bert-fine-tune-toxic-comment
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5352
- Accuracy: 0.935
- Recall: 0.4762
- Precision: 0.8333
- F1: 0.6061
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: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
---|---|---|---|---|---|---|---|
0.2674 | 1.0 | 100 | 0.3102 | 0.93 | 0.4286 | 0.8182 | 0.5625 |
0.1455 | 2.0 | 200 | 0.2689 | 0.93 | 0.6190 | 0.6842 | 0.65 |
0.0502 | 3.0 | 300 | 0.3969 | 0.925 | 0.6667 | 0.6364 | 0.6512 |
0.011 | 4.0 | 400 | 0.5352 | 0.935 | 0.4762 | 0.8333 | 0.6061 |
0.0071 | 5.0 | 500 | 0.5568 | 0.92 | 0.6667 | 0.6087 | 0.6364 |
0.0242 | 6.0 | 600 | 0.4414 | 0.915 | 0.6190 | 0.5909 | 0.6047 |
0.0003 | 7.0 | 700 | 0.5668 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0001 | 8.0 | 800 | 0.6002 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0001 | 9.0 | 900 | 0.6258 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 10.0 | 1000 | 0.6471 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 11.0 | 1100 | 0.6641 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 12.0 | 1200 | 0.6767 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 13.0 | 1300 | 0.6877 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 14.0 | 1400 | 0.6971 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 15.0 | 1500 | 0.7063 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 16.0 | 1600 | 0.7140 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 17.0 | 1700 | 0.7210 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 18.0 | 1800 | 0.7285 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 19.0 | 1900 | 0.7348 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 20.0 | 2000 | 0.7410 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 21.0 | 2100 | 0.7467 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 22.0 | 2200 | 0.7527 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 23.0 | 2300 | 0.7582 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 24.0 | 2400 | 0.7636 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 25.0 | 2500 | 0.7687 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 26.0 | 2600 | 0.7738 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 27.0 | 2700 | 0.7784 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 28.0 | 2800 | 0.7830 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 29.0 | 2900 | 0.7873 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 30.0 | 3000 | 0.7912 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 31.0 | 3100 | 0.7957 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 32.0 | 3200 | 0.8000 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 33.0 | 3300 | 0.8041 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 34.0 | 3400 | 0.8080 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 35.0 | 3500 | 0.8117 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 36.0 | 3600 | 0.8156 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 37.0 | 3700 | 0.8193 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 38.0 | 3800 | 0.8228 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 39.0 | 3900 | 0.8263 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 40.0 | 4000 | 0.8300 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 41.0 | 4100 | 0.8333 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 42.0 | 4200 | 0.8365 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 43.0 | 4300 | 0.8397 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 44.0 | 4400 | 0.8429 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 45.0 | 4500 | 0.8461 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 46.0 | 4600 | 0.8489 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 47.0 | 4700 | 0.8523 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 48.0 | 4800 | 0.8554 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 49.0 | 4900 | 0.8584 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 50.0 | 5000 | 0.8614 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 51.0 | 5100 | 0.8641 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 52.0 | 5200 | 0.8669 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 53.0 | 5300 | 0.8698 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 54.0 | 5400 | 0.8729 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 55.0 | 5500 | 0.8757 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 56.0 | 5600 | 0.8785 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 57.0 | 5700 | 0.8812 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 58.0 | 5800 | 0.8840 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 59.0 | 5900 | 0.8865 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 60.0 | 6000 | 0.8893 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 61.0 | 6100 | 0.8918 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 62.0 | 6200 | 0.8944 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 63.0 | 6300 | 0.8968 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 64.0 | 6400 | 0.8993 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 65.0 | 6500 | 0.9018 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 66.0 | 6600 | 0.9044 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 67.0 | 6700 | 0.9065 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 68.0 | 6800 | 0.9088 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 69.0 | 6900 | 0.9113 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 70.0 | 7000 | 0.9132 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 71.0 | 7100 | 0.9156 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 72.0 | 7200 | 0.9181 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 73.0 | 7300 | 0.9204 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 74.0 | 7400 | 0.9222 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 75.0 | 7500 | 0.9244 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 76.0 | 7600 | 0.9263 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 77.0 | 7700 | 0.9283 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 78.0 | 7800 | 0.9304 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 79.0 | 7900 | 0.9324 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 80.0 | 8000 | 0.9342 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 81.0 | 8100 | 0.9359 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 82.0 | 8200 | 0.9377 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 83.0 | 8300 | 0.9396 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 84.0 | 8400 | 0.9414 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 85.0 | 8500 | 0.9431 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 86.0 | 8600 | 0.9446 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 87.0 | 8700 | 0.9461 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 88.0 | 8800 | 0.9474 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 89.0 | 8900 | 0.9486 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 90.0 | 9000 | 0.9497 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 91.0 | 9100 | 0.9509 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 92.0 | 9200 | 0.9519 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 93.0 | 9300 | 0.9528 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 94.0 | 9400 | 0.9538 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 95.0 | 9500 | 0.9545 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 96.0 | 9600 | 0.9551 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 97.0 | 9700 | 0.9556 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 98.0 | 9800 | 0.9559 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 99.0 | 9900 | 0.9562 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
0.0 | 100.0 | 10000 | 0.9563 | 0.93 | 0.4762 | 0.7692 | 0.5882 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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