readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: codebert-base-finetuned-code-ner-15e
    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. -->
  
  # codebert-base-finetuned-code-ner-15e
  
  This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3831
  - Precision: 0.6363
  - Recall: 0.6494
  - F1: 0.6428
  - Accuracy: 0.9197
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 191  | 0.4566          | 0.5021    | 0.4220 | 0.4585 | 0.8827   |
  | No log        | 2.0   | 382  | 0.3756          | 0.5699    | 0.5764 | 0.5731 | 0.9043   |
  | 0.5133        | 3.0   | 573  | 0.3605          | 0.6001    | 0.5767 | 0.5882 | 0.9093   |
  | 0.5133        | 4.0   | 764  | 0.3500          | 0.6130    | 0.6130 | 0.6130 | 0.9153   |
  | 0.5133        | 5.0   | 955  | 0.3501          | 0.6337    | 0.6172 | 0.6254 | 0.9178   |
  | 0.2203        | 6.0   | 1146 | 0.3645          | 0.6250    | 0.6352 | 0.6300 | 0.9163   |
  | 0.2203        | 7.0   | 1337 | 0.3488          | 0.6263    | 0.6422 | 0.6341 | 0.9189   |
  | 0.1457        | 8.0   | 1528 | 0.3575          | 0.6372    | 0.6397 | 0.6384 | 0.9194   |
  | 0.1457        | 9.0   | 1719 | 0.3662          | 0.6406    | 0.6343 | 0.6375 | 0.9189   |
  | 0.1457        | 10.0  | 1910 | 0.3613          | 0.6374    | 0.6473 | 0.6423 | 0.9201   |
  | 0.107         | 11.0  | 2101 | 0.3716          | 0.6329    | 0.6544 | 0.6435 | 0.9197   |
  | 0.107         | 12.0  | 2292 | 0.3754          | 0.6328    | 0.6487 | 0.6406 | 0.9193   |
  | 0.107         | 13.0  | 2483 | 0.3826          | 0.6395    | 0.6490 | 0.6443 | 0.9204   |
  | 0.0863        | 14.0  | 2674 | 0.3821          | 0.6368    | 0.6535 | 0.6451 | 0.9200   |
  | 0.0863        | 15.0  | 2865 | 0.3831          | 0.6363    | 0.6494 | 0.6428 | 0.9197   |
  
  
  ### Evaluation results
  
  |           |   Algorithm |   Application |       Class |   Code_Block |   Data_Structure |   Data_Type |    Device |   Error_Name |   File_Name |   File_Type |   Function |   HTML_XML_Tag |   Keyboard_IP |   Language |    Library |   Operating_System |   Output_Block |   User_Interface_Element |   User_Name |      Value |   Variable |    Version |   Website |   overall_precision |   overall_recall |   overall_f1 |   overall_accuracy |
  |:----------|------------:|--------------:|------------:|-------------:|-----------------:|------------:|----------:|-------------:|------------:|------------:|-----------:|---------------:|--------------:|-----------:|-----------:|-------------------:|---------------:|-------------------------:|------------:|-----------:|-----------:|-----------:|----------:|--------------------:|-----------------:|-------------:|-------------------:|
  | precision |           0 |      0.619835 |    0.680851 |     0.455629 |         0.813187 |    0.592593 |  0.395062 |     0.181818 |    0.800505 |    0.775956 |   0.757664 |       0.585366 |     0.333333  |   0.689769 |   0.61807  |           0.769231 |      0.0212766 |                 0.542214 |    0.4375   |   0.370236 |   0.560479 |   0.883721 |  0.382353 |            0.626308 |         0.642171 |      0.63414 |           0.918927 |
  | recall    |           0 |      0.677711 |    0.696864 |     0.494253 |         0.840909 |    0.8      |  0.533333 |     0.333333 |    0.794486 |    0.628319 |   0.631387 |       0.470588 |     0.0169492 |   0.81323  |   0.546279 |           0.843373 |      0.04      |                 0.653846 |    0.518519 |   0.52987  |   0.54482  |   0.914089 |  0.270833 |            0.626308 |         0.642171 |      0.63414 |           0.918927 |
  | f1        |           0 |      0.647482 |    0.688765 |     0.474156 |         0.826816 |    0.680851 |  0.453901 |     0.235294 |    0.797484 |    0.694377 |   0.688786 |       0.521739 |     0.0322581 |   0.746429 |   0.579961 |           0.804598 |      0.0277778 |                 0.592821 |    0.474576 |   0.435897 |   0.552538 |   0.898649 |  0.317073 |            0.626308 |         0.642171 |      0.63414 |           0.918927 |
  | number    |          31 |    664        | 1148        |   696        