readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: bert-base-uncased-offensive-lm-tapt-finetuned
    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. -->
  
  # bert-base-uncased-offensive-lm-tapt-finetuned
  
  This model is a fine-tuned version of [k4black/bert-base-uncased-offensive-lm-tapt](https://huggingface.co/k4black/bert-base-uncased-offensive-lm-tapt) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4705
  - F1: 0.7560
  
  ## 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: 1e-05
  - train_batch_size: 10
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 5
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.6505        | 0.08  | 100  | 0.6699          | 0.3932 |
  | 0.636         | 0.16  | 200  | 0.6464          | 0.3932 |
  | 0.6275        | 0.25  | 300  | 0.6865          | 0.3932 |
  | 0.6257        | 0.33  | 400  | 0.5955          | 0.5356 |
  | 0.5831        | 0.41  | 500  | 0.5828          | 0.5760 |
  | 0.5511        | 0.49  | 600  | 0.5302          | 0.6414 |
  | 0.5326        | 0.57  | 700  | 0.5431          | 0.7068 |
  | 0.5116        | 0.65  | 800  | 0.5073          | 0.7380 |
  | 0.4843        | 0.74  | 900  | 0.5101          | 0.7266 |
  | 0.5097        | 0.82  | 1000 | 0.4752          | 0.7475 |
  | 0.4833        | 0.9   | 1100 | 0.4781          | 0.7415 |
  | 0.4868        | 0.98  | 1200 | 0.4828          | 0.7557 |
  | 0.441         | 1.06  | 1300 | 0.4906          | 0.7600 |
  | 0.4761        | 1.14  | 1400 | 0.4871          | 0.7585 |
  | 0.4628        | 1.23  | 1500 | 0.4721          | 0.7541 |
  | 0.4562        | 1.31  | 1600 | 0.4818          | 0.7529 |
  | 0.4605        | 1.39  | 1700 | 0.4901          | 0.7545 |
  | 0.4662        | 1.47  | 1800 | 0.4727          | 0.7505 |
  | 0.4598        | 1.55  | 1900 | 0.4695          | 0.7588 |
  | 0.4348        | 1.63  | 2000 | 0.4845          | 0.7551 |
  | 0.3896        | 1.72  | 2100 | 0.5245          | 0.7598 |
  | 0.4046        | 1.8   | 2200 | 0.5140          | 0.7489 |
  | 0.4304        | 1.88  | 2300 | 0.5124          | 0.7278 |
  | 0.4478        | 1.96  | 2400 | 0.4705          | 0.7560 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  