readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: GroNLP-hateBERT-offensive-lm-tapt
    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. -->
  
  # GroNLP-hateBERT-offensive-lm-tapt
  
  This model is a fine-tuned version of [k4black/GroNLP-hateBERT-offensive-lm-tapt](https://huggingface.co/k4black/GroNLP-hateBERT-offensive-lm-tapt) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4551
  - F1: 0.7806
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.6532        | 0.08  | 100  | 0.6731          | 0.3932 |
  | 0.6359        | 0.16  | 200  | 0.6519          | 0.3932 |
  | 0.6285        | 0.25  | 300  | 0.6639          | 0.3932 |
  | 0.6314        | 0.33  | 400  | 0.6165          | 0.3932 |
  | 0.6029        | 0.41  | 500  | 0.5852          | 0.4232 |
  | 0.5575        | 0.49  | 600  | 0.5143          | 0.6702 |
  | 0.4994        | 0.57  | 700  | 0.5479          | 0.7023 |
  | 0.5012        | 0.65  | 800  | 0.4916          | 0.7515 |
  | 0.4641        | 0.74  | 900  | 0.4899          | 0.7543 |
  | 0.504         | 0.82  | 1000 | 0.4614          | 0.7535 |
  | 0.4495        | 0.9   | 1100 | 0.4622          | 0.7694 |
  | 0.4678        | 0.98  | 1200 | 0.4869          | 0.7591 |
  | 0.4311        | 1.06  | 1300 | 0.4505          | 0.7813 |
  | 0.4501        | 1.14  | 1400 | 0.4727          | 0.7609 |
  | 0.4231        | 1.23  | 1500 | 0.4553          | 0.7685 |
  | 0.431         | 1.31  | 1600 | 0.4448          | 0.7793 |
  | 0.4306        | 1.39  | 1700 | 0.4467          | 0.7680 |
  | 0.407         | 1.47  | 1800 | 0.4627          | 0.7546 |
  | 0.4248        | 1.55  | 1900 | 0.4582          | 0.7679 |
  | 0.4083        | 1.63  | 2000 | 0.4628          | 0.7758 |
  | 0.375         | 1.72  | 2100 | 0.4551          | 0.7806 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  