0   | 1056 | 0.4361          | 0.5068            | 0.6850         | 0.5825     | 0.9569           | 0.4553         | 0.6457                | 0.7429                 | 0.6667        | 0.6887        | 0.6875         | 0.4536      |
  | 0.1083        | 9.0   | 1188 | 0.5592          | 0.4954            | 0.6504         | 0.5624     | 0.9549           | 0.4538         | 0.6552                | 0.6753                 | 0.6397        | 0.6581        | 0.7647         | 0.4118      |
  | 0.1083        | 10.0  | 1320 | 0.5272          | 0.4686            | 0.6667         | 0.5503     | 0.9497           | 0.3816         | 0.6074                | 0.7                    | 0.6340        | 0.7347        | 0.7429         | 0.3917      |
  | 0.1083        | 11.0  | 1452 | 0.6108          | 0.5412            | 0.6809         | 0.6031     | 0.9562           | 0.4727         | 0.6724                | 0.7222                 | 0.6615        | 0.7097        | 0.6857         | 0.5027      |
  | 0.0491        | 12.0  | 1584 | 0.7836          | 0.5481            | 0.6138         | 0.5791     | 0.9546           | 0.5043         | 0.6446                | 0.7246                 | 0.6286        | 0.7347        | 0.7273         | 0.4217      |
  | 0.0491        | 13.0  | 1716 | 0.5258          | 0.4838            | 0.6667         | 0.5607     | 0.9527           | 0.4580         | 0.6299                | 0.6944                 | 0.6234      