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hatespeech_distilbert

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9977
  • Accuracy: 0.7737
  • Recall: 0.8118
  • Precision: 0.7526
  • F1: 0.7811

And the following results on the test set:

  • Loss: 1.0640
  • Accuracy: 0.7544
  • Recall: 0.7930
  • Precision: 0.7406
  • F1: 0.7659

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: 8e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Recall Precision F1
0.4863 0.9935 77 0.4678 0.7701 0.7421 0.7841 0.7625
0.3935 2.0 155 0.4595 0.7834 0.7340 0.8124 0.7712
0.2792 2.9935 232 0.5285 0.7850 0.7291 0.8188 0.7713
0.1408 4.0 310 0.7130 0.7785 0.7940 0.7684 0.7810
0.0945 4.9935 387 0.8230 0.7806 0.7551 0.7937 0.7739
0.0541 6.0 465 0.9977 0.7737 0.8118 0.7526 0.7811
0.0331 6.9935 542 1.1107 0.7753 0.7859 0.7678 0.7768
0.0151 8.0 620 1.1703 0.7789 0.7543 0.7915 0.7724
0.0106 8.9935 697 1.2741 0.7785 0.7616 0.7864 0.7738
0.0051 9.9355 770 1.2964 0.7753 0.7851 0.7683 0.7766

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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