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BERT-evidence-types

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update model card README.md

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@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.9735
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- - Macro f1: 0.3791
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- - Weighted f1: 0.6925
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- - Accuracy: 0.7070
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- - Balanced accuracy: 0.3625
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  ## Model description
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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: 16
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- - eval_batch_size: 16
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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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  | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
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- | 1.098 | 1.0 | 250 | 1.0176 | 0.2666 | 0.6861 | 0.7070 | 0.2775 |
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- | 0.7656 | 2.0 | 500 | 1.0072 | 0.4124 | 0.7126 | 0.7215 | 0.3876 |
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- | 0.5045 | 3.0 | 750 | 1.1791 | 0.3759 | 0.6843 | 0.6910 | 0.3799 |
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- | 0.2874 | 4.0 | 1000 | 1.4338 | 0.3738 | 0.6888 | 0.6986 | 0.3705 |
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- | 0.1599 | 5.0 | 1250 | 1.8058 | 0.3839 | 0.6947 | 0.7070 | 0.3682 |
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- | 0.0991 | 6.0 | 1500 | 2.0263 | 0.3777 | 0.6793 | 0.6903 | 0.3627 |
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- | 0.0529 | 7.0 | 1750 | 2.2380 | 0.4046 | 0.6932 | 0.7047 | 0.3877 |
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- | 0.0311 | 8.0 | 2000 | 2.4153 | 0.4185 | 0.6999 | 0.7131 | 0.3899 |
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- | 0.0129 | 9.0 | 2250 | 2.7230 | 0.3702 | 0.6852 | 0.7123 | 0.3331 |
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- | 0.0102 | 10.0 | 2500 | 2.6453 | 0.4115 | 0.6934 | 0.7070 | 0.3880 |
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- | 0.0141 | 11.0 | 2750 | 2.7078 | 0.4054 | 0.6859 | 0.6979 | 0.3863 |
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- | 0.0088 | 12.0 | 3000 | 2.7182 | 0.3724 | 0.6904 | 0.7062 | 0.3559 |
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- | 0.0061 | 13.0 | 3250 | 2.7814 | 0.4091 | 0.6917 | 0.7055 | 0.3839 |
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- | 0.0069 | 14.0 | 3500 | 2.8035 | 0.3836 | 0.6986 | 0.7108 | 0.3688 |
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- | 0.0067 | 15.0 | 3750 | 2.9326 | 0.4119 | 0.6952 | 0.7139 | 0.3793 |
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- | 0.0049 | 16.0 | 4000 | 2.9338 | 0.4133 | 0.6885 | 0.7040 | 0.3794 |
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- | 0.0065 | 17.0 | 4250 | 2.9380 | 0.3820 | 0.6964 | 0.7100 | 0.3650 |
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- | 0.0045 | 18.0 | 4500 | 2.9439 | 0.3802 | 0.6925 | 0.7055 | 0.3646 |
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- | 0.0044 | 19.0 | 4750 | 2.9731 | 0.3796 | 0.6932 | 0.7078 | 0.3626 |
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- | 0.0056 | 20.0 | 5000 | 2.9735 | 0.3791 | 0.6925 | 0.7070 | 0.3625 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.4683
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+ - Macro f1: 0.3854
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+ - Weighted f1: 0.6985
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+ - Accuracy: 0.7116
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+ - Balanced accuracy: 0.3720
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  ## Model description
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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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  | Training Loss | Epoch | Step | Validation Loss | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:|
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+ | 1.1736 | 1.0 | 125 | 1.0715 | 0.2535 | 0.6525 | 0.6667 | 0.2721 |
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+ | 0.8182 | 2.0 | 250 | 0.9876 | 0.3068 | 0.6769 | 0.6804 | 0.3152 |
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+ | 0.6024 | 3.0 | 375 | 1.0436 | 0.4067 | 0.7024 | 0.7047 | 0.4089 |
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+ | 0.4004 | 4.0 | 500 | 1.1912 | 0.4045 | 0.6988 | 0.7078 | 0.4050 |
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+ | 0.2531 | 5.0 | 625 | 1.3408 | 0.3882 | 0.6861 | 0.6903 | 0.3967 |
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+ | 0.1625 | 6.0 | 750 | 1.5378 | 0.3866 | 0.6951 | 0.7040 | 0.3807 |
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+ | 0.0985 | 7.0 | 875 | 1.7579 | 0.3850 | 0.6990 | 0.7161 | 0.3824 |
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+ | 0.0664 | 8.0 | 1000 | 1.9837 | 0.3609 | 0.6849 | 0.7032 | 0.3529 |
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+ | 0.0411 | 9.0 | 1125 | 2.0033 | 0.3807 | 0.6929 | 0.7024 | 0.3618 |
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+ | 0.0262 | 10.0 | 1250 | 2.1714 | 0.3771 | 0.6924 | 0.7085 | 0.3585 |
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+ | 0.0204 | 11.0 | 1375 | 2.2539 | 0.3734 | 0.6832 | 0.6933 | 0.3658 |
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+ | 0.0141 | 12.0 | 1500 | 2.3033 | 0.3654 | 0.6830 | 0.6979 | 0.3556 |
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+ | 0.0118 | 13.0 | 1625 | 2.3853 | 0.3679 | 0.6912 | 0.7108 | 0.3520 |
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+ | 0.0109 | 14.0 | 1750 | 2.3749 | 0.3810 | 0.6952 | 0.7100 | 0.3665 |
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+ | 0.0078 | 15.0 | 1875 | 2.4042 | 0.3777 | 0.6942 | 0.7078 | 0.3645 |
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+ | 0.0079 | 16.0 | 2000 | 2.5097 | 0.3790 | 0.6938 | 0.7123 | 0.3632 |
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+ | 0.0073 | 17.0 | 2125 | 2.4305 | 0.3844 | 0.6957 | 0.7070 | 0.3725 |
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+ | 0.0046 | 18.0 | 2250 | 2.4700 | 0.3762 | 0.6941 | 0.7093 | 0.3638 |
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+ | 0.0064 | 19.0 | 2375 | 2.4566 | 0.3844 | 0.6974 | 0.7100 | 0.3713 |
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+ | 0.0057 | 20.0 | 2500 | 2.4683 | 0.3854 | 0.6985 | 0.7116 | 0.3720 |
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  ### Framework versions