readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: albert-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-offensive-lm-tapt-finetuned
  
  This model is a fine-tuned version of [k4black/albert-offensive-lm-tapt](https://huggingface.co/k4black/albert-offensive-lm-tapt) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4680
  - F1: 0.7765
  
  ## 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: 12
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.6585        | 0.1   | 100  | 0.6663          | 0.3932 |
  | 0.6308        | 0.2   | 200  | 0.5807          | 0.5746 |
  | 0.5161        | 0.29  | 300  | 0.5005          | 0.7366 |
  | 0.4986        | 0.39  | 400  | 0.4984          | 0.7434 |
  | 0.484         | 0.49  | 500  | 0.4956          | 0.7098 |
  | 0.5035        | 0.59  | 600  | 0.4876          | 0.7334 |
  | 0.4767        | 0.69  | 700  | 0.4824          | 0.7314 |
  | 0.482         | 0.78  | 800  | 0.4937          | 0.7194 |
  | 0.4524        | 0.88  | 900  | 0.4759          | 0.7606 |
  | 0.4541        | 0.98  | 1000 | 0.4786          | 0.7613 |
  | 0.4404        | 1.08  | 1100 | 0.4597          | 0.7663 |
  | 0.4383        | 1.18  | 1200 | 0.4531          | 0.7762 |
  | 0.4414        | 1.27  | 1300 | 0.4436          | 0.7764 |
  | 0.4336        | 1.37  | 1400 | 0.4477          | 0.7625 |
  | 0.4353        | 1.47  | 1500 | 0.4466          | 0.7490 |
  | 0.4356        | 1.57  | 1600 | 0.4429          | 0.7743 |
  | 0.3938        | 1.67  | 1700 | 0.4450          | 0.7727 |
  | 0.4066        | 1.76  | 1800 | 0.4437          | 0.7776 |
  | 0.3867        | 1.86  | 1900 | 0.4717          | 0.7618 |
  | 0.4123        | 1.96  | 2000 | 0.4511          | 0.7689 |
  | 0.3671        | 2.06  | 2100 | 0.4680          | 0.7765 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  