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TerryHenrickson
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t5-small-finetuned-xsum

Text2Text Generation
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PyTorch
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Safetensors
t5
Generated from Trainer
text-generation-inference
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End of training

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  1. README.md +49 -29
  2. model.safetensors +1 -1
  3. runs/Feb19_09-07-25_3e139e734b2e/events.out.tfevents.1708333646.3e139e734b2e.332.0 +2 -2
README.md CHANGED
@@ -17,12 +17,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2397
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- - Rouge1: 39.9145
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- - Rouge2: 33.183
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- - Rougel: 39.9484
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- - Rougelsum: 39.9376
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- - Gen Len: 19.0
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  ## Model description
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@@ -47,33 +47,53 @@ 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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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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- |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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- | No log | 1.0 | 90 | 3.4654 | 9.8007 | 0.2231 | 9.4993 | 9.501 | 18.6889 |
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- | No log | 2.0 | 180 | 2.1906 | 10.4443 | 0.2158 | 9.8828 | 9.8755 | 18.8972 |
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- | No log | 3.0 | 270 | 1.3067 | 11.7213 | 0.5145 | 10.913 | 10.9345 | 18.8528 |
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- | No log | 4.0 | 360 | 0.7369 | 14.9807 | 1.4227 | 13.7179 | 13.7131 | 18.8333 |
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- | No log | 5.0 | 450 | 0.6143 | 19.8511 | 4.6089 | 18.0244 | 17.9558 | 18.7083 |
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- | 2.447 | 6.0 | 540 | 0.5312 | 23.1026 | 8.6515 | 20.7866 | 20.757 | 18.7139 |
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- | 2.447 | 7.0 | 630 | 0.4782 | 21.9961 | 9.3626 | 19.7651 | 19.7488 | 18.5944 |
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- | 2.447 | 8.0 | 720 | 0.4365 | 16.4406 | 6.5397 | 14.9694 | 14.9816 | 18.6639 |
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- | 2.447 | 9.0 | 810 | 0.3603 | 6.9337 | 3.7397 | 6.6337 | 6.6621 | 18.9028 |
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- | 2.447 | 10.0 | 900 | 0.2696 | 24.2884 | 19.0601 | 24.1044 | 24.1488 | 18.9694 |
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- | 2.447 | 11.0 | 990 | 0.2590 | 39.2002 | 32.3107 | 39.202 | 39.1928 | 19.0 |
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- | 0.572 | 12.0 | 1080 | 0.2546 | 39.0083 | 32.1464 | 39.0296 | 38.9988 | 19.0 |
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- | 0.572 | 13.0 | 1170 | 0.2486 | 39.519 | 32.7114 | 39.5614 | 39.5391 | 19.0 |
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- | 0.572 | 14.0 | 1260 | 0.2465 | 39.589 | 32.8014 | 39.6298 | 39.6092 | 19.0 |
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- | 0.572 | 15.0 | 1350 | 0.2444 | 39.5831 | 32.7959 | 39.6266 | 39.6123 | 19.0 |
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- | 0.572 | 16.0 | 1440 | 0.2427 | 39.7174 | 32.9525 | 39.7513 | 39.7311 | 19.0 |
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- | 0.3469 | 17.0 | 1530 | 0.2412 | 39.8478 | 33.0999 | 39.8901 | 39.8708 | 19.0 |
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- | 0.3469 | 18.0 | 1620 | 0.2401 | 39.8528 | 33.1031 | 39.8819 | 39.873 | 19.0 |
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- | 0.3469 | 19.0 | 1710 | 0.2398 | 39.9283 | 33.1964 | 39.9502 | 39.9533 | 19.0 |
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- | 0.3469 | 20.0 | 1800 | 0.2397 | 39.9145 | 33.183 | 39.9484 | 39.9376 | 19.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0734
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+ - Rouge1: 99.9038
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+ - Rouge2: 99.838
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+ - Rougel: 99.9145
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+ - Rougelsum: 99.9038
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+ - Gen Len: 93.9181
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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: 40
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
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+ | No log | 1.0 | 180 | 1.7815 | 9.7268 | 2.7047 | 8.7069 | 8.7035 | 155.8472 |
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+ | No log | 2.0 | 360 | 0.6270 | 28.7135 | 19.99 | 27.1646 | 27.1386 | 265.2903 |
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+ | 2.122 | 3.0 | 540 | 0.3572 | 21.4211 | 17.5143 | 21.0387 | 20.9118 | 142.7333 |
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+ | 2.122 | 4.0 | 720 | 0.2757 | 92.8223 | 90.5077 | 92.0061 | 92.0015 | 87.0847 |
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+ | 2.122 | 5.0 | 900 | 0.2493 | 95.6972 | 94.5082 | 95.5057 | 95.522 | 91.8556 |
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+ | 0.4002 | 6.0 | 1080 | 0.2348 | 96.8942 | 96.2704 | 96.7552 | 96.7736 | 96.0764 |
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+ | 0.4002 | 7.0 | 1260 | 0.2227 | 97.7669 | 97.4255 | 97.6867 | 97.6913 | 93.9097 |
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+ | 0.4002 | 8.0 | 1440 | 0.2111 | 98.7823 | 98.5538 | 98.7622 | 98.7722 | 94.2875 |
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+ | 0.2717 | 9.0 | 1620 | 0.1979 | 99.7455 | 99.6524 | 99.7428 | 99.7449 | 93.8569 |
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+ | 0.2717 | 10.0 | 1800 | 0.1843 | 99.8967 | 99.8175 | 99.8953 | 99.8939 | 93.875 |
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+ | 0.2717 | 11.0 | 1980 | 0.1716 | 99.9078 | 99.8578 | 99.9114 | 99.9095 | 93.8556 |
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+ | 0.2244 | 12.0 | 2160 | 0.1606 | 99.9371 | 99.8807 | 99.9373 | 99.9373 | 93.9236 |
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+ | 0.2244 | 13.0 | 2340 | 0.1512 | 99.9112 | 99.8535 | 99.9141 | 99.9103 | 93.8542 |
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+ | 0.19 | 14.0 | 2520 | 0.1424 | 99.9573 | 99.919 | 99.9573 | 99.9573 | 93.9236 |
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+ | 0.19 | 15.0 | 2700 | 0.1353 | 99.9679 | 99.9421 | 99.9679 | 99.9679 | 93.925 |
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+ | 0.19 | 16.0 | 2880 | 0.1290 | 99.9234 | 99.8727 | 99.9323 | 99.9234 | 93.8736 |
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+ | 0.1652 | 17.0 | 3060 | 0.1235 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9222 |
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+ | 0.1652 | 18.0 | 3240 | 0.1184 | 99.9038 | 99.8373 | 99.911 | 99.9021 | 93.8722 |
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+ | 0.1652 | 19.0 | 3420 | 0.1137 | 99.9466 | 99.9074 | 99.9573 | 99.9466 | 93.9236 |
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+ | 0.1471 | 20.0 | 3600 | 0.1092 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9222 |
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+ | 0.1471 | 21.0 | 3780 | 0.1053 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9222 |
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+ | 0.1471 | 22.0 | 3960 | 0.1014 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9222 |
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+ | 0.1331 | 23.0 | 4140 | 0.0982 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9222 |
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+ | 0.1331 | 24.0 | 4320 | 0.0949 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9208 |
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+ | 0.1226 | 25.0 | 4500 | 0.0918 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9208 |
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+ | 0.1226 | 26.0 | 4680 | 0.0892 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9208 |
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+ | 0.1226 | 27.0 | 4860 | 0.0867 | 99.9252 | 99.8727 | 99.9359 | 99.9252 | 93.9208 |
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+ | 0.114 | 28.0 | 5040 | 0.0848 | 99.9145 | 99.8495 | 99.9252 | 99.9145 | 93.9194 |
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+ | 0.114 | 29.0 | 5220 | 0.0828 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.114 | 30.0 | 5400 | 0.0811 | 99.9145 | 99.8495 | 99.9252 | 99.9145 | 93.9194 |
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+ | 0.1074 | 31.0 | 5580 | 0.0794 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.1074 | 32.0 | 5760 | 0.0781 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.1074 | 33.0 | 5940 | 0.0769 | 99.9252 | 99.8669 | 99.9252 | 99.9252 | 93.9194 |
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+ | 0.1027 | 34.0 | 6120 | 0.0757 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.1027 | 35.0 | 6300 | 0.0751 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.1027 | 36.0 | 6480 | 0.0745 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.0994 | 37.0 | 6660 | 0.0740 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.0994 | 38.0 | 6840 | 0.0737 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.0975 | 39.0 | 7020 | 0.0735 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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+ | 0.0975 | 40.0 | 7200 | 0.0734 | 99.9038 | 99.838 | 99.9145 | 99.9038 | 93.9181 |
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  ### Framework versions
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