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MiniLM-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 [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.8388
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- - Macro f1: 0.4307
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- - Weighted f1: 0.6983
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- - Accuracy: 0.7032
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- - Balanced accuracy: 0.4139
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  ## Model description
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@@ -39,7 +39,7 @@ More information needed
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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: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -52,26 +52,26 @@ The following hyperparameters were used during training:
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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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- | 1.3124 | 1.0 | 250 | 1.1166 | 0.2582 | 0.6393 | 0.6788 | 0.2758 |
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- | 0.9939 | 2.0 | 500 | 0.9671 | 0.3859 | 0.6988 | 0.7093 | 0.3799 |
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- | 0.8486 | 3.0 | 750 | 1.0263 | 0.3519 | 0.6632 | 0.6606 | 0.3642 |
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- | 0.7396 | 4.0 | 1000 | 1.0125 | 0.4195 | 0.7092 | 0.7192 | 0.4186 |
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- | 0.6425 | 5.0 | 1250 | 1.0983 | 0.3910 | 0.6746 | 0.6826 | 0.3925 |
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- | 0.5648 | 6.0 | 1500 | 1.0948 | 0.4184 | 0.7145 | 0.7222 | 0.4089 |
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- | 0.4858 | 7.0 | 1750 | 1.1658 | 0.4242 | 0.7058 | 0.7184 | 0.4279 |
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- | 0.4329 | 8.0 | 2000 | 1.3020 | 0.4178 | 0.6806 | 0.6849 | 0.4081 |
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- | 0.3799 | 9.0 | 2250 | 1.2622 | 0.4466 | 0.7004 | 0.7055 | 0.4419 |
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- | 0.326 | 10.0 | 2500 | 1.3822 | 0.4162 | 0.6971 | 0.7032 | 0.4048 |
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- | 0.2849 | 11.0 | 2750 | 1.4716 | 0.3933 | 0.6941 | 0.6971 | 0.3826 |
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- | 0.251 | 12.0 | 3000 | 1.5651 | 0.4259 | 0.6928 | 0.6956 | 0.4231 |
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- | 0.2205 | 13.0 | 3250 | 1.6920 | 0.4257 | 0.6942 | 0.7032 | 0.4112 |
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- | 0.205 | 14.0 | 3500 | 1.7016 | 0.4269 | 0.6899 | 0.6872 | 0.4260 |
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- | 0.1946 | 15.0 | 3750 | 1.7647 | 0.4312 | 0.6891 | 0.6910 | 0.4232 |
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- | 0.1661 | 16.0 | 4000 | 1.8255 | 0.4168 | 0.6886 | 0.6933 | 0.4003 |
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- | 0.1502 | 17.0 | 4250 | 1.8261 | 0.4190 | 0.6950 | 0.7040 | 0.3996 |
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- | 0.1625 | 18.0 | 4500 | 1.8163 | 0.4260 | 0.7001 | 0.7047 | 0.4079 |
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- | 0.1329 | 19.0 | 4750 | 1.8274 | 0.4368 | 0.7023 | 0.7055 | 0.4218 |
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- | 0.1248 | 20.0 | 5000 | 1.8388 | 0.4307 | 0.6983 | 0.7032 | 0.4139 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.1769
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+ - Macro f1: 0.4136
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+ - Weighted f1: 0.6948
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+ - Accuracy: 0.7017
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+ - Balanced accuracy: 0.3972
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  ## Model description
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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: 16
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  - eval_batch_size: 16
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  - seed: 42
 
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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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+ | 1.2712 | 1.0 | 250 | 1.1047 | 0.3342 | 0.6582 | 0.7260 | 0.3356 |
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+ | 0.968 | 2.0 | 500 | 0.9558 | 0.3972 | 0.6866 | 0.6948 | 0.4063 |
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+ | 0.8119 | 3.0 | 750 | 1.0086 | 0.3156 | 0.6913 | 0.7002 | 0.3292 |
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+ | 0.6873 | 4.0 | 1000 | 1.0305 | 0.3884 | 0.7035 | 0.7123 | 0.3780 |
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+ | 0.5959 | 5.0 | 1250 | 1.1257 | 0.3922 | 0.6727 | 0.6773 | 0.4151 |
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+ | 0.5078 | 6.0 | 1500 | 1.1642 | 0.3911 | 0.6767 | 0.6773 | 0.4180 |
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+ | 0.4042 | 7.0 | 1750 | 1.2840 | 0.4195 | 0.6891 | 0.6941 | 0.4103 |
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+ | 0.3446 | 8.0 | 2000 | 1.4170 | 0.4208 | 0.6791 | 0.6796 | 0.4240 |
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+ | 0.2973 | 9.0 | 2250 | 1.5195 | 0.4147 | 0.6841 | 0.6849 | 0.4137 |
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+ | 0.2377 | 10.0 | 2500 | 1.6252 | 0.4235 | 0.6950 | 0.7024 | 0.4098 |
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+ | 0.2074 | 11.0 | 2750 | 1.7327 | 0.4139 | 0.6856 | 0.6910 | 0.4046 |
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+ | 0.1849 | 12.0 | 3000 | 1.7941 | 0.4228 | 0.7005 | 0.7070 | 0.4102 |
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+ | 0.146 | 13.0 | 3250 | 1.8656 | 0.4317 | 0.7085 | 0.7199 | 0.4086 |
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+ | 0.137 | 14.0 | 3500 | 2.0057 | 0.4085 | 0.6987 | 0.7040 | 0.4011 |
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+ | 0.1324 | 15.0 | 3750 | 2.0904 | 0.4061 | 0.6822 | 0.6849 | 0.3972 |
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+ | 0.109 | 16.0 | 4000 | 2.0957 | 0.4133 | 0.6908 | 0.6941 | 0.4020 |
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+ | 0.0933 | 17.0 | 4250 | 2.1307 | 0.4011 | 0.6950 | 0.7017 | 0.3876 |
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+ | 0.0925 | 18.0 | 4500 | 2.1606 | 0.4122 | 0.6964 | 0.7040 | 0.3970 |
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+ | 0.0699 | 19.0 | 4750 | 2.1809 | 0.4161 | 0.7004 | 0.7078 | 0.3987 |
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+ | 0.0764 | 20.0 | 5000 | 2.1769 | 0.4136 | 0.6948 | 0.7017 | 0.3972 |
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  ### Framework versions