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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: 2.0233
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- - Macro f1: 0.3675
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- - Weighted f1: 0.6815
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- - Accuracy: 0.6948
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- - Balanced accuracy: 0.3520
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  ## Model description
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@@ -39,9 +39,9 @@ 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: 5e-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
@@ -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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- | 1.3773 | 1.0 | 125 | 1.2259 | 0.1981 | 0.6131 | 0.6819 | 0.2171 |
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- | 1.156 | 2.0 | 250 | 1.1316 | 0.2898 | 0.6207 | 0.6636 | 0.3052 |
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- | 1.0304 | 3.0 | 375 | 1.1232 | 0.2515 | 0.6382 | 0.6461 | 0.2741 |
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- | 0.8953 | 4.0 | 500 | 1.0837 | 0.2739 | 0.6950 | 0.7131 | 0.2830 |
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- | 0.7685 | 5.0 | 625 | 1.1225 | 0.3440 | 0.6965 | 0.7207 | 0.3420 |
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- | 0.6505 | 6.0 | 750 | 1.1907 | 0.3380 | 0.6814 | 0.6963 | 0.3376 |
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- | 0.5534 | 7.0 | 875 | 1.2381 | 0.3348 | 0.6932 | 0.7139 | 0.3296 |
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- | 0.4729 | 8.0 | 1000 | 1.3227 | 0.3117 | 0.6929 | 0.7161 | 0.3013 |
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- | 0.4205 | 9.0 | 1125 | 1.4013 | 0.3374 | 0.6793 | 0.6925 | 0.3298 |
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- | 0.3618 | 10.0 | 1250 | 1.4847 | 0.3623 | 0.6963 | 0.7131 | 0.3385 |
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- | 0.3165 | 11.0 | 1375 | 1.5459 | 0.3507 | 0.6732 | 0.6842 | 0.3387 |
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- | 0.2759 | 12.0 | 1500 | 1.5969 | 0.3556 | 0.6861 | 0.7032 | 0.3406 |
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- | 0.2474 | 13.0 | 1625 | 1.7362 | 0.3559 | 0.6795 | 0.6880 | 0.3448 |
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- | 0.2187 | 14.0 | 1750 | 1.8644 | 0.3460 | 0.6786 | 0.6979 | 0.3262 |
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- | 0.2144 | 15.0 | 1875 | 1.8729 | 0.3478 | 0.6830 | 0.7032 | 0.3289 |
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- | 0.1911 | 16.0 | 2000 | 1.8958 | 0.3620 | 0.6765 | 0.6834 | 0.3609 |
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- | 0.1858 | 17.0 | 2125 | 1.9366 | 0.3662 | 0.6815 | 0.6933 | 0.3535 |
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- | 0.1579 | 18.0 | 2250 | 2.0065 | 0.3624 | 0.6820 | 0.6979 | 0.3442 |
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- | 0.1492 | 19.0 | 2375 | 2.0467 | 0.3577 | 0.6786 | 0.6963 | 0.3373 |
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- | 0.1527 | 20.0 | 2500 | 2.0233 | 0.3675 | 0.6815 | 0.6948 | 0.3520 |
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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: 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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  ### 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
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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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+ | 1.3124 | 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