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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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@@ -39,7 +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: 5e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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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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- | 1.3773 | 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: 1.6603
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+ - Macro f1: 0.4329
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+ - Weighted f1: 0.7053
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+ - Accuracy: 0.7154
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+ - Balanced accuracy: 0.4114
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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: 32
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  - eval_batch_size: 32
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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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+ | 1.3633 | 1.0 | 125 | 1.1325 | 0.3442 | 0.6470 | 0.6872 | 0.3862 |
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+ | 1.0162 | 2.0 | 250 | 0.9858 | 0.3062 | 0.6889 | 0.7131 | 0.3135 |
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+ | 0.868 | 3.0 | 375 | 0.9587 | 0.4091 | 0.7071 | 0.7207 | 0.3993 |
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+ | 0.75 | 4.0 | 500 | 0.9983 | 0.4105 | 0.7080 | 0.7192 | 0.4039 |
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+ | 0.6317 | 5.0 | 625 | 1.0197 | 0.4095 | 0.6941 | 0.6994 | 0.4093 |
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+ | 0.5253 | 6.0 | 750 | 1.0760 | 0.4303 | 0.7073 | 0.7123 | 0.4223 |
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+ | 0.4615 | 7.0 | 875 | 1.1371 | 0.4328 | 0.7040 | 0.7169 | 0.4096 |
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+ | 0.3984 | 8.0 | 1000 | 1.1649 | 0.4516 | 0.6997 | 0.7002 | 0.4678 |
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+ | 0.3332 | 9.0 | 1125 | 1.2009 | 0.4364 | 0.6994 | 0.7040 | 0.4243 |
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+ | 0.2996 | 10.0 | 1250 | 1.2760 | 0.4336 | 0.7095 | 0.7192 | 0.4162 |
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+ | 0.255 | 11.0 | 1375 | 1.3266 | 0.4353 | 0.6914 | 0.6918 | 0.4402 |
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+ | 0.2318 | 12.0 | 1500 | 1.3591 | 0.4322 | 0.7011 | 0.7116 | 0.4101 |
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+ | 0.2163 | 13.0 | 1625 | 1.4554 | 0.4226 | 0.7080 | 0.7237 | 0.4029 |
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+ | 0.1837 | 14.0 | 1750 | 1.4363 | 0.4385 | 0.6938 | 0.6963 | 0.4250 |
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+ | 0.1735 | 15.0 | 1875 | 1.5356 | 0.4363 | 0.7118 | 0.7230 | 0.4098 |
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+ | 0.1526 | 16.0 | 2000 | 1.5731 | 0.4370 | 0.7073 | 0.7169 | 0.4181 |
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+ | 0.1288 | 17.0 | 2125 | 1.6258 | 0.4406 | 0.7123 | 0.7245 | 0.4151 |
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+ | 0.1321 | 18.0 | 2250 | 1.6590 | 0.4364 | 0.7081 | 0.7184 | 0.4148 |
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+ | 0.114 | 19.0 | 2375 | 1.6598 | 0.4324 | 0.7074 | 0.7192 | 0.4081 |
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+ | 0.1063 | 20.0 | 2500 | 1.6603 | 0.4329 | 0.7053 | 0.7154 | 0.4114 |
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  ### Framework versions