readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: roberta-base-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. -->
  
  # roberta-base-offensive-lm-tapt-finetuned
  
  This model is a fine-tuned version of [k4black/roberta-base-offensive-lm-tapt](https://huggingface.co/k4black/roberta-base-offensive-lm-tapt) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4931
  - F1: 0.7689
  
  ## 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     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.5239        | 0.08  | 100  | 0.4834          | 0.7377 |
  | 0.5121        | 0.16  | 200  | 0.4681          | 0.7404 |
  | 0.4579        | 0.25  | 300  | 0.4864          | 0.7632 |
  | 0.4434        | 0.33  | 400  | 0.4867          | 0.7691 |
  | 0.3907        | 0.41  | 500  | 0.4730          | 0.7830 |
  | 0.489         | 0.49  | 600  | 0.4360          | 0.7863 |
  | 0.4802        | 0.57  | 700  | 0.5699          | 0.7266 |
  | 0.4854        | 0.65  | 800  | 0.4966          | 0.7625 |
  | 0.4686        | 0.74  | 900  | 0.4931          | 0.7689 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  