readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: codebert-base-finetuned-code-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. -->
  
  # codebert-base-finetuned-code-ner
  
  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: 0.3522
  - Precision: 0.6297
  - Recall: 0.6417
  - F1: 0.6356
  - Accuracy: 0.9185
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 191  | 0.4601          | 0.4861    | 0.4578 | 0.4715 | 0.8853   |
  | No log        | 2.0   | 382  | 0.3989          | 0.5806    | 0.5243 | 0.5510 | 0.8996   |
  | 0.5081        | 3.0   | 573  | 0.3547          | 0.5723    | 0.6017 | 0.5866 | 0.9059   |
  | 0.5081        | 4.0   | 764  | 0.3507          | 0.6161    | 0.6115 | 0.6138 | 0.9135   |
  | 0.5081        | 5.0   | 955  | 0.3412          | 0.6299    | 0.6252 | 0.6276 | 0.9161   |
  | 0.2299        | 6.0   | 1146 | 0.3418          | 0.6162    | 0.6465 | 0.6310 | 0.9175   |
  | 0.2299        | 7.0   | 1337 | 0.3497          | 0.6288    | 0.6287 | 0.6287 | 0.9175   |
  | 0.1618        | 8.0   | 1528 | 0.3474          | 0.6340    | 0.6397 | 0.6368 | 0.9189   |
  | 0.1618        | 9.0   | 1719 | 0.3501          | 0.6262    | 0.6432 | 0.6346 | 0.9179   |
  | 0.1618        | 10.0  | 1910 | 0.3522          | 0.6297    | 0.6417 | 0.6356 | 0.9185   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  