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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: 1.6961
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- - Macro f1: 0.3667
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- - Weighted f1: 0.6964
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- - Accuracy: 0.7085
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- - Balanced accuracy: 0.3549
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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: 3e-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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- | 1.4505 | 1.0 | 125 | 1.2490 | 0.1931 | 0.5920 | 0.6492 | 0.2258 |
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- | 1.1504 | 2.0 | 250 | 1.1182 | 0.2455 | 0.6526 | 0.6781 | 0.2589 |
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- | 1.0009 | 3.0 | 375 | 1.1105 | 0.2583 | 0.6540 | 0.6644 | 0.2804 |
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- | 0.8819 | 4.0 | 500 | 1.0584 | 0.2693 | 0.6913 | 0.7131 | 0.2774 |
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- | 0.7763 | 5.0 | 625 | 1.1073 | 0.2936 | 0.6833 | 0.7024 | 0.2931 |
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- | 0.6855 | 6.0 | 750 | 1.1266 | 0.3152 | 0.6596 | 0.6606 | 0.3217 |
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- | 0.5883 | 7.0 | 875 | 1.1544 | 0.3744 | 0.6978 | 0.7093 | 0.3638 |
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- | 0.5245 | 8.0 | 1000 | 1.1876 | 0.3614 | 0.6880 | 0.6971 | 0.3484 |
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- | 0.4645 | 9.0 | 1125 | 1.2525 | 0.3727 | 0.6876 | 0.6948 | 0.3646 |
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- | 0.4057 | 10.0 | 1250 | 1.3357 | 0.3691 | 0.6805 | 0.6895 | 0.3560 |
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- | 0.3774 | 11.0 | 1375 | 1.3848 | 0.3479 | 0.6647 | 0.6659 | 0.3454 |
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- | 0.3353 | 12.0 | 1500 | 1.4084 | 0.3654 | 0.6923 | 0.6986 | 0.3581 |
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- | 0.2991 | 13.0 | 1625 | 1.4604 | 0.3497 | 0.6888 | 0.7032 | 0.3310 |
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- | 0.273 | 14.0 | 1750 | 1.5256 | 0.3727 | 0.6874 | 0.6963 | 0.3653 |
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- | 0.2612 | 15.0 | 1875 | 1.5884 | 0.3679 | 0.7024 | 0.7192 | 0.3508 |
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- | 0.2464 | 16.0 | 2000 | 1.5953 | 0.3634 | 0.6929 | 0.7032 | 0.3536 |
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- | 0.2364 | 17.0 | 2125 | 1.6697 | 0.3682 | 0.6859 | 0.6948 | 0.3610 |
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- | 0.2239 | 18.0 | 2250 | 1.6794 | 0.3632 | 0.6911 | 0.7032 | 0.3511 |
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- | 0.1936 | 19.0 | 2375 | 1.6892 | 0.3666 | 0.6963 | 0.7085 | 0.3545 |
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- | 0.2117 | 20.0 | 2500 | 1.6961 | 0.3667 | 0.6964 | 0.7085 | 0.3549 |
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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: 2.0233
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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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  ### 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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  | 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