albert-offensive-lm-tapt-finetuned
This model is a fine-tuned version of 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
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