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BERT_Offensive_English_Twitter
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2731
- Macro F1: 0.8890
- Micro F1: 0.8954
- Accuracy: 0.8954
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: 8
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|---|---|---|---|---|---|---|
| 0.3321 | 1.0 | 4036 | 0.6479 | 0.5302 | 0.5589 | 0.5589 |
| 0.4337 | 2.0 | 8073 | 0.3772 | 0.8346 | 0.8524 | 0.8524 |
| 0.2945 | 3.0 | 12109 | 0.3232 | 0.8753 | 0.8843 | 0.8843 |
| 0.243 | 4.0 | 16144 | 0.2731 | 0.8890 | 0.8954 | 0.8954 |
Framework versions
- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3
