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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: 2.3471
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- - Macro f1: 0.4351
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- - Weighted f1: 0.7056
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- - Accuracy: 0.7207
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- - Balanced accuracy: 0.4063
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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: 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
@@ -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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- | 1.249 | 1.0 | 250 | 1.1782 | 0.2143 | 0.5844 | 0.6385 | 0.2417 |
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- | 1.0481 | 2.0 | 500 | 1.0009 | 0.3079 | 0.6757 | 0.6865 | 0.3192 |
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- | 0.903 | 3.0 | 750 | 1.0094 | 0.3105 | 0.6840 | 0.6986 | 0.3179 |
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- | 0.7604 | 4.0 | 1000 | 1.0636 | 0.3817 | 0.6834 | 0.6994 | 0.3751 |
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- | 0.6367 | 5.0 | 1250 | 1.0813 | 0.3999 | 0.6963 | 0.7108 | 0.3945 |
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- | 0.5293 | 6.0 | 1500 | 1.1597 | 0.3909 | 0.6920 | 0.6986 | 0.3895 |
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- | 0.4097 | 7.0 | 1750 | 1.3520 | 0.3517 | 0.6739 | 0.6865 | 0.3757 |
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- | 0.3442 | 8.0 | 2000 | 1.5343 | 0.4012 | 0.6684 | 0.6743 | 0.4028 |
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- | 0.2663 | 9.0 | 2250 | 1.5623 | 0.4241 | 0.7007 | 0.7154 | 0.4052 |
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- | 0.2383 | 10.0 | 2500 | 1.6971 | 0.4327 | 0.7080 | 0.7169 | 0.4179 |
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- | 0.2053 | 11.0 | 2750 | 1.7675 | 0.4331 | 0.7073 | 0.7177 | 0.4199 |
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- | 0.1698 | 12.0 | 3000 | 1.8678 | 0.4381 | 0.7103 | 0.7298 | 0.4097 |
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- | 0.1467 | 13.0 | 3250 | 2.0007 | 0.4343 | 0.7113 | 0.7268 | 0.4082 |
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- | 0.1098 | 14.0 | 3500 | 2.0797 | 0.4267 | 0.7004 | 0.7131 | 0.3986 |
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- | 0.1049 | 15.0 | 3750 | 2.2048 | 0.4190 | 0.7037 | 0.7192 | 0.3939 |
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- | 0.0912 | 16.0 | 4000 | 2.2582 | 0.4263 | 0.6903 | 0.7024 | 0.4003 |
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- | 0.0678 | 17.0 | 4250 | 2.2735 | 0.4276 | 0.7052 | 0.7222 | 0.4019 |
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- | 0.0623 | 18.0 | 4500 | 2.3478 | 0.4317 | 0.7048 | 0.7207 | 0.4030 |
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- | 0.0546 | 19.0 | 4750 | 2.3598 | 0.4298 | 0.7043 | 0.7207 | 0.4003 |
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- | 0.0415 | 20.0 | 5000 | 2.3471 | 0.4351 | 0.7056 | 0.7207 | 0.4063 |
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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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  - 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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+ | 1.3633 | 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