readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - generator
  model-index:
  - name: scideberta-cs-tdm-pretrained-finetuned-ner-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-tdm-pretrained-finetuned-ner-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: 0.7548
  - Overall Precision: 0.5582
  - Overall Recall: 0.7048
  - Overall F1: 0.6230
  - Overall Accuracy: 0.9578
  - Datasetname F1: 0.6225
  - Hyperparametername F1: 0.5707
  - Hyperparametervalue F1: 0.6796
  - Methodname F1: 0.6812
  - Metricname F1: 0.5039
  - Metricvalue F1: 0.7097
  - Taskname F1: 0.5776
  
  ## 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  | 0.6819          | 0.2314            | 0.3769         | 0.2867     | 0.9125           | 0.1270         | 0.2305                | 0.2479                 | 0.4072        | 0.3119        | 0.0635         | 0.2366      |
  | No log        | 2.0   | 264  | 0.4337          | 0.3977            | 0.5687         | 0.4681     | 0.9429           | 0.4516         | 0.3704                | 0.5419                 | 0.5900        | 0.2446        | 0.4340         | 0.4609      |
  | No log        | 3.0   | 396  | 0.3968          | 0.3617            | 0.6367         | 0.4613     | 0.9335           | 0.4828         | 0.3586                | 0.5649                 | 0.5331        | 0.3190        | 0.4800         | 0.4585      |
  | 0.5603        | 4.0   | 528  | 0.3730          | 0.3605            | 0.6327         | 0.4593     | 0.9363           | 0.4750         | 0.3789                | 0.6066                 | 0.5376        | 0.3229        | 0.4571         | 0.4375      |
  | 0.5603        | 5.0   | 660  | 0.4132          | 0.4650            | 0.6871         | 0.5546     | 0.9482           | 0.4943         | 0.4965                | 0.6577                 | 0.6465        | 0.4387        | 0.5306         | 0.5039      |
  | 0.5603        | 6.0   | 792  | 0.4071          | 0.4482            | 0.6884         | 0.5429     | 0.9468           | 0.5541         | 0.4341                | 0.5991                 | 0.6037        | 0.4865        | 0.64           | 0.5688      |
  | 0.5603        | 7.0   | 924  | 0.4077          | 0.4830            | 0.6952         | 0.5700     | 0.9508           | 0.5063         | 0.4953                | 0.7032                 | 0.6397        | 0.4286        | 0.6263         | 0.5469      |
  | 0.1161        | 8.0   | 1056 | 0.5215          | 0.5426            | 0.6925         | 0.6085     | 0.9577           | 0.6423         | 0.5190                | 0.7115                 | 0.6711        | 0.5175        | 0.6286         | 0.5797      |
  | 0.1161        | 9.0   | 1188 | 0.5192          | 0.4859            | 0.7020         | 0.5743     | 0.9518           | 0.5578         | 0.5195                | 0.5992                 | 0.6571        | 0.4744        | 0.5532         | 0.5611      |
  | 0.1161        | 10.0  | 1320 | 0.5301          | 0.5478            | 0.7020         | 0.6154     | 0.9563           | 0.5732         | 0.5782                | 0.7619                 | 0.6462        | 0.4675        | 0.7253         | 0.5727      |
  | 0.1161        | 11.0  | 1452 | 0.4965          | 0.5139            | 0.7048         | 0.5944     | 0.9531           | 0.5857         | 0.5290                | 0.7189                 | 0.6639        | 0.4235        | 0.6476         | 0.5532      |
  | 0.049         | 12.0  | 1584 | 0.6207          | 0.5713            | 0.6925         | 0.6261     | 0.9582           | 0.64           | 0.5377                | 0.7594                 | 0.7207        | 0.5070        | 0.6136         | 0.5530      |
  | 0.049         | 13.0  | 1716 | 0.6056          | 0.5360            | 0.7088         | 0.6104     | 0.9570           | 0.5921    