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distilbert-base-uncased-finetuned-pfe-projectt

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  2. model.safetensors +1 -1
README.md CHANGED
@@ -15,16 +15,16 @@ 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: 1.5684
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- - Start Accuracy: 0.6667
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- - End Accuracy: 0.6099
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- - Overall Accuracy: 0.6383
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- - Start Precision: 0.2599
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- - End Precision: 0.1569
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- - Start Recall: 0.2741
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- - End Recall: 0.1927
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- - Start F1 Score: 0.2520
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- - End F1 Score: 0.1614
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  ## Model description
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@@ -49,32 +49,42 @@ The following hyperparameters were used during training:
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  - seed: 0
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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 | Start Accuracy | End Accuracy | Overall Accuracy | Start Precision | End Precision | Start Recall | End Recall | Start F1 Score | End F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------:|:------------:|:----------:|:--------------:|:------------:|
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- | 4.5919 | 1.0 | 17 | 3.2463 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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- | 2.4317 | 2.0 | 34 | 2.5979 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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- | 2.1431 | 3.0 | 51 | 2.3111 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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- | 1.946 | 4.0 | 68 | 2.1552 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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- | 1.7608 | 5.0 | 85 | 2.0098 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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- | 1.5737 | 6.0 | 102 | 1.8658 | 0.5887 | 0.4752 | 0.5319 | 0.0893 | 0.0140 | 0.1187 | 0.0294 | 0.0992 | 0.0189 |
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- | 1.5679 | 7.0 | 119 | 1.8468 | 0.5674 | 0.4752 | 0.5213 | 0.1126 | 0.0141 | 0.1366 | 0.0294 | 0.1164 | 0.0190 |
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- | 1.3914 | 8.0 | 136 | 1.8338 | 0.5674 | 0.6099 | 0.5887 | 0.1225 | 0.0944 | 0.1814 | 0.1266 | 0.1404 | 0.1057 |
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- | 1.264 | 9.0 | 153 | 1.6698 | 0.6383 | 0.6170 | 0.6277 | 0.1737 | 0.0930 | 0.2169 | 0.1270 | 0.1867 | 0.1033 |
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- | 1.2254 | 10.0 | 170 | 1.6453 | 0.6454 | 0.6099 | 0.6277 | 0.1782 | 0.0851 | 0.2174 | 0.1197 | 0.1891 | 0.0957 |
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- | 1.0858 | 11.0 | 187 | 1.6160 | 0.6525 | 0.6028 | 0.6277 | 0.1889 | 0.1062 | 0.2352 | 0.1404 | 0.2036 | 0.1169 |
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- | 1.0867 | 12.0 | 204 | 1.6558 | 0.6241 | 0.5957 | 0.6099 | 0.1804 | 0.0812 | 0.2118 | 0.1188 | 0.1859 | 0.0936 |
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- | 0.9459 | 13.0 | 221 | 1.6093 | 0.6596 | 0.6170 | 0.6383 | 0.1990 | 0.1377 | 0.2583 | 0.1914 | 0.2181 | 0.1544 |
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- | 0.9257 | 14.0 | 238 | 1.5496 | 0.6667 | 0.6028 | 0.6348 | 0.2737 | 0.1021 | 0.2791 | 0.1502 | 0.2577 | 0.1138 |
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- | 0.9268 | 15.0 | 255 | 1.5402 | 0.6596 | 0.6170 | 0.6383 | 0.2230 | 0.1578 | 0.2583 | 0.1692 | 0.2253 | 0.1486 |
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- | 0.8647 | 16.0 | 272 | 1.5718 | 0.6525 | 0.6170 | 0.6348 | 0.1975 | 0.1541 | 0.2482 | 0.1864 | 0.2129 | 0.1598 |
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- | 0.837 | 17.0 | 289 | 1.5851 | 0.6667 | 0.6099 | 0.6383 | 0.2454 | 0.1976 | 0.2493 | 0.2347 | 0.2339 | 0.2042 |
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- | 0.8297 | 18.0 | 306 | 1.5847 | 0.6667 | 0.6099 | 0.6383 | 0.2586 | 0.1201 | 0.2644 | 0.1534 | 0.2472 | 0.1255 |
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- | 0.7824 | 19.0 | 323 | 1.5682 | 0.6667 | 0.6170 | 0.6418 | 0.2586 | 0.1586 | 0.2644 | 0.1931 | 0.2472 | 0.1626 |
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- | 0.7917 | 20.0 | 340 | 1.5684 | 0.6667 | 0.6099 | 0.6383 | 0.2599 | 0.1569 | 0.2741 | 0.1927 | 0.2520 | 0.1614 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 1.5126
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+ - Start Accuracy: 0.7021
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+ - End Accuracy: 0.6241
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+ - Overall Accuracy: 0.6631
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+ - Start Precision: 0.2862
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+ - End Precision: 0.1446
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+ - Start Recall: 0.3165
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+ - End Recall: 0.1961
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+ - Start F1 Score: 0.2802
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+ - End F1 Score: 0.1506
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  ## Model description
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  - seed: 0
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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: 30
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Start Accuracy | End Accuracy | Overall Accuracy | Start Precision | End Precision | Start Recall | End Recall | Start F1 Score | End F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------:|:------------:|:----------:|:--------------:|:------------:|
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+ | 4.8235 | 1.0 | 17 | 3.6096 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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+ | 2.5596 | 2.0 | 34 | 2.5775 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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+ | 2.0949 | 3.0 | 51 | 2.2935 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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+ | 1.9248 | 4.0 | 68 | 2.1906 | 0.4752 | 0.4752 | 0.4752 | 0.0153 | 0.0140 | 0.0323 | 0.0294 | 0.0208 | 0.0189 |
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+ | 1.7309 | 5.0 | 85 | 2.0463 | 0.5674 | 0.4752 | 0.5213 | 0.0978 | 0.0140 | 0.1340 | 0.0294 | 0.1110 | 0.0189 |
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+ | 1.5193 | 6.0 | 102 | 1.9047 | 0.5745 | 0.4823 | 0.5284 | 0.1116 | 0.0390 | 0.1544 | 0.0657 | 0.1240 | 0.0438 |
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+ | 1.4401 | 7.0 | 119 | 1.8648 | 0.5532 | 0.5035 | 0.5284 | 0.0960 | 0.0686 | 0.1271 | 0.0858 | 0.1046 | 0.0676 |
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+ | 1.3916 | 8.0 | 136 | 1.7904 | 0.6170 | 0.5532 | 0.5851 | 0.1673 | 0.0709 | 0.2090 | 0.1041 | 0.1800 | 0.0795 |
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+ | 1.2498 | 9.0 | 153 | 1.8084 | 0.6170 | 0.5816 | 0.5993 | 0.1309 | 0.0862 | 0.1791 | 0.1145 | 0.1449 | 0.0930 |
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+ | 1.1733 | 10.0 | 170 | 1.7518 | 0.6241 | 0.5957 | 0.6099 | 0.1586 | 0.0925 | 0.2032 | 0.1221 | 0.1712 | 0.0995 |
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+ | 1.0563 | 11.0 | 187 | 1.6420 | 0.6241 | 0.5816 | 0.6028 | 0.1673 | 0.0918 | 0.2206 | 0.1337 | 0.1814 | 0.1047 |
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+ | 1.0074 | 12.0 | 204 | 1.8142 | 0.6454 | 0.6099 | 0.6277 | 0.1453 | 0.0934 | 0.1737 | 0.1080 | 0.1497 | 0.0982 |
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+ | 0.9473 | 13.0 | 221 | 1.6035 | 0.6738 | 0.6241 | 0.6489 | 0.2440 | 0.1010 | 0.2606 | 0.1444 | 0.2340 | 0.1138 |
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+ | 0.9307 | 14.0 | 238 | 1.4999 | 0.6809 | 0.6241 | 0.6525 | 0.2226 | 0.1151 | 0.2484 | 0.1418 | 0.2225 | 0.1141 |
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+ | 0.8668 | 15.0 | 255 | 1.5837 | 0.6950 | 0.6312 | 0.6631 | 0.2456 | 0.1033 | 0.2678 | 0.1520 | 0.2395 | 0.1162 |
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+ | 0.8226 | 16.0 | 272 | 1.5517 | 0.6879 | 0.6312 | 0.6596 | 0.2741 | 0.1385 | 0.2911 | 0.1955 | 0.2523 | 0.1451 |
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+ | 0.7358 | 17.0 | 289 | 1.5387 | 0.7092 | 0.6241 | 0.6667 | 0.3022 | 0.1374 | 0.3360 | 0.1898 | 0.2854 | 0.1424 |
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+ | 0.7529 | 18.0 | 306 | 1.4644 | 0.6950 | 0.6383 | 0.6667 | 0.2554 | 0.1358 | 0.3011 | 0.1947 | 0.2531 | 0.1450 |
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+ | 0.6962 | 19.0 | 323 | 1.5374 | 0.6809 | 0.6383 | 0.6596 | 0.2570 | 0.1419 | 0.2890 | 0.1861 | 0.2516 | 0.1502 |
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+ | 0.6807 | 20.0 | 340 | 1.4873 | 0.6809 | 0.6383 | 0.6596 | 0.2577 | 0.1469 | 0.3058 | 0.1840 | 0.2623 | 0.1572 |
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+ | 0.6988 | 21.0 | 357 | 1.5178 | 0.6667 | 0.6099 | 0.6383 | 0.2843 | 0.1558 | 0.3050 | 0.1982 | 0.2802 | 0.1597 |
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+ | 0.6632 | 22.0 | 374 | 1.5319 | 0.7092 | 0.6312 | 0.6702 | 0.2860 | 0.1489 | 0.3053 | 0.2015 | 0.2788 | 0.1628 |
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+ | 0.6081 | 23.0 | 391 | 1.5817 | 0.7021 | 0.6454 | 0.6738 | 0.2537 | 0.1767 | 0.2853 | 0.1944 | 0.2489 | 0.1677 |
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+ | 0.5926 | 24.0 | 408 | 1.5514 | 0.6950 | 0.6241 | 0.6596 | 0.3069 | 0.1451 | 0.3160 | 0.1913 | 0.2937 | 0.1465 |
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+ | 0.6449 | 25.0 | 425 | 1.5521 | 0.6950 | 0.6383 | 0.6667 | 0.2826 | 0.1687 | 0.3336 | 0.2133 | 0.2806 | 0.1654 |
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+ | 0.5542 | 26.0 | 442 | 1.4860 | 0.7305 | 0.6383 | 0.6844 | 0.3305 | 0.1598 | 0.3734 | 0.1812 | 0.3257 | 0.1580 |
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+ | 0.5668 | 27.0 | 459 | 1.5091 | 0.7092 | 0.6454 | 0.6773 | 0.3201 | 0.1688 | 0.3304 | 0.2075 | 0.2982 | 0.1670 |
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+ | 0.5603 | 28.0 | 476 | 1.5088 | 0.7021 | 0.6454 | 0.6738 | 0.2819 | 0.1535 | 0.3165 | 0.2022 | 0.2772 | 0.1569 |
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+ | 0.5353 | 29.0 | 493 | 1.5101 | 0.7021 | 0.6383 | 0.6702 | 0.2862 | 0.1447 | 0.3165 | 0.1968 | 0.2802 | 0.1494 |
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+ | 0.5082 | 30.0 | 510 | 1.5126 | 0.7021 | 0.6241 | 0.6631 | 0.2862 | 0.1446 | 0.3165 | 0.1961 | 0.2802 | 0.1506 |
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  ### Framework versions
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