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GenZ-mental-health-toxic-content-classification-v2

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roberta
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metadata
base_model: vinai/phobert-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: GenZ-mental-health-toxic-content-classification-v2
    results: []

GenZ-mental-health-toxic-content-classification-v2

This model is a fine-tuned version of vinai/phobert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7329
  • Accuracy: 0.8816
  • F1: 0.8088

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
No log 0.2558 200 0.3588 0.8499 0.7643
No log 0.5115 400 0.3447 0.8649 0.7834
No log 0.7673 600 0.3380 0.8740 0.7832
0.3661 1.0230 800 0.3297 0.8771 0.8042
0.3661 1.2788 1000 0.2997 0.8829 0.8085
0.3661 1.5345 1200 0.3038 0.8841 0.7980
0.3661 1.7903 1400 0.3058 0.8859 0.8110
0.2685 2.0460 1600 0.3259 0.8867 0.8171
0.2685 2.3018 1800 0.3194 0.8861 0.8086
0.2685 2.5575 2000 0.3328 0.8792 0.8137
0.2685 2.8133 2200 0.3462 0.8823 0.7894
0.2176 3.0691 2400 0.4151 0.8867 0.8209
0.2176 3.3248 2600 0.3727 0.8830 0.8129
0.2176 3.5806 2800 0.3754 0.8807 0.8126
0.2176 3.8363 3000 0.3496 0.8905 0.8229
0.1825 4.0921 3200 0.4204 0.8839 0.8058
0.1825 4.3478 3400 0.4341 0.8890 0.8129
0.1825 4.6036 3600 0.3610 0.8908 0.8166
0.1825 4.8593 3800 0.3546 0.8883 0.8143
0.1592 5.1151 4000 0.4929 0.8740 0.8073
0.1592 5.3708 4200 0.4861 0.8865 0.8029
0.1592 5.6266 4400 0.4340 0.8856 0.8162
0.1592 5.8824 4600 0.4357 0.8724 0.8038
0.1375 6.1381 4800 0.4334 0.8885 0.8157
0.1375 6.3939 5000 0.4724 0.8760 0.8083
0.1375 6.6496 5200 0.4504 0.8899 0.8162
0.1375 6.9054 5400 0.3867 0.8854 0.8096
0.1255 7.1611 5600 0.5133 0.8756 0.8070
0.1255 7.4169 5800 0.4806 0.8883 0.8163
0.1255 7.6726 6000 0.4748 0.8816 0.8114
0.1255 7.9284 6200 0.5101 0.8803 0.8084
0.115 8.1841 6400 0.5017 0.8832 0.8068
0.115 8.4399 6600 0.4820 0.8823 0.8041
0.115 8.6957 6800 0.5131 0.8865 0.8089
0.115 8.9514 7000 0.4742 0.8858 0.8145
0.1005 9.2072 7200 0.5905 0.8870 0.8108
0.1005 9.4629 7400 0.5393 0.8796 0.8067
0.1005 9.7187 7600 0.5595 0.8776 0.8077
0.1005 9.9744 7800 0.5101 0.8859 0.8079
0.0918 10.2302 8000 0.6249 0.8781 0.8067
0.0918 10.4859 8200 0.5490 0.8825 0.8077
0.0918 10.7417 8400 0.5394 0.8769 0.8040
0.0818 10.9974 8600 0.6048 0.8807 0.8099
0.0818 11.2532 8800 0.5951 0.8745 0.8011
0.0818 11.5090 9000 0.6220 0.8819 0.8077
0.0818 11.7647 9200 0.6505 0.8785 0.8063
0.078 12.0205 9400 0.6327 0.8785 0.8048
0.078 12.2762 9600 0.6260 0.8796 0.8084
0.078 12.5320 9800 0.5645 0.8800 0.8075
0.078 12.7877 10000 0.6264 0.8823 0.8083
0.071 13.0435 10200 0.6611 0.8798 0.8118
0.071 13.2992 10400 0.6474 0.8845 0.8125
0.071 13.5550 10600 0.6508 0.8819 0.8125
0.071 13.8107 10800 0.6394 0.8823 0.8089
0.0652 14.0665 11000 0.6261 0.8783 0.8069
0.0652 14.3223 11200 0.6541 0.8809 0.8070
0.0652 14.5780 11400 0.7019 0.8778 0.8088
0.0652 14.8338 11600 0.6469 0.8830 0.8091
0.0606 15.0895 11800 0.7078 0.8767 0.8049
0.0606 15.3453 12000 0.6889 0.8809 0.8070
0.0606 15.6010 12200 0.7316 0.8787 0.8090
0.0606 15.8568 12400 0.6827 0.8801 0.8033
0.0575 16.1125 12600 0.7547 0.8812 0.8094
0.0575 16.3683 12800 0.7358 0.8825 0.8055
0.0575 16.6240 13000 0.7128 0.8794 0.8045
0.0575 16.8798 13200 0.7322 0.8818 0.8061
0.054 17.1355 13400 0.7335 0.8814 0.8072
0.054 17.3913 13600 0.7275 0.8818 0.8058
0.054 17.6471 13800 0.7316 0.8810 0.8063
0.054 17.9028 14000 0.7090 0.8823 0.8056
0.052 18.1586 14200 0.7444 0.8781 0.8051
0.052 18.4143 14400 0.7201 0.8810 0.8066
0.052 18.6701 14600 0.7200 0.8825 0.8103
0.052 18.9258 14800 0.7259 0.8800 0.8073
0.049 19.1816 15000 0.7419 0.8830 0.8085
0.049 19.4373 15200 0.7344 0.8823 0.8085
0.049 19.6931 15400 0.7400 0.8816 0.8085
0.049 19.9488 15600 0.7329 0.8816 0.8088

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1