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README.md
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---
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language:
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- en
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license:
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tags:
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- generated_from_trainer
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datasets:
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- jigsaw
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model_index:
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- name: bert-base-uncased
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results:
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- {}
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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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# bert-base-uncased
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This model is a fine-tuned version of [](https://huggingface.co/) on the jigsaw dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0393
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- Precision Micro: 0.7758
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- Recall Micro: 0.7858
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- F1 Micro: 0.7808
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- F2 Micro: 0.7838
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- Precision Macro: 0.6349
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- Recall Macro: 0.5972
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- F1 Macro: 0.6105
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- F2 Macro: 0.6015
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- Overall Precision: 0.9841
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- Overall Recall: 0.9841
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- Overall F1: 0.9841
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- Overall F2: 0.9841
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- Overall Accuracy: 0.9841
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- Matthews Corrcoef: 0.7725
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- Aucroc Macro: 0.9897
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- Aucroc Micro: 0.9920
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- Accuracy Toxic: 0.9678
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- F1 Toxic: 0.8295
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- Accuracy Severe Toxic: 0.9899
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- F1 Severe Toxic: 0.3313
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- Accuracy Obscene: 0.9816
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- F1 Obscene: 0.8338
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- Accuracy Threat: 0.9974
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- F1 Threat: 0.4545
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- Accuracy Insult: 0.9763
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- F1 Insult: 0.7662
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- Accuracy Identity Hate: 0.9914
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- F1 Identity Hate: 0.4480
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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: 3e-05
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- train_batch_size: 24
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- eval_batch_size: 12
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 48
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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: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Recall Micro | F1 Micro | F2 Micro | Precision Macro | Recall Macro | F1 Macro | F2 Macro | Overall Precision | Overall Recall | Overall F1 | Overall F2 | Overall Accuracy | Matthews Corrcoef | Aucroc Macro | Aucroc Micro | Accuracy Toxic | F1 Toxic | Accuracy Severe Toxic | F1 Severe Toxic | Accuracy Obscene | F1 Obscene | Accuracy Threat | F1 Threat | Accuracy Insult | F1 Insult | Accuracy Identity Hate | F1 Identity Hate |
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|:-------------:|:-----:|:-----:|:---------------:|:---------------:|:------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:--------:|:-----------------:|:--------------:|:----------:|:----------:|:----------------:|:-----------------:|:------------:|:------------:|:--------------:|:--------:|:---------------------:|:---------------:|:----------------:|:----------:|:---------------:|:---------:|:---------------:|:---------:|:----------------------:|:----------------:|
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| 0.0433 | 1.0 | 2659 | 0.0423 | 0.7607 | 0.7798 | 0.7702 | 0.7759 | 0.6398 | 0.5561 | 0.5585 | 0.5535 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | 0.7615 | 0.9887 | 0.9908 | 0.9671 | 0.8211 | 0.9878 | 0.4354 | 0.9805 | 0.8265 | 0.9974 | 0.2243 | 0.9746 | 0.7602 | 0.9918 | 0.2834 |
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| 0.0366 | 2.0 | 5318 | 0.0393 | 0.7758 | 0.7858 | 0.7808 | 0.7838 | 0.6349 | 0.5972 | 0.6105 | 0.6015 | 0.9841 | 0.9841 | 0.9841 | 0.9841 | 0.9841 | 0.7725 | 0.9897 | 0.9920 | 0.9678 | 0.8295 | 0.9899 | 0.3313 | 0.9816 | 0.8338 | 0.9974 | 0.4545 | 0.9763 | 0.7662 | 0.9914 | 0.4480 |
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| 0.0305 | 3.0 | 7977 | 0.0399 | 0.7608 | 0.8186 | 0.7887 | 0.8064 | 0.6621 | 0.6856 | 0.6715 | 0.6794 | 0.9842 | 0.9842 | 0.9842 | 0.9842 | 0.9842 | 0.7810 | 0.9897 | 0.9919 | 0.9662 | 0.8272 | 0.9892 | 0.4772 | 0.9815 | 0.8347 | 0.9977 | 0.5629 | 0.9772 | 0.7740 | 0.9931 | 0.5528 |
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| 0.0263 | 4.0 | 10636 | 0.0435 | 0.7333 | 0.8336 | 0.7803 | 0.8114 | 0.6395 | 0.7039 | 0.6687 | 0.6890 | 0.9830 | 0.9830 | 0.9830 | 0.9830 | 0.9830 | 0.7732 | 0.9897 | 0.9912 | 0.9608 | 0.8083 | 0.9898 | 0.4791 | 0.9812 | 0.8319 | 0.9972 | 0.5368 | 0.9756 | 0.7700 | 0.9935 | 0.5861 |
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| 0.0218 | 5.0 | 13295 | 0.0456 | 0.7480 | 0.8108 | 0.7781 | 0.7974 | 0.6661 | 0.6720 | 0.6662 | 0.6691 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | 0.9833 | 0.7701 | 0.9890 | 0.9907 | 0.9612 | 0.8071 | 0.9894 | 0.4642 | 0.9823 | 0.8354 | 0.9977 | 0.5325 | 0.9754 | 0.7613 | 0.9936 | 0.5968 |
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### Framework versions
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- Transformers 4.8.2
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- Pytorch 1.9.0+cu102
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- Datasets 1.9.0
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- Tokenizers 0.10.3
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