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CommitPredictorT5

Text2Text Generation
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PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
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update model card README.md

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@@ -17,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Salesforce/codet5-base-multi-sum](https://huggingface.co/Salesforce/codet5-base-multi-sum) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.7376
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- - Rouge1: 0.3735
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- - Rouge2: 0.1504
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- - Rougel: 0.3701
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- - Rougelsum: 0.3697
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- - Gen Len: 19.1365
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- - Bleu: 0.1453
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  ## Model description
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@@ -48,32 +48,37 @@ 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: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
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- | 3.2138 | 1.0 | 687 | 2.6919 | 0.3246 | 0.0964 | 0.3216 | 0.3218 | 9.125 | 0.0937 |
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- | 2.7517 | 2.0 | 1374 | 2.5546 | 0.3394 | 0.1042 | 0.3364 | 0.3362 | 8.9803 | 0.1066 |
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- | 2.4305 | 3.0 | 2061 | 2.4836 | 0.3523 | 0.1136 | 0.3496 | 0.3496 | 9.2049 | 0.1123 |
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- | 2.2956 | 4.0 | 2748 | 2.4483 | 0.3658 | 0.126 | 0.3633 | 0.3633 | 9.525 | 0.1146 |
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- | 2.1888 | 5.0 | 3435 | 2.4312 | 0.3665 | 0.1332 | 0.3636 | 0.3634 | 10.0631 | 0.1253 |
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- | 2.0056 | 6.0 | 4122 | 2.4251 | 0.3674 | 0.1352 | 0.3646 | 0.3644 | 9.8365 | 0.1240 |
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- | 1.9128 | 7.0 | 4809 | 2.4289 | 0.3725 | 0.1431 | 0.3694 | 0.3694 | 9.8713 | 0.1295 |
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- | 1.8487 | 8.0 | 5496 | 2.4625 | 0.3683 | 0.1377 | 0.3657 | 0.3659 | 10.0947 | 0.1291 |
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- | 1.6726 | 9.0 | 6183 | 2.4643 | 0.3725 | 0.1449 | 0.3702 | 0.3697 | 13.1967 | 0.1325 |
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- | 1.6292 | 10.0 | 6870 | 2.4716 | 0.3688 | 0.1438 | 0.3664 | 0.3661 | 13.7656 | 0.1307 |
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- | 1.5025 | 11.0 | 7557 | 2.4974 | 0.3762 | 0.1494 | 0.3732 | 0.3732 | 14.0988 | 0.1391 |
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- | 1.4224 | 12.0 | 8244 | 2.5273 | 0.3723 | 0.1489 | 0.3692 | 0.3688 | 12.084 | 0.1388 |
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- | 1.3912 | 13.0 | 8931 | 2.5460 | 0.375 | 0.1499 | 0.3722 | 0.3723 | 13.9529 | 0.1399 |
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- | 1.2766 | 14.0 | 9618 | 2.5771 | 0.3698 | 0.1453 | 0.3668 | 0.3667 | 12.5102 | 0.1386 |
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- | 1.2188 | 15.0 | 10305 | 2.6005 | 0.3789 | 0.1493 | 0.3763 | 0.376 | 16.0545 | 0.1423 |
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- | 1.1779 | 16.0 | 10992 | 2.6296 | 0.3757 | 0.1497 | 0.3729 | 0.3723 | 14.7201 | 0.1423 |
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- | 1.0739 | 17.0 | 11679 | 2.6512 | 0.3749 | 0.1522 | 0.3717 | 0.3715 | 16.0008 | 0.1468 |
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- | 1.0408 | 18.0 | 12366 | 2.6792 | 0.3758 | 0.1494 | 0.3735 | 0.3733 | 18.4971 | 0.1465 |
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- | 0.9567 | 19.0 | 13053 | 2.7153 | 0.3695 | 0.144 | 0.3669 | 0.3667 | 16.2357 | 0.1435 |
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- | 0.9072 | 20.0 | 13740 | 2.7376 | 0.3735 | 0.1504 | 0.3701 | 0.3697 | 19.1365 | 0.1453 |
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [Salesforce/codet5-base-multi-sum](https://huggingface.co/Salesforce/codet5-base-multi-sum) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.6717
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+ - Rouge1: 0.372
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+ - Rouge2: 0.1507
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+ - Rougel: 0.369
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+ - Rougelsum: 0.3688
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+ - Gen Len: 13.8906
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+ - Bleu: 0.1392
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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: 30
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
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+ | 3.2418 | 1.0 | 687 | 2.6988 | 0.3229 | 0.0914 | 0.3201 | 0.3198 | 9.2172 | 0.0900 |
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+ | 2.7629 | 2.0 | 1374 | 2.5616 | 0.3421 | 0.1069 | 0.3396 | 0.339 | 8.9447 | 0.1064 |
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+ | 2.4427 | 3.0 | 2061 | 2.4913 | 0.354 | 0.1151 | 0.3512 | 0.3506 | 8.9373 | 0.1113 |
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+ | 2.3102 | 4.0 | 2748 | 2.4524 | 0.3645 | 0.1246 | 0.3615 | 0.3612 | 8.9365 | 0.1162 |
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+ | 2.2119 | 5.0 | 3435 | 2.4388 | 0.3619 | 0.1333 | 0.3587 | 0.3586 | 9.018 | 0.1266 |
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+ | 2.0409 | 6.0 | 4122 | 2.4275 | 0.3682 | 0.1355 | 0.3652 | 0.3653 | 9.2045 | 0.1286 |
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+ | 1.9594 | 7.0 | 4809 | 2.4329 | 0.3671 | 0.1395 | 0.364 | 0.3639 | 9.6348 | 0.1296 |
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+ | 1.9035 | 8.0 | 5496 | 2.4506 | 0.3722 | 0.1404 | 0.3686 | 0.3683 | 9.7484 | 0.1266 |
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+ | 1.7457 | 9.0 | 6183 | 2.4438 | 0.3729 | 0.1424 | 0.3695 | 0.3695 | 9.973 | 0.1323 |
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+ | 1.7143 | 10.0 | 6870 | 2.4548 | 0.3705 | 0.1448 | 0.3676 | 0.3674 | 10.7934 | 0.1316 |
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+ | 1.6037 | 11.0 | 7557 | 2.4729 | 0.3761 | 0.1467 | 0.3731 | 0.3729 | 10.0484 | 0.1374 |
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+ | 1.537 | 12.0 | 8244 | 2.4932 | 0.3777 | 0.1498 | 0.3742 | 0.3742 | 10.3074 | 0.1390 |
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+ | 1.5159 | 13.0 | 8931 | 2.5125 | 0.3757 | 0.1507 | 0.3728 | 0.3727 | 10.1291 | 0.1387 |
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+ | 1.4279 | 14.0 | 9618 | 2.5307 | 0.3773 | 0.1503 | 0.3741 | 0.3739 | 9.9828 | 0.1406 |
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+ | 1.3794 | 15.0 | 10305 | 2.5565 | 0.3745 | 0.1469 | 0.3719 | 0.3717 | 10.1434 | 0.1389 |
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+ | 1.3466 | 16.0 | 10992 | 2.5625 | 0.3743 | 0.1456 | 0.3712 | 0.3711 | 11.5988 | 0.1375 |
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+ | 1.2756 | 17.0 | 11679 | 2.5823 | 0.3719 | 0.1504 | 0.369 | 0.3689 | 11.332 | 0.1423 |
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+ | 1.2543 | 18.0 | 12366 | 2.5854 | 0.3762 | 0.1503 | 0.3734 | 0.3733 | 12.1697 | 0.1451 |
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+ | 1.202 | 19.0 | 13053 | 2.6075 | 0.3737 | 0.1484 | 0.3704 | 0.37 | 12.8557 | 0.1383 |
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+ | 1.1653 | 20.0 | 13740 | 2.6147 | 0.3729 | 0.1497 | 0.3697 | 0.3696 | 13.5848 | 0.1413 |
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+ | 1.1593 | 21.0 | 14427 | 2.6340 | 0.3737 | 0.1511 | 0.3705 | 0.3706 | 13.6131 | 0.1448 |
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+ | 1.1013 | 22.0 | 15114 | 2.6416 | 0.3724 | 0.1498 | 0.3693 | 0.3691 | 13.0574 | 0.1421 |
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+ | 1.0954 | 23.0 | 15801 | 2.6523 | 0.3719 | 0.1521 | 0.369 | 0.3689 | 14.9652 | 0.1412 |
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+ | 1.0632 | 24.0 | 16488 | 2.6664 | 0.3688 | 0.1488 | 0.3657 | 0.3655 | 14.441 | 0.1366 |
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+ | 1.0407 | 25.0 | 17175 | 2.6717 | 0.372 | 0.1507 | 0.369 | 0.3688 | 13.8906 | 0.1392 |
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  ### Framework versions