readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: albert-base-v2-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. -->
  
  # albert-base-v2-offensive-lm-tapt-finetuned
  
  This model is a fine-tuned version of [k4black/albert-base-v2-offensive-lm-tapt](https://huggingface.co/k4black/albert-base-v2-offensive-lm-tapt) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4677
  - F1: 0.7559
  
  ## 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: 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     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.6413        | 0.08  | 100  | 0.6541          | 0.3932 |
  | 0.6072        | 0.16  | 200  | 0.5366          | 0.6515 |
  | 0.4913        | 0.25  | 300  | 0.5003          | 0.7335 |
  | 0.506         | 0.33  | 400  | 0.5075          | 0.7377 |
  | 0.4924        | 0.41  | 500  | 0.5066          | 0.7236 |
  | 0.5042        | 0.49  | 600  | 0.5035          | 0.6925 |
  | 0.5035        | 0.57  | 700  | 0.5126          | 0.7464 |
  | 0.4826        | 0.65  | 800  | 0.4840          | 0.7427 |
  | 0.4699        | 0.74  | 900  | 0.4769          | 0.7315 |
  | 0.4881        | 0.82  | 1000 | 0.4644          | 0.7520 |
  | 0.4681        | 0.9   | 1100 | 0.4705          | 0.7469 |
  | 0.4664        | 0.98  | 1200 | 0.4978          | 0.7556 |
  | 0.4471        | 1.06  | 1300 | 0.4677          | 0.7559 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  