readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - hate_speech18
  widget:
  - text: "ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature like the humpback of notre dame or Igor haha"
  metrics:
  - accuracy
  model-index:
  - name: deberta-v3-small-hate-speech
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: hate_speech18
        type: hate_speech18
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.916058394160584
  ---
  
  <!-- 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. -->
  
  # DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection
  
  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the hate_speech18 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2922
  - Accuracy: 0.9161
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4147        | 1.0   | 650  | 0.3910          | 0.8832   |
  | 0.2975        | 2.0   | 1300 | 0.2922          | 0.9161   |
  | 0.2575        | 3.0   | 1950 | 0.3555          | 0.9051   |
  | 0.1553        | 4.0   | 2600 | 0.4263          | 0.9124   |
  | 0.1267        | 5.0   | 3250 | 0.4238          | 0.9161   |
  
  
  ### Framework versions
  
  - Transformers 4.12.5
  - Pytorch 1.10.0+cu111
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  