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--- |
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base_model: vinai/phobert-base-v2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: GenZ-mental-health-toxic-content-classification-v2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# GenZ-mental-health-toxic-content-classification-v2 |
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This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7329 |
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- Accuracy: 0.8816 |
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- F1: 0.8088 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:| |
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| No log | 0.2558 | 200 | 0.3588 | 0.8499 | 0.7643 | |
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| No log | 0.5115 | 400 | 0.3447 | 0.8649 | 0.7834 | |
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| No log | 0.7673 | 600 | 0.3380 | 0.8740 | 0.7832 | |
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| 0.3661 | 1.0230 | 800 | 0.3297 | 0.8771 | 0.8042 | |
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| 0.3661 | 1.2788 | 1000 | 0.2997 | 0.8829 | 0.8085 | |
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| 0.3661 | 1.5345 | 1200 | 0.3038 | 0.8841 | 0.7980 | |
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| 0.3661 | 1.7903 | 1400 | 0.3058 | 0.8859 | 0.8110 | |
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| 0.2685 | 2.0460 | 1600 | 0.3259 | 0.8867 | 0.8171 | |
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| 0.2685 | 2.3018 | 1800 | 0.3194 | 0.8861 | 0.8086 | |
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| 0.2685 | 2.5575 | 2000 | 0.3328 | 0.8792 | 0.8137 | |
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| 0.2685 | 2.8133 | 2200 | 0.3462 | 0.8823 | 0.7894 | |
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| 0.2176 | 3.0691 | 2400 | 0.4151 | 0.8867 | 0.8209 | |
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| 0.2176 | 3.3248 | 2600 | 0.3727 | 0.8830 | 0.8129 | |
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| 0.2176 | 3.5806 | 2800 | 0.3754 | 0.8807 | 0.8126 | |
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| 0.2176 | 3.8363 | 3000 | 0.3496 | 0.8905 | 0.8229 | |
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| 0.1825 | 4.0921 | 3200 | 0.4204 | 0.8839 | 0.8058 | |
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| 0.1825 | 4.3478 | 3400 | 0.4341 | 0.8890 | 0.8129 | |
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| 0.1825 | 4.6036 | 3600 | 0.3610 | 0.8908 | 0.8166 | |
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| 0.1825 | 4.8593 | 3800 | 0.3546 | 0.8883 | 0.8143 | |
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| 0.1592 | 5.1151 | 4000 | 0.4929 | 0.8740 | 0.8073 | |
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| 0.1592 | 5.3708 | 4200 | 0.4861 | 0.8865 | 0.8029 | |
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| 0.1592 | 5.6266 | 4400 | 0.4340 | 0.8856 | 0.8162 | |
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| 0.1592 | 5.8824 | 4600 | 0.4357 | 0.8724 | 0.8038 | |
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| 0.1375 | 6.1381 | 4800 | 0.4334 | 0.8885 | 0.8157 | |
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| 0.1375 | 6.3939 | 5000 | 0.4724 | 0.8760 | 0.8083 | |
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| 0.1375 | 6.6496 | 5200 | 0.4504 | 0.8899 | 0.8162 | |
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| 0.1375 | 6.9054 | 5400 | 0.3867 | 0.8854 | 0.8096 | |
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| 0.1255 | 7.1611 | 5600 | 0.5133 | 0.8756 | 0.8070 | |
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| 0.1255 | 7.4169 | 5800 | 0.4806 | 0.8883 | 0.8163 | |
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| 0.1255 | 7.6726 | 6000 | 0.4748 | 0.8816 | 0.8114 | |
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| 0.1255 | 7.9284 | 6200 | 0.5101 | 0.8803 | 0.8084 | |
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| 0.115 | 8.1841 | 6400 | 0.5017 | 0.8832 | 0.8068 | |
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| 0.115 | 8.4399 | 6600 | 0.4820 | 0.8823 | 0.8041 | |
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| 0.115 | 8.6957 | 6800 | 0.5131 | 0.8865 | 0.8089 | |
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| 0.115 | 8.9514 | 7000 | 0.4742 | 0.8858 | 0.8145 | |
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| 0.1005 | 9.2072 | 7200 | 0.5905 | 0.8870 | 0.8108 | |
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| 0.1005 | 9.4629 | 7400 | 0.5393 | 0.8796 | 0.8067 | |
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| 0.1005 | 9.7187 | 7600 | 0.5595 | 0.8776 | 0.8077 | |
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| 0.1005 | 9.9744 | 7800 | 0.5101 | 0.8859 | 0.8079 | |
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| 0.0918 | 10.2302 | 8000 | 0.6249 | 0.8781 | 0.8067 | |
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| 0.0918 | 10.4859 | 8200 | 0.5490 | 0.8825 | 0.8077 | |
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| 0.0918 | 10.7417 | 8400 | 0.5394 | 0.8769 | 0.8040 | |
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| 0.0818 | 10.9974 | 8600 | 0.6048 | 0.8807 | 0.8099 | |
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| 0.0818 | 11.2532 | 8800 | 0.5951 | 0.8745 | 0.8011 | |
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| 0.0818 | 11.5090 | 9000 | 0.6220 | 0.8819 | 0.8077 | |
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| 0.0818 | 11.7647 | 9200 | 0.6505 | 0.8785 | 0.8063 | |
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| 0.078 | 12.0205 | 9400 | 0.6327 | 0.8785 | 0.8048 | |
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| 0.078 | 12.2762 | 9600 | 0.6260 | 0.8796 | 0.8084 | |
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| 0.078 | 12.5320 | 9800 | 0.5645 | 0.8800 | 0.8075 | |
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| 0.078 | 12.7877 | 10000 | 0.6264 | 0.8823 | 0.8083 | |
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| 0.071 | 13.0435 | 10200 | 0.6611 | 0.8798 | 0.8118 | |
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| 0.071 | 13.2992 | 10400 | 0.6474 | 0.8845 | 0.8125 | |
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| 0.071 | 13.5550 | 10600 | 0.6508 | 0.8819 | 0.8125 | |
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| 0.071 | 13.8107 | 10800 | 0.6394 | 0.8823 | 0.8089 | |
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| 0.0652 | 14.0665 | 11000 | 0.6261 | 0.8783 | 0.8069 | |
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| 0.0652 | 14.3223 | 11200 | 0.6541 | 0.8809 | 0.8070 | |
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| 0.0652 | 14.5780 | 11400 | 0.7019 | 0.8778 | 0.8088 | |
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| 0.0652 | 14.8338 | 11600 | 0.6469 | 0.8830 | 0.8091 | |
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| 0.0606 | 15.0895 | 11800 | 0.7078 | 0.8767 | 0.8049 | |
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| 0.0606 | 15.3453 | 12000 | 0.6889 | 0.8809 | 0.8070 | |
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| 0.0606 | 15.6010 | 12200 | 0.7316 | 0.8787 | 0.8090 | |
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| 0.0606 | 15.8568 | 12400 | 0.6827 | 0.8801 | 0.8033 | |
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| 0.0575 | 16.1125 | 12600 | 0.7547 | 0.8812 | 0.8094 | |
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| 0.0575 | 16.3683 | 12800 | 0.7358 | 0.8825 | 0.8055 | |
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| 0.0575 | 16.6240 | 13000 | 0.7128 | 0.8794 | 0.8045 | |
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| 0.0575 | 16.8798 | 13200 | 0.7322 | 0.8818 | 0.8061 | |
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| 0.054 | 17.1355 | 13400 | 0.7335 | 0.8814 | 0.8072 | |
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| 0.054 | 17.3913 | 13600 | 0.7275 | 0.8818 | 0.8058 | |
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| 0.054 | 17.6471 | 13800 | 0.7316 | 0.8810 | 0.8063 | |
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| 0.054 | 17.9028 | 14000 | 0.7090 | 0.8823 | 0.8056 | |
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| 0.052 | 18.1586 | 14200 | 0.7444 | 0.8781 | 0.8051 | |
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| 0.052 | 18.4143 | 14400 | 0.7201 | 0.8810 | 0.8066 | |
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| 0.052 | 18.6701 | 14600 | 0.7200 | 0.8825 | 0.8103 | |
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| 0.052 | 18.9258 | 14800 | 0.7259 | 0.8800 | 0.8073 | |
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| 0.049 | 19.1816 | 15000 | 0.7419 | 0.8830 | 0.8085 | |
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| 0.049 | 19.4373 | 15200 | 0.7344 | 0.8823 | 0.8085 | |
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| 0.049 | 19.6931 | 15400 | 0.7400 | 0.8816 | 0.8085 | |
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| 0.049 | 19.9488 | 15600 | 0.7329 | 0.8816 | 0.8088 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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