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distilbert-medical-question_answer

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update model card README.md

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@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.5173
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  ## Model description
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@@ -40,37 +40,117 @@ The following hyperparameters were used during training:
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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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- - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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- | No log | 1.0 | 21 | 3.9834 |
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- | No log | 2.0 | 42 | 3.3933 |
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- | No log | 3.0 | 63 | 3.3457 |
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- | No log | 4.0 | 84 | 3.2997 |
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- | No log | 5.0 | 105 | 3.2746 |
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- | No log | 6.0 | 126 | 3.2334 |
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- | No log | 7.0 | 147 | 3.2655 |
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- | No log | 8.0 | 168 | 3.3428 |
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- | No log | 9.0 | 189 | 3.3693 |
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- | No log | 10.0 | 210 | 3.3065 |
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- | No log | 11.0 | 231 | 3.3817 |
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- | No log | 12.0 | 252 | 3.4270 |
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- | No log | 13.0 | 273 | 3.4121 |
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- | No log | 14.0 | 294 | 3.4588 |
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- | No log | 15.0 | 315 | 3.4182 |
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- | No log | 16.0 | 336 | 3.4626 |
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- | No log | 17.0 | 357 | 3.4975 |
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- | No log | 18.0 | 378 | 3.4880 |
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- | No log | 19.0 | 399 | 3.5034 |
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- | No log | 20.0 | 420 | 3.5173 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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- - Datasets 2.14.3
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  - Tokenizers 0.13.3
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 5.6100
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  ## Model description
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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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+ - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:----:|:---------------:|
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+ | No log | 1.0 | 21 | 4.2930 |
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+ | No log | 2.0 | 42 | 3.3634 |
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+ | No log | 3.0 | 63 | 3.2834 |
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+ | No log | 4.0 | 84 | 3.2596 |
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+ | No log | 5.0 | 105 | 3.2594 |
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+ | No log | 6.0 | 126 | 3.2574 |
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+ | No log | 7.0 | 147 | 3.2845 |
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+ | No log | 8.0 | 168 | 3.2187 |
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+ | No log | 9.0 | 189 | 3.3233 |
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+ | No log | 10.0 | 210 | 3.3231 |
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+ | No log | 11.0 | 231 | 3.3505 |
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+ | No log | 12.0 | 252 | 3.5721 |
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+ | No log | 13.0 | 273 | 3.5463 |
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+ | No log | 14.0 | 294 | 3.5413 |
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+ | No log | 15.0 | 315 | 3.6203 |
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+ | No log | 16.0 | 336 | 3.6025 |
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+ | No log | 17.0 | 357 | 3.6301 |
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+ | No log | 18.0 | 378 | 3.8150 |
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+ | No log | 19.0 | 399 | 4.0084 |
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+ | No log | 20.0 | 420 | 3.9528 |
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+ | No log | 21.0 | 441 | 4.0350 |
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+ | No log | 22.0 | 462 | 3.9436 |
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+ | No log | 23.0 | 483 | 4.0115 |
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+ | 1.7508 | 24.0 | 504 | 4.0571 |
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+ | 1.7508 | 25.0 | 525 | 4.0290 |
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+ | 1.7508 | 26.0 | 546 | 4.0609 |
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+ | 1.7508 | 27.0 | 567 | 4.2875 |
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+ | 1.7508 | 28.0 | 588 | 4.0578 |
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+ | 1.7508 | 29.0 | 609 | 4.1743 |
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+ | 1.7508 | 30.0 | 630 | 4.1155 |
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+ | 1.7508 | 31.0 | 651 | 4.2136 |
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+ | 1.7508 | 32.0 | 672 | 4.3880 |
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+ | 1.7508 | 33.0 | 693 | 4.4454 |
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+ | 1.7508 | 34.0 | 714 | 4.3621 |
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+ | 1.7508 | 35.0 | 735 | 4.1829 |
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+ | 1.7508 | 36.0 | 756 | 4.2985 |
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+ | 1.7508 | 37.0 | 777 | 4.5783 |
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+ | 1.7508 | 38.0 | 798 | 4.4504 |
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+ | 1.7508 | 39.0 | 819 | 4.6955 |
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+ | 1.7508 | 40.0 | 840 | 4.5165 |
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+ | 1.7508 | 41.0 | 861 | 4.3018 |
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+ | 1.7508 | 42.0 | 882 | 4.5299 |
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+ | 1.7508 | 43.0 | 903 | 4.6147 |
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+ | 1.7508 | 44.0 | 924 | 4.4756 |
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+ | 1.7508 | 45.0 | 945 | 4.6782 |
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+ | 1.7508 | 46.0 | 966 | 4.6168 |
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+ | 1.7508 | 47.0 | 987 | 4.7553 |
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+ | 0.2318 | 48.0 | 1008 | 4.8580 |
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+ | 0.2318 | 49.0 | 1029 | 4.8970 |
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+ | 0.2318 | 50.0 | 1050 | 4.8502 |
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+ | 0.2318 | 51.0 | 1071 | 4.7219 |
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+ | 0.2318 | 52.0 | 1092 | 4.9355 |
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+ | 0.2318 | 53.0 | 1113 | 5.0003 |
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+ | 0.2318 | 54.0 | 1134 | 5.1603 |
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+ | 0.2318 | 55.0 | 1155 | 5.0398 |
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+ | 0.2318 | 56.0 | 1176 | 5.1349 |
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+ | 0.2318 | 57.0 | 1197 | 5.1403 |
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+ | 0.2318 | 58.0 | 1218 | 5.0170 |
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+ | 0.2318 | 59.0 | 1239 | 5.0553 |
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+ | 0.2318 | 60.0 | 1260 | 5.2331 |
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+ | 0.2318 | 61.0 | 1281 | 5.0543 |
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+ | 0.2318 | 62.0 | 1302 | 5.1769 |
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+ | 0.2318 | 63.0 | 1323 | 5.4024 |
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+ | 0.2318 | 64.0 | 1344 | 5.2960 |
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+ | 0.2318 | 65.0 | 1365 | 5.2071 |
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+ | 0.2318 | 66.0 | 1386 | 5.1635 |
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+ | 0.2318 | 67.0 | 1407 | 5.2613 |
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+ | 0.2318 | 68.0 | 1428 | 5.3370 |
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+ | 0.2318 | 69.0 | 1449 | 5.3725 |
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+ | 0.2318 | 70.0 | 1470 | 5.2739 |
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+ | 0.2318 | 71.0 | 1491 | 5.2887 |
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+ | 0.0363 | 72.0 | 1512 | 5.4713 |
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+ | 0.0363 | 73.0 | 1533 | 5.4102 |
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+ | 0.0363 | 74.0 | 1554 | 5.3190 |
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+ | 0.0363 | 75.0 | 1575 | 5.3406 |
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+ | 0.0363 | 76.0 | 1596 | 5.4775 |
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+ | 0.0363 | 77.0 | 1617 | 5.4636 |
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+ | 0.0363 | 78.0 | 1638 | 5.4894 |
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+ | 0.0363 | 79.0 | 1659 | 5.5111 |
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+ | 0.0363 | 80.0 | 1680 | 5.5769 |
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+ | 0.0363 | 81.0 | 1701 | 5.5069 |
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+ | 0.0363 | 82.0 | 1722 | 5.5296 |
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+ | 0.0363 | 83.0 | 1743 | 5.5471 |
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+ | 0.0363 | 84.0 | 1764 | 5.5630 |
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+ | 0.0363 | 85.0 | 1785 | 5.5563 |
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+ | 0.0363 | 86.0 | 1806 | 5.5700 |
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+ | 0.0363 | 87.0 | 1827 | 5.6082 |
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+ | 0.0363 | 88.0 | 1848 | 5.5808 |
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+ | 0.0363 | 89.0 | 1869 | 5.5351 |
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+ | 0.0363 | 90.0 | 1890 | 5.4856 |
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+ | 0.0363 | 91.0 | 1911 | 5.5007 |
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+ | 0.0363 | 92.0 | 1932 | 5.5076 |
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+ | 0.0363 | 93.0 | 1953 | 5.5377 |
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+ | 0.0363 | 94.0 | 1974 | 5.5612 |
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+ | 0.0363 | 95.0 | 1995 | 5.5754 |
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+ | 0.0067 | 96.0 | 2016 | 5.5861 |
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+ | 0.0067 | 97.0 | 2037 | 5.5973 |
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+ | 0.0067 | 98.0 | 2058 | 5.6035 |
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+ | 0.0067 | 99.0 | 2079 | 5.6073 |
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+ | 0.0067 | 100.0 | 2100 | 5.6100 |
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  ### Framework versions
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  - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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  - Tokenizers 0.13.3