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satyanshu404/long-t5-local-base-finetuned-justification-v05
long-t5-local-base-finetuned-justification-v05 is a machine learning model from satyanshu404. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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From the Hugging Face model README
This model is a fine-tuned version of google/long-t5-local-base on the None dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.7549 | 1.0 | 676 | 2.2649 |
| 1.8473 | 2.0 | 1352 | 1.9235 |
| 1.4521 | 3.0 | 2028 | 1.7887 |
| 1.4025 | 4.0 | 2704 | 1.6854 |
| 1.3373 | 5.0 | 3380 | 1.6227 |
| 1.2577 | 6.0 | 4056 | 1.5758 |
| 1.2089 | 7.0 | 4732 | 1.5332 |
| 1.1828 | 8.0 | 5408 | 1.4992 |
| 1.152 | 9.0 | 6084 | 1.4637 |
| 1.1035 | 10.0 | 6760 | 1.4390 |
| 1.1482 | 11.0 | 7436 | 1.4142 |
| 1.0386 | 12.0 | 8112 | 1.3913 |
| 1.0287 | 13.0 | 8788 | 1.3702 |
| 1.0355 | 14.0 | 9464 | 1.3613 |
| 0.9495 | 15.0 | 10140 | 1.3458 |
| 0.9726 | 16.0 | 10816 | 1.3346 |
| 0.9417 | 17.0 | 11492 | 1.3223 |
| 0.8965 | 18.0 | 12168 | 1.3155 |
| 0.9588 | 19.0 | 12844 | 1.3123 |
| 0.8909 | 20.0 | 13520 | 1.3079 |
| 0.8935 | 21.0 | 14196 | 1.2922 |
| 0.8629 | 22.0 | 14872 | 1.2886 |
| 0.8533 | 23.0 | 15548 | 1.2938 |
| 0.8676 | 24.0 | 16224 | 1.2867 |
| 0.8245 | 25.0 | 16900 | 1.2804 |
| 0.8449 | 26.0 | 17576 | 1.2781 |
| 0.817 | 27.0 | 18252 | 1.2816 |
| 0.7731 | 28.0 | 18928 | 1.2768 |
| 0.8 | 29.0 | 19604 | 1.2732 |
| 0.785 | 30.0 | 20280 | 1.2785 |
| 0.7751 | 31.0 | 20956 | 1.2790 |
| 0.7829 | 32.0 | 21632 | 1.2817 |
| 0.7334 | 33.0 | 22308 | 1.2749 |
| 0.7409 | 34.0 | 22984 | 1.2653 |
| 0.761 | 35.0 | 23660 | 1.2711 |
| 0.7175 | 36.0 | 24336 | 1.2744 |
| 0.7244 | 37.0 | 25012 | 1.2728 |
| 0.7229 | 38.0 | 25688 | 1.2758 |
| 0.6733 | 39.0 | 26364 | 1.2783 |
| 0.6748 | 40.0 | 27040 | 1.2858 |
| 0.6798 | 41.0 | 27716 | 1.2810 |
| 0.6682 | 42.0 | 28392 | 1.2809 |
| 0.6945 | 43.0 | 29068 | 1.2900 |
| 0.6563 | 44.0 | 29744 | 1.2896 |
| 0.6362 | 45.0 | 30420 | 1.2918 |
| 0.6391 | 46.0 | 31096 | 1.2923 |
| 0.6379 | 47.0 | 31772 | 1.2853 |
| 0.6384 | 48.0 | 32448 | 1.2888 |
| 0.6341 | 49.0 | 33124 | 1.2932 |
| 0.6344 | 50.0 | 33800 | 1.2958 |
| 0.6012 | 51.0 | 34476 | 1.2995 |
| 0.6029 | 52.0 | 35152 | 1.3007 |
| 0.6376 | 53.0 | 35828 | 1.3127 |
| 0.5931 | 54.0 | 36504 | 1.3073 |
| 0.5984 | 55.0 | 37180 | 1.3092 |
| 0.6052 | 56.0 | 37856 | 1.3134 |
| 0.5843 | 57.0 | 38532 | 1.3158 |
| 0.5877 | 58.0 | 39208 | 1.3204 |
| 0.5708 | 59.0 | 39884 | 1.3335 |
| 0.566 | 60.0 | 40560 | 1.3294 |
| 0.542 | 61.0 | 41236 | 1.3305 |
| 0.5594 | 62.0 | 41912 | 1.3248 |
| 0.5461 | 63.0 | 42588 | 1.3323 |
| 0.5498 | 64.0 | 43264 | 1.3336 |
| 0.5302 | 65.0 | 43940 | 1.3314 |
| 0.5418 | 66.0 | 44616 | 1.3297 |
| 0.5319 | 67.0 | 45292 | 1.3298 |
| 0.5241 | 68.0 | 45968 | 1.3440 |
| 0.5275 | 69.0 | 46644 | 1.3418 |
| 0.5427 | 70.0 | 47320 | 1.3493 |
| 0.5078 | 71.0 | 47996 | 1.3477 |
| 0.5075 | 72.0 | 48672 | 1.3510 |
| 0.5116 | 73.0 | 49348 | 1.3460 |
| 0.521 | 74.0 | 50024 | 1.3519 |
| 0.5191 | 75.0 | 50700 | 1.3550 |
| 0.5053 | 76.0 | 51376 | 1.3573 |
| 0.5063 | 77.0 | 52052 | 1.3576 |
| 0.5057 | 78.0 | 52728 | 1.3599 |
| 0.4916 | 79.0 | 53404 | 1.3657 |
| 0.4946 | 80.0 | 54080 | 1.3604 |
| 0.502 | 81.0 | 54756 | 1.3656 |
| 0.4915 | 82.0 | 55432 | 1.3640 |
| 0.4926 | 83.0 | 56108 | 1.3659 |
| 0.4935 | 84.0 | 56784 | 1.3665 |
| 0.4566 | 85.0 | 57460 | 1.3685 |
| 0.4876 | 86.0 | 58136 | 1.3747 |
| 0.4822 | 87.0 | 58812 | 1.3711 |
| 0.4656 | 88.0 | 59488 | 1.3752 |
| 0.4705 | 89.0 | 60164 | 1.3727 |
| 0.477 | 90.0 | 60840 | 1.3736 |
| 0.4752 | 91.0 | 61516 | 1.3772 |
| 0.4712 | 92.0 | 62192 | 1.3778 |
| 0.4753 | 93.0 | 62868 | 1.3774 |
| 0.4827 | 94.0 | 63544 | 1.3821 |
| 0.4605 | 95.0 | 64220 | 1.3784 |
| 0.4757 | 96.0 | 64896 | 1.3835 |
| 0.4482 | 97.0 | 65572 | 1.3819 |
| 0.467 | 98.0 | 66248 | 1.3800 |
| 0.4684 | 99.0 | 66924 | 1.3816 |
| 0.4613 | 100.0 | 67600 | 1.3822 |